Showing papers in "IEEE Transactions on Communications in 2019"
TL;DR: It will be shown that the data-driven approaches should not replace, but rather complement, traditional design techniques based on mathematical models in future wireless communication networks.
Abstract: This paper deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that the data-driven approaches should not replace, but rather complement, traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communication networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is presented. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks, such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case studies wherein the use of deep learning proves extremely useful for network design. For each case study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement the data-driven approaches. Finally, concluding remarks describe those that, in our opinion, are the major directions for future research in this field.
366 citations
TL;DR: This paper proposes an efficient method to verify its feasibility via checking the connectivity between two given vertices on an equivalent graph, and obtains useful structural results on the optimal UAV trajectory, based on which two efficient methods are proposed to find high-quality approximate trajectory solutions by leveraging the techniques from graph theory and convex optimization.
Abstract: Integrating the unmanned aerial vehicles (UAVs) into the cellular network is envisioned to be a promising technology to significantly enhance the communication performance of both UAVs and existing terrestrial users. In this paper, we first provide an overview on the two main research paradigms in cellular UAV communications, namely, cellular-enabled UAV communication with UAVs as new aerial users served by the ground base stations (GBSs), and UAV-assisted cellular communication with UAVs as new aerial communication platforms serving the terrestrial users. Then, we focus on the former paradigm and study a new UAV trajectory design problem subject to practical communication connectivity constraints with the GBSs. Specifically, we consider a cellular-connected UAV in the mission of flying from an initial location to a final location that are given, during which it needs to maintain reliable communication with the cellular network by associating with one of the available GBSs at each time instant that has the best line-of-sight channel (or shortest distance) with it. We aim to minimize the UAV’s mission completion time by optimizing its trajectory, subject to a quality-of-connectivity constraint of the GBS-UAV link specified by a minimum receive signal-to-noise ratio target, which needs to be satisfied throughout its mission. To tackle this challenging non-convex optimization problem, we first propose an efficient method to verify its feasibility via checking the connectivity between two given vertices on an equivalent graph. Next, by examining the GBS-UAV association sequence over time, we obtain useful structural results on the optimal UAV trajectory, based on which two efficient methods are proposed to find high-quality approximate trajectory solutions by leveraging the techniques from graph theory and convex optimization. The proposed methods are analytically shown to be capable of achieving a flexible tradeoff between complexity and performance, and yielding a solution in polynomial time with the performance arbitrarily close to that of the optimal solution. Numerical results further validate the effectiveness of our proposed designs against benchmark schemes. Finally, we make concluding remarks and point out promising directions for future work.
315 citations
TL;DR: In this article, the authors proposed a new system design, where probabilistic and statistical constraints are imposed on task queue lengths, by applying extreme value theory to minimize users' power consumption while trading off the allocated resources for local computation and task offloading.
Abstract: To overcome devices’ limitations in performing computation-intense applications, mobile edge computing (MEC) enables users to offload tasks to proximal MEC servers for faster task computation. However, the current MEC system design is based on average-based metrics, which fails to account for the ultra-reliable low-latency requirements in mission-critical applications. To tackle this, this paper proposes a new system design, where probabilistic and statistical constraints are imposed on task queue lengths, by applying extreme value theory . The aim is to minimize users’ power consumption while trading off the allocated resources for local computation and task offloading. Due to wireless channel dynamics, users are reassociated to MEC servers in order to offload tasks using higher rates or accessing proximal servers. In this regard, a user–server association policy is proposed, taking into account the channel quality as well as the servers’ computation capabilities and workloads. By marrying tools from Lyapunov optimization and matching theory, a two-timescale mechanism is proposed, where a user–server association is solved in the long timescale, while a dynamic task offloading and resource allocation policy are executed in the short timescale. The simulation results corroborate the effectiveness of the proposed approach by guaranteeing highly reliable task computation and lower delay performance, compared to several baselines.
297 citations
TL;DR: This paper investigates the resource allocation algorithm design for multicarrier solar-powered unmanned aerial vehicle (UAV) communication systems and proposes a low-complexity iterative suboptimal online scheme based on the successive convex approximation.
Abstract: In this paper, we investigate the resource allocation algorithm design for multicarrier solar-powered unmanned aerial vehicle (UAV) communication systems. In particular, the UAV is powered by the solar energy enabling sustainable communication services to multiple ground users. We study the joint design of the 3D aerial trajectory and the wireless resource allocation for maximization of the system sum throughput over a given time period. As a performance benchmark, we first consider an off-line resource allocation design assuming non-causal knowledge of the channel gains. The algorithm design is formulated as a mixed-integer non-convex optimization problem taking into account the aerodynamic power consumption, solar energy harvesting, a finite energy storage capacity, and the quality-of-service requirements of the users. Despite the non-convexity of the optimization problem, we solve it optimally by applying monotonic optimization to obtain the optimal 3D-trajectory and the optimal power and subcarrier allocation policy. Subsequently, we focus on the online algorithm design that only requires real-time and statistical knowledge of the channel gains. The optimal online resource allocation algorithm is motivated by the off-line scheme and entails a high computational complexity. Hence, we also propose a low-complexity iterative suboptimal online scheme based on the successive convex approximation. Our simulation results reveal that both the proposed online schemes closely approach the performance of the benchmark off-line scheme and substantially outperform two baseline schemes. Furthermore, our results unveil the tradeoff between solar energy harvesting and power-efficient communication. In particular, the solar-powered UAV first climbs up to a high altitude to harvest a sufficient amount of solar energy and then descends again to a lower altitude to reduce the path loss of the communication links to the users it serves.
