Showing papers in "IEEE Transactions on Wireless Communications in 2019"

Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming

Abstract: Intelligent reflecting surface (IRS) is a revolutionary and transformative technology for achieving spectrum and energy efficient wireless communication cost-effectively in the future. Specifically, an IRS consists of a large number of low-cost passive elements each being able to reflect the incident signal independently with an adjustable phase shift so as to collaboratively achieve three-dimensional (3D) passive beamforming without the need of any transmit radio-frequency (RF) chains. In this paper, we study an IRS-aided single-cell wireless system where one IRS is deployed to assist in the communications between a multi-antenna access point (AP) and multiple single-antenna users. We formulate and solve new problems to minimize the total transmit power at the AP by jointly optimizing the transmit beamforming by active antenna array at the AP and reflect beamforming by passive phase shifters at the IRS, subject to users’ individual signal-to-interference-plus-noise ratio (SINR) constraints. Moreover, we analyze the asymptotic performance of IRS’s passive beamforming with infinitely large number of reflecting elements and compare it to that of the traditional active beamforming/relaying. Simulation results demonstrate that an IRS-aided MIMO system can achieve the same rate performance as a benchmark massive MIMO system without using IRS, but with significantly reduced active antennas/RF chains. We also draw useful insights into optimally deploying IRS in future wireless systems.

Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication

Abstract: The adoption of a reconfigurable intelligent surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to 300% higher energy efficiency in comparison with the use of regular multi-antenna amplify-and-forward relaying.

Energy Minimization for Wireless Communication With Rotary-Wing UAV

Abstract: This paper studies unmanned aerial vehicle (UAV)-enabled wireless communication, where a rotary-wing UAV is dispatched to communicate with multiple ground nodes (GNs). We aim to minimize the total UAV energy consumption, including both propulsion energy and communication related energy, while satisfying the communication throughput requirement of each GN. To this end, we first derive a closed-form propulsion power consumption model for rotary-wing UAVs, and then formulate the energy minimization problem by jointly optimizing the UAV trajectory and communication time allocation among GNs, as well as the total mission completion time. The problem is difficult to be optimally solved, as it is non-convex and involves infinitely many variables over time. To tackle this problem, we first consider the simple fly-hover-communicate design, where the UAV successively visits a set of hovering locations and communicates with one corresponding GN while hovering at each location. For this design, we propose an efficient algorithm to optimize the hovering locations and durations, as well as the flying trajectory connecting these hovering locations, by leveraging the travelling salesman problem with neighborhood and convex optimization techniques. Next, we consider the general case, where the UAV also communicates while flying. We propose a new path discretization method to transform the original problem into a discretized equivalent with a finite number of optimization variables, for which we obtain a high-quality suboptimal solution by applying the successive convex approximation technique. The numerical results show that the proposed designs significantly outperform the benchmark schemes.

Securing UAV Communications via Joint Trajectory and Power Control

Abstract: Unmanned aerial vehicle (UAV) communication is anticipated to be widely applied in the forthcoming fifth-generation wireless networks, due to its many advantages such as low cost, high mobility, and on-demand deployment. However, the broadcast and line-of-sight nature of air-to-ground wireless channels give rise to a new challenge on how to realize secure UAV communications with the destined nodes on the ground. This paper aims to tackle this challenge by applying the physical layer security technique. We consider both the downlink and uplink UAV communications with a ground node, namely, UAV-to-ground (U2G) and ground-to-UAV (G2U) communications, respectively, subject to a potential eavesdropper on the ground. In contrast to the existing literature on the wireless physical layer security only with the ground nodes at fixed or quasi-static locations, we exploit the high mobility of the UAV to proactively establish favorable and degraded channels for the legitimate and eavesdropping links, through its trajectory design. We formulate new problems to maximize the average secrecy rates of the U2G and G2U transmissions, by jointly optimizing the UAV’s trajectory, and the transmit power of the legitimate transmitter over a given flight period of the UAV. Although the formulated problems are non-convex, we propose iterative algorithms to solve them efficiently by applying the block coordinate descent and successive convex optimization methods. Specifically, both the transmit power and UAV trajectory are optimized, with the other being fixed in an alternating manner, until the algorithms converge. The simulation results show that the proposed algorithms can improve the secrecy rates for both U2G and G2U communications, as compared to other benchmark schemes without power control and/or trajectory optimization.