273 citations
TL;DR: In this article, the authors provide a broad perspective on the fundamental tradeoffs in ultra-reliable low latency communication (URLLC), as well as the principles used in building access protocols.
Abstract: The future connectivity landscape, and notably, the 5G wireless systems will feature Ultra-Reliable Low Latency Communication (URLLC). The coupling of high reliability and low latency requirements in URLLC use cases makes the wireless access design very challenging, in terms of both the protocol design and of the associated transmission techniques. This paper aims to provide a broad perspective on the fundamental tradeoffs in URLLC, as well as the principles used in building access protocols. Two specific technologies are considered in the context of URLLC: massive MIMO and multi-connectivity, also termed interface diversity . This paper also touches on the importance of the proper statistical methodology for designing and assessing extremely high-reliability levels.
221 citations
TL;DR: This paper proposes a UAV-assisted NOMA network, in which the UAV and base station (BS) cooperate with each other to serve ground users simultaneously, and proposes an iterative algorithm to solve the optimization problems.
Abstract: The explosive data traffic and connections in 5G networks require the use of non-orthogonal multiple access (NOMA) to accommodate more users. Unmanned aerial vehicle (UAV) can be exploited with NOMA to improve the situation further. In this paper, we propose a UAV-assisted NOMA network, in which the UAV and base station (BS) cooperate with each other to serve ground users simultaneously. The sum rate is maximized by jointly optimizing the UAV trajectory and the NOMA precoding. To solve the optimization, we decompose it into two steps. First, the sum rate of the UAV-served users is maximized via alternate user scheduling and UAV trajectory with its interference to the BS-served users below a threshold. Then, the optimal NOMA precoding vectors are obtained using two schemes with different constraints. The first scheme intends to cancel the interference from the BS to the UAV-served user, while the second one restricts the interference to a given threshold. In both schemes, the non-convex optimization problems are converted into tractable ones. An iterative algorithm is designed. Numerical results are provided to evaluate the effectiveness of the proposed algorithms for the hybrid NOMA and UAV network.
210 citations
TL;DR: Numerical results show that the proposed RS-assisted NOUM transmission strategies are more spectrally and energy efficient than the conventional Multi-User Linear-Precoding (MU–LP), Orthogonal Multiple Access (OMA) and power-domain NOMA in a wide range of user deployments.
Abstract: In a Non-Orthogonal Unicast and Multicast (NOUM) transmission system, a multicast stream intended to all the receivers is superimposed in the power domain on the unicast streams. One layer of Successive Interference Cancellation (SIC) is required at each receiver to remove the multicast stream before decoding its intended unicast stream. In this paper, we first show that a linearly-precoded 1-layer Rate-Splitting (RS) strategy at the transmitter can efficiently exploit this existing SIC receiver architecture. By splitting the unicast messages into common and private parts and encoding the common parts along with the multicast message into a super-common stream decoded by all users, the SIC is better reused for the dual purpose of separating the unicast and multicast streams as well as better managing the multi-user interference among the unicast streams. We further propose multi-layer transmission strategies based on the generalized RS and power-domain Non-Orthogonal Multiple Access (NOMA). Two different objectives are studied for the design of the precoders, namely, maximizing the Weighted Sum Rate (WSR) of the unicast messages and maximizing the system Energy Efficiency (EE), both subject to Quality of Service (QoS) rate requirements of all messages and a sum power constraint. A Weighted Minimum Mean Square Error (WMMSE)-based algorithm and a Successive Convex Approximation (SCA)-based algorithm are proposed to solve the WSR and EE problems, respectively. Numerical results show that the proposed RS-assisted NOUM transmission strategies are more spectrally and energy efficient than the conventional Multi-User Linear-Precoding (MU–LP), Orthogonal Multiple Access (OMA) and power-domain NOMA in a wide range of user deployments (with a diversity of channel directions, channel strengths and qualities of channel state information at the transmitter) and network loads (underloaded and overloaded regimes). It is superior for the downlink multi-antenna NOUM transmission.
209 citations
TL;DR: A novel scheme to guarantee the security of UAV-relayed wireless networks with caching via jointly optimizing the UAV trajectory and time scheduling and a benchmark scheme in which the minimum average secrecy rate among all users is maximized and no user has the caching ability.
Abstract: Unmanned aerial vehicle (UAV) can be utilized as a relay to connect nodes with long distance, which can achieve significant throughput gain owing to its mobility and line-of-sight (LoS) channel with ground nodes. However, such LoS channels make UAV transmission easy to eavesdrop. In this paper, we propose a novel scheme to guarantee the security of UAV-relayed wireless networks with caching via jointly optimizing the UAV trajectory and time scheduling. For every two users that have cached the required file for the other, the UAV broadcasts the files together to these two users, and the eavesdropping can be disrupted. For the users without caching, we maximize their minimum average secrecy rate by jointly optimizing the trajectory and scheduling, with the secrecy rate of the caching users satisfied. The corresponding optimization problem is difficult to solve due to its non-convexity, and we propose an iterative algorithm via successive convex optimization to solve it approximately. Furthermore, we also consider a benchmark scheme in which we maximize the minimum average secrecy rate among all users by jointly optimizing the UAV trajectory and time scheduling when no user has the caching ability. Simulation results are provided to show the effectiveness and efficiency of our proposed scheme.
187 citations
TL;DR: This paper investigates the HBF design for broadband mmWave transmissions and proposes a manifold optimization-based HBF algorithm, which directly handles the constant modulus constraint of the analog component and achieves a significant performance improvement over existing ones and performs close to full-digital beamforming.
Abstract: Hybrid analog and digital beamforming (HBF) has recently emerged as an attractive technique for millimeter-wave (mmWave) communication systems. It well balances the demand for sufficient beamforming gains to overcome the propagation loss and the desire to reduce the hardware cost and power consumption. In this paper, the mean square error (MSE) is chosen as the performance metric to characterize the transmission reliability. Using the minimum sum-MSE criterion, we investigate the HBF design for broadband mmWave transmissions. To overcome the difficulty of solving the multi-variable design problem, the alternating minimization method is adopted to optimize the hybrid transmit and receive beamformers alternatively. Specifically, a manifold optimization-based HBF algorithm is first proposed, which directly handles the constant modulus constraint of the analog component. Its convergence is then proved. To reduce the computational complexity, we then propose a low-complexity general eigenvalue decomposition-based HBF algorithm in the narrowband scenario and three algorithms via the eigenvalue decomposition and orthogonal matching pursuit methods in the broadband scenario. A particular innovation in our proposed alternating minimization algorithms is a carefully designed initialization method, which leads to a faster convergence. Furthermore, we extend the sum-MSE-based design to that with weighted sum-MSE, which is then connected to the spectral efficiency-based design. Simulation results show that the proposed HBF algorithms achieve a significant performance improvement over existing ones and perform close to full-digital beamforming.
172 citations
TL;DR: In this article, a real-time IoT monitoring system is considered, in which the IoT devices sample a physical process with a sampling cost and send the status packet to a given destination with an updating cost.
Abstract: The effective operation of time-critical Internet of things (IoT) applications requires real-time reporting of fresh status information of underlying physical processes. In this paper, a real-time IoT monitoring system is considered, in which the IoT devices sample a physical process with a sampling cost and send the status packet to a given destination with an updating cost. This joint status sampling and updating process is designed to minimize the average age of information (AoI) at the destination node under an average energy cost constraint at each device. This stochastic problem is formulated as an infinite horizon average cost constrained Markov decision process (CMDP) and transformed into an unconstrained Markov decision process (MDP) using a Lagrangian method. For the single IoT device case, the optimal policy for the CMDP is shown to be a randomized mixture of two deterministic policies for the unconstrained MDP, which is of threshold type. This reveals a fundamental tradeoff between the average AoI at the destination and the sampling and updating costs. Then, a structure-aware optimal algorithm to obtain the optimal policy of the CMDP is proposed and the impact of the wireless channel dynamics is studied while demonstrating that channels having a larger mean channel gain and less scattering can achieve better AoI performance. For the case of multiple IoT devices, a low-complexity semi-distributed suboptimal policy is proposed with the updating control at the destination and the sampling control at each IoT device. Then, an online learning algorithm is developed to obtain this policy, which can be implemented at each IoT device and requires only the local knowledge and small signaling from the destination. The proposed learning algorithm is shown to converge almost surely to the suboptimal policy. Simulation results show the structural properties of the optimal policy for the single IoT device case; and show that the proposed policy for multiple IoT devices outperforms a zero-wait baseline policy, with average AoI reductions reaching up to 33%.
168 citations
TL;DR: In this paper, the authors exploit the multi-antenna non-orthogonal multiple access (NOMA) technique for multiuser computation offloading, such that different users can simultaneously offload their computation tasks to the multiple antenna BS over the same time/frequency resources, and the BS can employ successive interference cancellation (SIC) to efficiently decode all users' offloaded tasks for remote execution.
Abstract: This paper studies a multiuser mobile edge computing (MEC) system in which one base station (BS) serves multiple users with intensive computation tasks. We exploit the multi-antenna non-orthogonal multiple access (NOMA) technique for multiuser computation offloading, such that different users can simultaneously offload their computation tasks to the multi-antenna BS over the same time/frequency resources, and the BS can employ successive interference cancelation (SIC) to efficiently decode all users’ offloaded tasks for remote execution. In particular, we pursue energy-efficient MEC designs by considering two cases with partial and binary offloading, respectively. We aim to minimize the weighted sum-energy consumption at all users subject to their computation latency constraints, by jointly optimizing the communication and computation resource allocation as well as the BS’s decoding order for SIC. For the case with partial offloading, the weighted sum-energy minimization is a convex optimization problem, for which an efficient algorithm based on the Lagrange duality method is presented to obtain the globally optimal solution. For the case with binary offloading, the weighted sum-energy minimization corresponds to a mixed Boolean convex optimization problem that is generally more difficult to be solved. We first use the branch-and-bound (BnB) method to obtain the globally optimal solution and then develop two low-complexity algorithms based on the greedy method and the convex relaxation, respectively, to find suboptimal solutions with high quality in practice. Via numerical results, it is shown that the proposed NOMA-based computation offloading design significantly improves the energy efficiency of the multiuser MEC system as compared to other benchmark schemes. It is also shown that for the case with binary offloading, the proposed greedy method performs close to the optimal BnB-based solution, and the convex relaxation-based solution achieves a suboptimal performance but with lower implementation complexity.