Beyond 5G With UAVs: Foundations of a 3D Wireless Cellular Network

Abstract: In this paper, a novel concept of three-dimensional (3D) cellular networks, that integrate drone base stations (drone-BS) and cellular-connected drone users (drone-UEs), is introduced. For this new 3D cellular architecture, a novel framework for network planning for drone-BSs and latency-minimal cell association for drone-UEs is proposed. For network planning, a tractable method for drone-BSs’ deployment based on the notion of truncated octahedron shapes is proposed, which ensures full coverage for a given space with a minimum number of drone-BSs. In addition, to characterize frequency planning in such 3D wireless networks, an analytical expression for the feasible integer frequency reuse factors is derived. Subsequently, an optimal 3D cell association scheme is developed for which the drone-UEs’ latency, considering transmission, computation, and backhaul delays, is minimized. To this end, first, the spatial distribution of the drone-UEs is estimated using a kernel density estimation method, and the parameters of the estimator are obtained using a cross-validation method. Then, according to the spatial distribution of drone-UEs and the locations of drone-BSs, the latency-minimal 3D cell association for drone-UEs is derived by exploiting tools from an optimal transport theory. The simulation results show that the proposed approach reduces the latency of drone-UEs compared with the classical cell association approach that uses a signal-to-interference-plus-noise ratio (SINR) criterion. In particular, the proposed approach yields a reduction of up to 46% in the average latency compared with the SINR-based association. The results also show that the proposed latency-optimal cell association improves the spectral efficiency of a 3D wireless cellular network of drones.

Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access

Abstract: We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into $K$ orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the spectrum access problem is computationally expensive, in general, due to the large-state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to the spectrum access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. The experimental results demonstrate the strong performance of the algorithm.

Cellular UAV-to-X Communications: Design and Optimization for Multi-UAV Networks

Abstract: In this paper, we consider a single-cell cellular network with a number of cellular users (CUs) and unmanned aerial vehicles (UAVs), in which multiple UAVs upload their collected data to the base station (BS). Two transmission modes are considered to support the multi-UAV communications, i.e., UAV-to-network (U2N) and UAV-to-UAV (U2U) communications. Specifically, the UAV with a high signal-to-noise ratio (SNR) for the U2N link uploads its collected data directly to the BS through U2N communication, while the UAV with a low SNR for the U2N link can transmit data to a nearby UAV through underlaying U2U communication for the sake of quality of service. We first propose a cooperative UAV sense-and-send protocol to enable the UAV-to-X communications, and then formulate the subchannel allocation and UAV speed optimization problem to maximize the uplink sum-rate. To solve this NP-hard problem efficiently, we decouple it into three sub-problems: U2N and cellular user (CU) subchannel allocation, U2U subchannel allocation, and UAV speed optimization. An iterative subchannel allocation and speed optimization algorithm (ISASOA) is proposed to solve these sub-problems jointly. The simulation results show that the proposed ISASOA can upload 10% more data than the greedy algorithm.

Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Cellular Networks

Abstract: Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment costs, which have been considered to be a promising technique in the next-generation wireless network. Due to the non-convex and combinatorial characteristics, it is challenging to obtain an optimal strategy for the joint user association and resource allocation issue. In this paper, a reinforcement learning (RL) approach is proposed to achieve the maximum long-term overall network utility while guaranteeing the quality of service requirements of user equipments (UEs) in the downlink of heterogeneous cellular networks. A distributed optimization method based on multi-agent RL is developed. Moreover, to solve the computationally expensive problem with the large action space, multi-agent deep RL method is proposed. Specifically, the state, action and reward function are defined for UEs, and dueling double deep Q-network (D3QN) strategy is introduced to obtain the nearly optimal policy. Through message passing, the distributed UEs can obtain the global state space with a small communication overhead. With the double-Q strategy and dueling architecture, D3QN can rapidly converge to a subgame perfect Nash equilibrium. Simulation results demonstrate that D3QN achieves the better performance than other RL approaches in solving large-scale learning problems.