TL;DR: In this paper, the authors considered the coexistence of two important communication techniques, NOMA and mobile edge computing (MEC), and analytical results were developed to demonstrate that the use of NOMa can efficiently reduce the latency and energy consumption of MEC offloading.
Abstract: This paper considers the co-existence of two important communication techniques, non-orthogonal multiple access (NOMA) and mobile edge computing (MEC). Both NOMA uplink and downlink transmissions are applied to MEC, and analytical results are developed to demonstrate that the use of NOMA can efficiently reduce the latency and energy consumption of MEC offloading. In addition, various asymptotic studies are carried out to reveal the impact of the users’ channel conditions and transmit powers on the application of NOMA to MEC are quite different from those in conventional NOMA scenarios. Computer simulation results are also provided to facilitate the performance evaluation of NOMA-MEC and also verify the accuracy of the developed analytical results.
TL;DR: In this paper, the authors considered a multiuser communication system, in which a single-antenna UAV-BS serves a large number of ground users by employing non-orthogonal multiple access (NOMA).
Abstract: Unmanned aerial vehicles (UAVs) can be deployed as flying base stations (BSs) to leverage the strength of line-of-sight connections and effectively support the coverage and throughput of wireless communication. This paper considers a multiuser communication system, in which a single-antenna UAV-BS serves a large number of ground users by employing non-orthogonal multiple access (NOMA). The max-min rate optimization problem is formulated under total power, total bandwidth, UAV altitude, and antenna beamwidth constraints. The objective of max-min rate optimization is non-convex in all optimization variables, i.e., UAV altitude, transmit antenna beamwidth, power allocation, and bandwidth allocation for multiple users. A path-following algorithm is proposed to solve the formulated problem. Next, orthogonal multiple access (OMA) and dirty paper coding (DPC)-based max-min rate optimization problems are formulated and respective path-following algorithms are developed to solve them. The numerical results show that NOMA outperforms OMA and achieves rates similar to those attained by DPC. In addition, a clear rate gain is observed by jointly optimizing all the parameters rather than optimizing a subset of parameters, which confirms the desirability of their joint optimization.
TL;DR: A new wireless network architecture of coordinate multipoint (CoMP) in the sky to harness both the benefits of interference mitigation via CoMP and high mobility of UAVs is proposed.
Abstract: Driven by the recent advancement in unmanned aerial vehicle (UAV) technology, this paper proposes a new wireless network architecture of coordinate multipoint (CoMP) in the sky to harness both the benefits of interference mitigation via CoMP and high mobility of UAVs. Specifically, we consider uplink communications in a multi-UAV enabled multi-user system, where each UAV forwards its received signals from all ground users to a central processor (CP) for joint decoding. Moreover, we consider the case where the users may move on the ground, thus the UAVs need to adjust their locations in accordance with the user locations over time to maximize the network throughput. Utilizing random matrix theory, we first characterize, in closed form, a set of approximated upper and lower bounds of the user’s achievable rate in each time epoch under the practical Rician fading channel model, which is shown to be very tight, both analytically and numerically. UAV placement and movement over different epochs are then optimized based on the derived bounds to maximize the minimum of user average achievable rates over all epochs for both cases of full information (of current and future epochs) and current information on the user’s movement. Interestingly, it is shown that the optimized location of each UAV at any particular epoch is the weighted average of the ground user locations at the current epoch as well as its own location at the previous and/or next epoch. Finally, simulation results are provided to validate and compare the performance of the proposed UAV placement and movement designs under different practical application scenarios.
TL;DR: This paper proposes an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the online gradient descent (OGD) method and the follow the (proximally) regularized leader (FTRL-Proximal) method.
Abstract: In this paper, the edge caching problem in fog radio access network (F-RAN) is investigated. By maximizing the overall cache hit rate, the edge caching optimization problem is formulated to find the optimal policy. Content popularity in terms of time and space is considered from the perspective of regional users. We propose an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the online gradient descent (OGD) method and the follow the (proximally) regularized leader (FTRL-Proximal) method. Our proposed edge caching policy not only can promptly predict the future content popularity in an online fashion with low complexity, but also can track the content popularity with spatial and temporal popularity dynamic in time without delay. Furthermore, we design two learning-based edge caching architectures. Moreover, we theoretically derive the upper bound of the popularity prediction error, the lower bound of the cache hit rate, and the regret bound of the overall cache hit rate of our proposed edge caching policy. Simulation results show that the overall cache hit rate of our proposed policy is superior to those of the traditional policies and asymptotically approaches the optimal performance.
TL;DR: This is the first-ever comprehensive channel model addressing the statistics of optical beam irradiance fluctuations in underwater wireless optical channels due to both air bubbles and temperature gradient, and it is shown to provide a perfect fit with the measured data under all channel conditions for both types of water.
Abstract: A unified statistical model is proposed to characterize turbulence-induced fading in underwater wireless optical communication (UWOC) channels in the presence of air bubbles and temperature gradient for fresh and salty waters, based on experimental data. In this model, the channel irradiance fluctuations are characterized by the mixture exponential–generalized gamma (EGG) distribution. We use the expectation–maximization algorithm to obtain the maximum likelihood parameter estimation of the new model. Interestingly, the proposed model is shown to provide a perfect fit with the measured data under all channel conditions for both types of water. The major advantage of the new model is that it has a simple mathematical form making it attractive from a performance analysis point of view. Indeed, we show that the application of the EGG model leads to closed-form and analytically tractable expressions for key UWOC system performance metrics such as the outage probability, the average bit-error rate, and the ergodic capacity. To the best of our knowledge, this is the first-ever comprehensive channel model addressing the statistics of optical beam irradiance fluctuations in underwater wireless optical channels due to both air bubbles and temperature gradient.
TL;DR: A novel MEC-based mobile VR delivery framework that is able to cache parts of the field of views (FOVs) in advance and compute certain post-processing procedures on demand at the mobile VR device is presented.
Abstract: Virtual reality (VR) over wireless is emerging as an important use case of 5G networks. Fully-immersive VR experience requires the wireless delivery of huge data at ultra-low latency, thus leading to ultra-high transmission rate requirement for wireless communications. This challenge can be largely addressed by the recent network architecture known as mobile edge computing (MEC) network, which enables caching and computing capabilities at the edge of wireless networks. This paper presents a novel MEC-based mobile VR delivery framework that is able to cache parts of the field of views (FOVs) in advance and compute certain post-processing procedures on demand at the mobile VR device. To minimize the average required transmission rate, we formulate the joint caching and computing optimization problem to determine which FOVs to cache, whether to cache them in 2D or 3D as well as which FOVs to compute at the mobile device under cache size, average power consumption as well as latency constraints. When FOVs are homogeneous, we obtain a closed-form expression for the optimal joint policy which reveals interesting communications-caching-computing tradeoffs. When FOVs are heterogeneous, we obtain a local optima of the problem by transforming it into a linearly constrained indefinite quadratic problem and then applying concave convex procedure. Numerical results demonstrate the proposed mobile VR delivery framework can significantly reduce communication bandwidth while meeting low latency requirement.
TL;DR: A expectation maximization-based sparse Bayesian learning framework is developed and the Kalman filter and the Rauch–Tung–Striebel smoother are utilized to track the model parameters of the uplink spatial sparse channel in the expectation step.
Abstract: The low-rank property of the channel covariances can be adopted to reduce the overhead of the channel training in massive MIMO systems. In this paper, with the help of the virtual channel representation, we apply such property to both time-division duplex and frequency-division duplex systems, where the time-varying channel scenarios are considered. First, we formulate the dynamic massive MIMO channel as one sparse signal model. Then, an expectation maximization-based sparse Bayesian learning framework is developed to learn the model parameters of the sparse virtual channel. Specifically, the Kalman filter (KF) and the Rauch–Tung–Striebel smoother are utilized to track the model parameters of the uplink (UL) spatial sparse channel in the expectation step. During the maximization step, a fixed-point theorem-based algorithm and a low-complex searching method are constructed to recover the temporal varying characteristics and the spatial signatures, respectively. With the angle reciprocity, we recover the downlink (DL) model parameters from the UL ones. After that, the KF with the reduced dimension is adopt to fully exploit the channel temporal correlations to enhance the DL/UL virtual channel tracking accuracy. A monitoring scheme is also designed to detect the change of model parameters and trigger the relearning process. Finally, we demonstrate the efficacy of the proposed schemes through the numerical simulations.
TL;DR: In this article, the outage probability and the ergodic rate of MIMO-NOMA-assisted UAV networks were derived by utilizing a stochastic geometry model.
Abstract: This paper investigates the multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) assisted unmanned aerial vehicles (UAVs) networks. By utilizing a stochastic geometry model, a new 3-D UAV framework for providing wireless service to randomly roaming NOMA users has been proposed. In an effort to evaluate the performance of the proposed framework, we derive analytical expressions for the outage probability and the ergodic rate of MIMO-NOMA enhanced UAV networks. We examine tractable upper bounds for the whole proposed framework, with deriving asymptotic results for scenarios that transmit power of interference sources being proportional or being fixed to the UAV. For obtaining more insights for the proposed framework, we investigate the diversity order and high signal-to-noise slope of MIMO-NOMA assisted UAV networks. Our results confirm that: 1) Outage probability of NOMA enhanced UAV networks is affected to a large extent by the targeted transmission rates and power allocation factors of NOMA users and 2) For the case that the interference power is proportional to the UAV power, there are error floors for the outage probabilities.