Interference Management for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach

Abstract: In this paper, an interference-aware path planning scheme for a network of cellular-connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV aims at achieving a tradeoff between maximizing energy efficiency and minimizing both wireless latency and the interference caused on the ground network along its path. The problem is cast as a dynamic game among UAVs. To solve this game, a deep reinforcement learning algorithm, based on echo state network (ESN) cells, is proposed. The introduced deep ESN architecture is trained to allow each UAV to map each observation of the network state to an action, with the goal of minimizing a sequence of time-dependent utility functions. Each UAV uses the ESN to learn its optimal path, transmission power, and cell association vector at different locations along its path. The proposed algorithm is shown to reach a subgame perfect Nash equilibrium upon convergence. Moreover, an upper bound and a lower bound for the altitude of the UAVs are derived thus reducing the computational complexity of the proposed algorithm. The simulation results show that the proposed scheme achieves better wireless latency per UAV and rate per ground user (UE) while requiring a number of steps that are comparable to a heuristic baseline that considers moving via the shortest distance toward the corresponding destinations. The results also show that the optimal altitude of the UAVs varies based on the ground network density and the UE data rate requirements and plays a vital role in minimizing the interference level on the ground UEs as well as the wireless transmission delay of the UAV.

3D Trajectory Optimization in Rician Fading for UAV-Enabled Data Harvesting

Abstract: Dispatching unmanned aerial vehicles (UAVs) to harvest sensing-data from distributed sensors is expected to significantly improve the data collection efficiency in conventional wireless sensor networks (WSNs). In this paper, we consider a UAV-enabled WSN, where a flying UAV is employed to collect data from multiple sensor nodes (SNs). Our objective is to maximize the minimum average data collection rate from all SNs subject to a prescribed reliability constraint for each SN by jointly optimizing the UAV communication scheduling and three-dimensional (3D) trajectory. Different from the existing works that assume the simplified line-of-sight (LoS) UAV-ground channels, we consider the more practically accurate angle-dependent Rician fading channels between the UAV and SNs with the Rician factors determined by the corresponding UAV-SN elevation angles. However, the formulated optimization problem is intractable due to the lack of a closed-form expression for a key parameter termed effective fading power that characterizes the achievable rate given the reliability requirement in terms of outage probability. To tackle this difficulty, we first approximate the parameter by a logistic (“S” shape) function with respect to the 3D UAV trajectory by using the data regression method. Then, the original problem is reformulated to an approximate form, which, however, is still challenging to solve due to its non-convexity. As such, we further propose an efficient algorithm to derive its suboptimal solution by using the block coordinate descent technique, which iteratively optimizes the communication scheduling, the UAV’s horizontal trajectory, and its vertical trajectory. The latter two subproblems are shown to be non-convex, while locally optimal solutions are obtained for them by using the successive convex approximation technique. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithm and draw new insights on the 3D UAV trajectory under the Rician fading as compared to conventional LoS channel models.

Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks

Abstract: In this paper, we consider the sum power minimization problem via jointly optimizing user association, power control, computation capacity allocation, and location planning in a mobile edge computing (MEC) network with multiple unmanned aerial vehicles (UAVs). To solve the nonconvex problem, we propose a low-complexity algorithm with solving three subproblems iteratively. For the user association subproblem, the compressive sensing-based algorithm is accordingly proposed. For the computation capacity allocation subproblem, the optimal solution is obtained in closed form. For the location planning subproblem, the optimal solution is effectively obtained via one-dimensional search method. To obtain a feasible solution for this iterative algorithm, a fuzzy c-means clustering-based algorithm is proposed. The numerical results show that the proposed algorithm achieves better performance than the conventional approaches.

UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization

Abstract: In this paper, we study an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture, in which a UAV roaming around the area may serve as a computing server to help user equipment (UEs) compute their tasks or act as a relay for further offloading their computation tasks to the access point (AP). We aim to minimize the weighted sum energy consumption of the UAV and UEs subject to the task constraints, the information-causality constraints, the bandwidth allocation constraints and the UAV’s trajectory constraints. The required optimization is nonconvex, and an alternating optimization algorithm is proposed to jointly optimize the computation resource scheduling, bandwidth allocation, and the UAV’s trajectory in an iterative fashion. The numerical results demonstrate that significant performance gain is obtained over conventional methods. Also, the advantages of the proposed algorithm are more prominent when handling computation-intensive latency-critical tasks.

Distributed Resource Allocation in Blockchain-Based Video Streaming Systems With Mobile Edge Computing

Abstract: Blockchain-based video streaming systems aim to build decentralized peer-to-peer networks with flexible monetization mechanisms for video streaming services. On these blockchain-based platforms, video transcoding, which is computationally intensive and time-consuming, is still a major challenge. Meanwhile, the block size of the underlying blockchain has significant impacts on the system performance. Therefore, this paper proposes a novel blockchain-based framework with an adaptive block size for video streaming with mobile edge computing (MEC). First, we design an incentive mechanism to facilitate collaboration among content creators, video transcoders, and consumers. In addition, we present a block size adaptation scheme for blockchain-based video streaming. Moreover, we consider two offloading modes, i.e., offloading to the nearby MEC nodes or a group of device-to-device (D2D) users, to avoid the overload of MEC nodes. Then, we formulate the issues of resource allocation, scheduling of offloading, and adaptive block size as an optimization problem. We employ a low-complexity alternating direction method of the multipliers-based algorithm to solve the problem in a distributed fashion. Simulation results are presented to show the effectiveness of the proposed scheme.

Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin

Abstract: In this paper, we consider a mobile edge computing system with both ultra-reliable and low-latency communications services and delay tolerant services. We aim to minimize the normalized energy consumption, defined as the energy consumption per bit, by optimizing user association, resource allocation, and offloading probabilities subject to the quality-of-service requirements. The user association is managed by the mobility management entity (MME), while resource allocation and offloading probabilities are determined by each access point (AP). We propose a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server. From the pre-trained deep neural network (DNN), the MME can obtain user association scheme in a real-time manner. Considering that the real networks are not static, the digital twin monitors the variation of real networks and updates the DNN accordingly. For a given user association scheme, we propose an optimization algorithm to find the optimal resource allocation and offloading probabilities at each AP. The simulation results show that our method can achieve lower normalized energy consumption with less computation complexity compared with an existing method and approach to the performance of the global optimal solution.

Optimizing Resource Allocation in the Short Blocklength Regime for Ultra-Reliable and Low-Latency Communications

Abstract: In this paper, we aim to find the global optimal resource allocation for ultra-reliable and low-latency communications (URLLC), where the blocklength of channel codes is short. The achievable rate in the short blocklength regime is neither convex nor concave in bandwidth and transmit power. Thus, a non-convex constraint is inevitable in optimizing resource allocation for URLLC. We first consider a general resource allocation problem with constraints on the transmission delay and decoding error probability, and prove that a global optimal solution can be found in a convex subset of the original feasible region. Then, we illustrate how to find the global optimal solution for an example problem, where the energy efficiency (EE) is maximized by optimizing antenna configuration, bandwidth allocation, and power control under the latency and reliability constraints. To improve the battery life of devices and EE of communication systems, both uplink and downlink resources are optimized. The simulation and numerical results validate the analysis and show that the circuit power is dominated by the total power consumption when the average inter-arrival time between packets is much larger than the required delay bound. Therefore, optimizing antenna configuration and bandwidth allocation without power control leads to minor EE loss.