TL;DR: This paper develops a closed-form path loss expression as a function of transceiver parameters and water type and utilizes this new expression to determine the maximum achievable link distance for UVLC systems in pure sea, clear ocean, coastal water, and harbor water.
Abstract: In this paper, we investigate the performance limits of underwater visible light communication (UVLC) systems. We first develop a closed-form path loss expression as a function of transceiver parameters and water type. We then utilize this new expression to determine the maximum achievable link distance for UVLC systems in pure sea, clear ocean, coastal water, and harbor water. Our results demonstrate that the maximum achievable distance is limited to a few tens of meters. This necessitates the deployment of relay-assisted UVLC systems to extend the transmission range. We consider both detect-and-forward and amplify-and-forward relaying. For each relaying method, we first consider a dual-hop UVLC system and determine optimal relay placement to minimize the bit error rate (BER). Then, we consider a multi-hop system with equidistant relays and determine the maximum achievable distance for a given number of hops to satisfy a targeted end-to-end BER.
TL;DR: In this paper, an orientation-based random waypoint (ORWP) mobility model is proposed by considering the random orientation of the UE during the user's movement, and the performance of ORWP is assessed on the handover rate.
Abstract: Light-fidelity (LiFi) is a networked optical wireless communication (OWC) solution for high-speed indoor connectivity for fixed and mobile optical communications. Unlike conventional radio frequency wireless systems, the OWC channel is not isotropic, meaning that the device orientation affects the channel gain significantly, particularly for mobile users. However, due to the lack of a proper model for device orientation, many studies have assumed that the receiver is vertically upward and fixed. In this paper, a novel model for device orientation based on experimental measurements of 40 participants has been proposed. It is shown that the probability density function (PDF) of the polar angle can be modeled either based on a Laplace (for static users) or a Gaussian (for mobile users) distribution. In addition, a closed-form expression is obtained for the PDF of the cosine of the incidence angle based on which the line-of-sight (LOS) channel gain is described in OWC channels. An approximation of this PDF based on the truncated Laplace is proposed and the accuracy of this approximation is confirmed by the Kolmogorov–Smirnov distance. Moreover, the statistics of the LOS channel gain are calculated and the random orientation of a user equipment (UE) is modeled as a random process. The influence of the random orientation on signal-to-noise-ratio performance of OWC systems has been evaluated. Finally, an orientation-based random waypoint (ORWP) mobility model is proposed by considering the random orientation of the UE during the user’s movement. The performance of ORWP is assessed on the handover rate and it is shown that it is important to take the random orientation into account.
TL;DR: A novel framework is proposed, where collisions are treated as interference to the remaining received signals and simplified expressions are derived that can well approximate the outage probability and throughput of the system for both successive joint decoding (SJD) and successive interference cancellation (SIC).
Abstract: In this paper, we consider a massive grant-free non-orthogonal multiple access (GF-NOMA) scheme, where devices have strict latency requirements and no retransmission opportunities are available. Each device chooses a pilot sequence from a predetermined set as its signature and transmits its selected pilot and data simultaneously. A collision occurs when two or more devices choose the same pilot sequence. Existing GF-NOMA schemes assume that a collision of at least one pair of users entails a collision for all simultaneously transmitting users, which is sub-optimal in terms of individual outage and system throughput. For that, we propose a novel framework, where collisions are treated as interference to the remaining received signals. With the aid of Poisson point processes and ordered statistics, we derive simplified expressions that can well approximate the outage probability and throughput of the system for both successive joint decoding (SJD) and successive interference cancellation (SIC). Numerical results verify the accuracy of our analytical expressions. For low data rate transmissions, results show that the performance of SIC is close to that of SJD in terms of outage probability, for packet arrival rates up to 10 packets per slot. However, SJD can achieve almost double the throughput of SIC and is, thus, far more superior.
TL;DR: A framework for enabling ultra-reliable and low-latency communications in the control and non-payload communications (CNPC) links of the unmanned aerial vehicle (UAV) communication systems is established and an algorithm that can converge to the optimal solution in DAS and CAS is proposed.
Abstract: In this paper, we establish a framework for enabling ultra-reliable and low-latency communications in the control and non-payload communications (CNPC) links of the unmanned aerial vehicle (UAV) communication systems. We first derive the available range of the CNPC links between UAVs and a ground control station. The available range is defined as the maximal horizontal communication distance within which the round-trip delay and the overall packet loss probability can be ensured with a required probability. To exploit the macro-diversity gain of the distributed multi-antenna systems (DAS) and the array gain of the centralized multi-antenna systems (CAS), we consider a modified DAS (M-DAS), where the ground control station is equipped with the distributed access points (APs), and each AP can have multiple antennas. We then show that the available range can be maximized by judiciously optimizing the altitude of UAVs, the duration of the uplink and downlink phases, and the antenna configuration. To solve the non-convex problem, we propose an algorithm that can converge to the optimal solution in DAS and CAS, and then extend it into more general M-DAS. The simulation and numerical results validate our analysis and show that the available range of M-DAS can be significantly larger than those of the DAS and CAS.