Cellular Connectivity for UAVs: Network Modeling, Performance Analysis, and Design Guidelines

Abstract: The growing use of aerial user equipments (UEs) in various applications requires ubiquitous and reliable connectivity for safe control and data exchange between these devices and ground stations. Key questions that need to be addressed when planning the deployment of aerial UEs are whether the cellular network is a suitable candidate for enabling such connectivity and how the inclusion of aerial UEs might impact the overall network efficiency. This paper provides an in-depth analysis of user and network-level performance of a cellular network that serves both unmanned aerial vehicles (UAVs) and ground users in the downlink. Our results show that the favorable propagation conditions that UAVs enjoy due to their height often backfire on them, as the increased load-dependent co-channel interference received from neighboring ground base stations (BSs) is not compensated by the improved signal strength. When compared with a ground user in an urban area, our analysis shows that a UAV flying at 100 m can experience a throughput decrease of a factor 10 and a coverage drop from 76% to 30%. Motivated by these findings, we develop UAV and network-based solutions to enable an adequate integration of UAVs into cellular networks. In particular, we show that an optimal tilting of the UAV antenna can increase the coverage from 23% to 89% and throughput from 3.5 to 5.8 b/s/Hz, outperforming ground UEs. Furthermore, our findings reveal that depending on the UAV altitude and its antenna configuration, the aerial user performance can scale with respect to the network density better than that of a ground user. Finally, our results show that network densification and the use of microcells limit the UAV performance. Although UAV usage has the potential to increase the area spectral efficiency (ASE) of cellular networks with a moderate number of cells, they might hamper the development of future ultradense networks.

Average Age of Information With Hybrid ARQ Under a Resource Constraint

Abstract: Scheduling the transmission of status updates over an error-prone communication channel is studied in order to minimize the long-term average age of information at the destination under a constraint on the average number of transmissions at the source node. After each transmission, the source receives an instantaneous ACK/NACK feedback, and decides on the next update without prior knowledge on the success of future transmissions. The optimal scheduling policy is first studied under different feedback mechanisms when the channel statistics are known; in particular, the standard automatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols are considered. The structural results are derived for the optimal policy under HARQ, while the optimal policy is determined analytically for ARQ. For the case of unknown environments, an average-cost reinforcement learning algorithm is proposed that learns the system parameters and the transmission policy in real time. The effectiveness of the proposed methods is verified through the numerical results.

Completion Time Minimization for Multi-UAV-Enabled Data Collection

Abstract: Energy consumption is one of the important design aspect for data collection in wireless sensor networks (WSNs). This paper studies data collection from a set of sensor nodes (SNs) in WSNs enabled by multiple unmanned aerial vehicles (UAVs). We aim to minimize the maximum mission completion time among all UAVs by jointly optimizing the UAV trajectory, as well as the wake-up scheduling and association for SNs, while ensuring that each SN can successfully upload the targeting amount of data with a given energy budget. The formulated problem is a non-convex problem which is difficult to be solved directly. To tackle this problem, we first propose a simple scheme that each UAV only collects data while hovering, termed as hovering mode (Hmode) . For this mode, in order to find the optimized hovering locations for each SN and the serving order among all locations, we propose an efficient algorithm by leveraging the min–max multiple Traveling Salesman Problem (min–max m-TSP) and convex optimization techniques. Furthermore, we propose the more general scheme that enables continuous data collection even while flying, termed as flying mode (Fmode) . By leveraging bisection method and time discretization technique, the original problem is transformed into a discretized equivalent with a finite number of optimization variables, based on which a Karush–Kuhn–Tucker (KKT) solution is obtained by applying the successive convex approximation (SCA) technique. The simulation results show that the proposed multi-UAV enabled data collection with joint trajectory and communication design achieves significant performance gains over the benchmark schemes.

On the Uplink Max–Min SINR of Cell-Free Massive MIMO Systems

Abstract: A cell-free massive multiple-input multiple-output system is considered using a max-min approach to maximize the minimum user rate with per-user power constraints. First, an approximated uplink user rate is derived based on channel statistics. Then, the original max-min signal-to-interference-plus-noise ratio problem is formulated for the optimization of receiver filter coefficients at a central processing unit and user power allocation. To solve this max-min non-convex problem, we decouple the original problem into two sub-problems, namely, receiver filter coefficient design and power allocation. The receiver filter coefficient design is formulated as a generalized Eigenvalue problem, whereas the geometric programming (GP) is used to solve the user power allocation problem. Based on these two sub-problems, an iterative algorithm is proposed, in which both problems are alternately solved while one of the design variables is fixed. This iterative algorithm obtains a globally optimum solution, whose optimality is proved through establishing an uplink-downlink duality. Moreover, we present a novel sub-optimal scheme which provides a GP formulation to efficiently and globally maximize the minimum uplink user rate. The numerical results demonstrate that the proposed scheme substantially outperforms the existing schemes in the literature.

D2D Communications Meet Mobile Edge Computing for Enhanced Computation Capacity in Cellular Networks

Abstract: The future 5G wireless networks aim to support high-rate data communications and high-speed mobile computing. To achieve this goal, the mobile edge computing (MEC) and device-to-device (D2D) communications have been recently developed, both of which take advantage of the proximity for better performance. In this paper, we integrate the D2D communications with MEC to further improve the computation capacity of the cellular networks, where the task of each device can be offloaded to an edge node and a nearby D2D device. We aim to maximize the number of devices supported by the cellular networks with the constraints of both communication and computation resources. The optimization problem is formulated as a mixed integer non-linear problem, which is not easy to solve in general. To tackle it, we decouple it into two subproblems. The first one minimizes the required edge computation resource for a given D2D pair, while the second one maximizes the number of supported devices via optimal D2D pairing. We prove that the optimal solutions to the two subproblems compose the optimal solution to the original problem. Then, the optimal algorithm to the original problem is developed by solving two subproblems, and some insightful results, such as the optimal transmit power allocation and the task offloading strategy, are also highlighted. Our proposal is finally tested by extensive numerical simulation results, which demonstrate that combining D2D communications with MEC can significantly enhance the computation capacity of the system.

Adaptive Deployment for UAV-Aided Communication Networks

Abstract: Unmanned aerial vehicle (UAV) as an aerial base station is a promising technology to rapidly provide wireless connectivity to ground users. Given UAV’s agility and mobility, a key question is how to adapt UAV deployment to the best cater to instantaneous wireless traffic in a territory. In this paper, we propose an adaptive deployment scheme for a UAV-aided communication network, where the UAV adapts its displacement direction and distance to serve randomly moving users’ instantaneous traffic in the target cell. In our adaptive scheme, the UAV does not need to learn users’ exact locations in real time, but chooses its displacement direction based on a simple majority rule by flying to the spatial sector with the greatest number of users in the cell. To balance the service qualities of the users in different sectors, we further optimize the UAV’s displacement distance in the chosen sector to maximize the average throughput and the successful transmission probability, respectively. We prove that the optimal displacement distance for average throughput maximization decreases with the user density: the UAV moves to the center of the chosen sector when the user density is small and the UAV displacement becomes mild when the user density is large. In contrast, the optimal displacement distance for success probability maximization does not necessarily decrease with the user density and further depends on the target signal-to-noise ratio (SNR) threshold. The extensive simulations show that the proposed adaptive deployment scheme outperforms the traditional non-adaptive scheme, especially when the user density is not large.

Liquid State Machine Learning for Resource and Cache Management in LTE-U Unmanned Aerial Vehicle (UAV) Networks

Abstract: In this paper, the problem of joint caching and resource allocation is investigated for a network of cache-enabled unmanned aerial vehicles (UAVs) that service wireless ground users over the LTE licensed and unlicensed bands. The considered model focuses on users that can access both licensed and unlicensed bands while receiving contents from either the cache units at the UAVs directly or via content server-UAV-user links. This problem is formulated as an optimization problem, which jointly incorporates user association, spectrum allocation, and content caching. To solve this problem, a distributed algorithm based on the machine learning framework of liquid state machine (LSM) is proposed. Using the proposed LSM algorithm, the cloud can predict the users’ content request distribution while having only limited information on the network’s and users’ states. The proposed algorithm also enables the UAVs to autonomously choose the optimal resource allocation strategies that maximize the number of users with stable queues depending on the network states. Based on the users’ association and content request distributions, the optimal contents that need to be cached at UAVs and the optimal resource allocation are derived. Simulation results using real datasets show that the proposed approach yields up to 17.8% and 57.1% gains, respectively, in terms of the number of users that have stable queues compared with two baseline algorithms: Q-learning with cache and Q-learning without cache. The results also show that the LSM significantly improves the convergence time of up to 20% compared with conventional learning algorithms such as Q-learning.