TL;DR: This paper studies a novel device-to-device (D2D)-enabled multi-helper MEC system, in which a local user solicits its nearby WDs serving as helpers for cooperative computation and proposes an efficient algorithm by first relaxing the original problem into a convex one, and then constructing a suboptimal task assignment solution based on the obtained optimal one.
Abstract: With the proliferation of computation-extensive and latency-critical applications in the 5G and beyond networks, mobile-edge computing (MEC) or fog computing, which provides cloud-like computation and/or storage capabilities at the network edge, is envisioned to reduce computation latency as well as to conserve energy for wireless devices (WDs). This paper studies a novel device-to-device (D2D)-enabled multi-helper MEC system, in which a local user solicits its nearby WDs serving as helpers for cooperative computation. We assume a time division multiple access (TDMA) transmission protocol, under which the local user offloads the tasks to multiple helpers and downloads the results from them over orthogonal pre-scheduled time slots. Under this setup, we minimize the computation latency by optimizing the local user’s task assignment jointly with the time and rate for task offloading and results downloading, as well as the computation frequency for task execution, subject to individual energy and computation capacity constraints at the local user and the helpers. However, the formulated problem is a mixed-integer non-linear program (MINLP) that is difficult to solve. To tackle this challenge, we propose an efficient algorithm by first relaxing the original problem into a convex one, and then constructing a suboptimal task assignment solution based on the obtained optimal one. Furthermore, we consider a benchmark scheme that endows the WDs with their maximum computation capacities. To further reduce the implementation complexity, we also develop a heuristic scheme based on the greedy task assignment. Finally, the numerical results validate the effectiveness of our proposed algorithm, as compared against the heuristic scheme and other benchmark ones without either joint optimization of radio and computation resources or task assignment design.
TL;DR: A novel framework is proposed for optimizing a platoon's operation while jointly taking into account the delay of the wireless V2V network and the stability of the vehicle’s control system, and guidelines for designing an autonomous platoon so as to realize the required wireless network reliability and control system stability.
Abstract: Autonomous vehicular platoons will play an important role in improving on-road safety in tomorrow’s smart cities. Vehicles in an autonomous platoon can exploit vehicle-to-vehicle (V2V) communications to collect environmental information so as to maintain the target velocity and inter-vehicle distance. However, due to the uncertainty of the wireless channel, V2V communications within a platoon will experience a wireless system delay. Such system delay can impair the vehicles’ ability to stabilize their velocity and distances within their platoon. In this paper, the problem of integrated communication and control system is studied for wireless connected autonomous vehicular platoons. In particular, a novel framework is proposed for optimizing a platoon’s operation while jointly taking into account the delay of the wireless V2V network and the stability of the vehicle’s control system. First, stability analysis for the control system is performed and the maximum wireless system delay requirements which can prevent the instability of the control system are derived. Then, delay analysis is conducted to determine the end-to-end delay, including queuing, processing, and transmission delay for the V2V link in the wireless network. Subsequently, using the derived wireless delay, a lower bound and an approximated expression of the reliability for the wireless system, defined as the probability that the wireless system meets the control system’s delay needs, are derived. Then, the parameters of the control system are optimized in a way to maximize the derived wireless system reliability. Simulation results corroborate the analytical derivations and study the impact of parameters, such as the packet size and the platoon size, on the reliability performance of the vehicular platoon. More importantly, the simulation results shed light on the benefits of integrating control system and wireless network design while providing guidelines for designing an autonomous platoon so as to realize the required wireless network reliability and control system stability.
TL;DR: A full duplex non-orthogonal multiple access (FD-NOMA)-based decentralized V2X system model is introduced and the approximate closed-form expressions with arbitrary small errors are given to solve the computation complicated problems of the involved exponential integral functions.
Abstract: In order to meet the requirements of massively connected devices, different quality of services (QoS), various transmit rates, and ultra-reliable and low latency communications (URLLC) in vehicle-to-everything (V2X) communications, we introduce a full duplex non-orthogonal multiple access (FD-NOMA)-based decentralized V2X system model. We, then, classify the V2X communications into two scenarios and give their exact capacity expressions. To solve the computation complicated problems of the involved exponential integral functions, we give the approximate closed-form expressions with arbitrary small errors. Numerical results indicate the validness of our derivations. Our analysis has that the accuracy of our approximate expressions is controlled by the division of $\frac {\pi }{2}$ in the urban and crowded scenarios, and the truncation point ${T}$ in the suburban and remote scenarios. Numerical results manifest that: 1) increasing the number of V2X device, NOMA power, and Rician factor value yields a better capacity performance; 2) effect of FD-NOMA is determined by the FD self-interference and the channel noise; and 3) FD-NOMA has a better latency performance compared with other schemes.
TL;DR: In this article, a multi-beam non-orthogonal multiple access (NOMA) scheme for hybrid millimeter wave (mmWave) systems and its resource allocation is proposed.
Abstract: In this paper, we propose a multi-beam non-orthogonal multiple access (NOMA) scheme for hybrid millimeter wave (mmWave) systems and study its resource allocation. A beam splitting technique is designed to generate multiple analog beams to serve multiple NOMA users on each radio frequency chain. In contrast to the recently proposed single-beam mmWave-NOMA scheme which can only serve multiple NOMA users within the same analog beam, the proposed scheme can perform NOMA transmission for the users with an arbitrary angle-of-departure distribution. This provides a higher flexibility for applying NOMA in mmWave communications and thus can efficiently exploit the potential multi-user diversity. Then, we design a suboptimal two-stage resource allocation for maximizing the system sum-rate. In the first stage, assuming that only analog beamforming is available, a user grouping and antenna allocation algorithm is proposed to maximize the conditional system sum-rate based on the coalition formation game theory. In the second stage, with the zero-forcing digital precoder, a suboptimal solution is devised to solve a non-convex power allocation optimization problem for the maximization of the system sum-rate which takes into account the quality of service constraints. Simulation results show that our designed resource allocation can achieve a close-to-optimal performance in each stage. In addition, we demonstrate that the proposed multi-beam mmWave-NOMA scheme offers a substantial spectral efficiency improvement compared to that of the single-beam mmWave-NOMA and the mmWave orthogonal multiple access schemes.
TL;DR: In this paper, a non-orthogonal multiple access (NOMA) transmission protocol that incorporates orthogonal time frequency space (OTFS) modulation is proposed, where users with different mobility profiles are grouped together for the implementation of NOMA.
Abstract: This paper considers a challenging communication scenario, in which users have heterogenous mobility profiles, e.g., some users are moving at high speeds and some users are static. A new non-orthogonal multiple-access (NOMA) transmission protocol that incorporates orthogonal time frequency space (OTFS) modulation is proposed. Thereby, users with different mobility profiles are grouped together for the implementation of NOMA. The proposed OTFS-NOMA protocol is shown to be applicable to both uplink and downlink transmission, where sophisticated transmit and receive strategies are developed to remove inter-symbol interference and harvest both multi-path and multi-user diversity. Analytical results demonstrate that both the high-mobility and the low-mobility users benefit from the application of OTFS-NOMA. In particular, the use of NOMA allows the spreading of the high-mobility users’ signals over a large amount of time-frequency resources, which enhances the OTFS resolution and improves the detection reliability. In addition, OTFS-NOMA ensures that low-mobility users have access to bandwidth resources which in conventional OTFS-orthogonal multiple access (OTFS-OMA) would be solely occupied by the high-mobility users. Thus, OTFS-NOMA improves the spectral efficiency and reduces latency.
TL;DR: This paper forms a joint user association and resource allocation problem in the downlink of the fog network, considering the evergrowing demand of QoS requirements imposed by the ultra-reliable low latency communications and enhanced mobile broadband services and solves the network resource allocationproblem by applying the “best-fit” resource allocation strategy during matching.
Abstract: In recent years, in order to provide a better quality of service (QoS) to Internet of Things (IoT) devices, the cloud computing paradigm has shifted toward the edge. However, the resource capacity (e.g., bandwidth) in fog network technology is limited and it is essential to efficiently bind the IoT applications with stringent QoS requirements with the available network infrastructure. In this paper, we formulate a joint user association and resource allocation problem in the downlink of the fog network, considering the evergrowing demand of QoS requirements imposed by the ultra-reliable low latency communications and enhanced mobile broadband services. First, we determine the priority of different QoS requirements of heterogeneous IoT applications at the fog network by enforcing the analytical framework using an analytic hierarchy process (AHP). Using the AHP, we then formulate a two-sided matching game to initiate stable association between the fog network infrastructure (i.e., fog devices) and IoT devices. Subsequently, we consider the externalities in the matching game that occurs due to job delay and solve the network resource allocation problem by applying the “best-fit” resource allocation strategy during matching. The simulation results illustrate the stability of the user association and efficiency of resource allocation with higher utility gain.
TL;DR: In this article, the authors considered a general multipair mMIMO relaying system with a mixed-ADC/DAC architecture, in which some antennas are connected to low-resolution ADCs and DACs, while the rest of the antennas were connected to high-resolution ADC/DCs.
Abstract: High power consumption and expensive hardware are two bottlenecks for practical massive multiple-input multiple-output (mMIMO) systems. One promising solution is to employ low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). In this paper, we consider a general multipair mMIMO relaying system with a mixed-ADC/DAC architecture, in which some antennas are connected to low-resolution ADCs/DACs, while the rest of the antennas are connected to high-resolution ADCs/DACs. Leveraging on the additive quantization noise model, both exact and approximate closed-form expressions for the achievable rate are derived. It is shown that the achievable rate can approach the unquantized one by using only 2–3 bits of resolutions. Moreover, a power scaling law is presented to reveal that the transmit power can be scaled down inversely proportional to the number of antennas at the relay. We further propose an efficient power allocation scheme by solving a complementary geometric programming problem. In addition, a tradeoff between the achievable rate and power consumption for different numbers of low-resolution ADCs/DACs is investigated by deriving the energy efficiency. Our results reveal that the large antenna array can be exploited to enable the mixed-ADC/DAC architecture, which significantly reduces the power consumption and hardware cost for practical mMIMO systems.