Cellular-Connected UAV: Uplink Association, Power Control and Interference Coordination

Abstract: The line-of-sight (LoS) air-to-ground channel brings both opportunities and challenges in cellular-connected unmanned aerial vehicle (UAV) communications. On one hand, the LoS channels make more cellular base stations (BSs) visible to a UAV as compared to the ground users, which leads to a higher macro-diversity gain for UAV-BS communications. On the other hand, they also render the UAV to impose/suffer more severe uplink/downlink interference to/from the BSs, thus requiring more sophisticated inter-cell interference coordination (ICIC) techniques with more BSs involved. In this paper, we consider the uplink transmission from a UAV to cellular BSs, under spectrum sharing with the existing ground users. To investigate the optimal ICIC design and air-ground performance trade-off, we maximize the weighted sum-rate of the UAV and existing ground users by jointly optimizing the UAV’s uplink cell associations and power allocations over multiple resource blocks. However, this problem is non-convex and difficult to be solved optimally. We first propose a centralized ICIC design to obtain a locally optimal solution based on the successive convex approximation (SCA) method. As the centralized ICIC requires global information of the network and substantial information exchange among an excessively large number of BSs, we further propose a decentralized ICIC scheme of significantly lower complexity and signaling overhead for implementation, by dividing the cellular BSs into small-size clusters and exploiting the LoS macro-diversity for exchanging information between the UAV and cluster-head BSs only. Numerical results show that the proposed centralized and decentralized ICIC schemes both achieve a near-optimal performance, and draw important design insights based on practical system setups.

On the Diversity of Uncoded OTFS Modulation in Doubly-Dispersive Channels

Abstract: Orthogonal time-frequency space (OTFS) is a two-dimensional (2D) modulation technique designed in the delay-Doppler domain. A key premise behind OTFS is the transformation of a time-varying multipath channel into an almost non-fading 2D channel in the delay-Doppler domain such that all symbols in a transmission frame experience the same channel gain. It has been suggested in the recent literature that the OTFS can extract full diversity in the delay-Doppler domain, where full diversity refers to the number of multipath components separable in either the delay or Doppler dimension, but without formal analysis. In this paper, we present a formal analysis of the diversity achieved by the OTFS modulation along with supporting simulations. Specifically, we prove that the asymptotic diversity order of the OTFS (as SNR $\rightarrow \infty $ ) is one. However, in the finite SNR regime, the potential for a higher order diversity is witnessed before the diversity one regime takes over. Also, the diversity one regime is found to start at lower BER values for increased frame sizes. We also propose a phase rotation scheme for the OTFS using transcendental numbers and show that the OTFS, with this proposed scheme, extracts full diversity in the delay-Doppler domain.

Multi-Agent Reinforcement Learning for Efficient Content Caching in Mobile D2D Networks

Abstract: To address the increase of multimedia traffic dominated by streaming videos, user equipment (UE) can collaboratively cache and share contents to alleviate the burden of base stations. Prior work on device-to-device (D2D) caching policies assumes perfect knowledge of the content popularity distribution. Since the content popularity distribution is usually unavailable in advance, a machine learning-based caching strategy that exploits the knowledge of content demand history would be highly promising. Thus, we design D2D caching strategies using multi-agent reinforcement learning in this paper. Specifically, we model the D2D caching problem as a multi-agent multi-armed bandit problem and use Q-learning to learn how to coordinate the caching decisions. The UEs can be independent learners (ILs) if they learn the Q-values of their own actions, and joint action learners (JALs) if they learn the Q-values of their own actions in conjunction with those of the other UEs. As the action space is very vast leading to high computational complexity, a modified combinatorial upper confidence bound algorithm is proposed to reduce the action space for both IL and JAL. The simulation results show that the proposed JAL-based caching scheme outperforms the IL-based caching scheme and other popular caching schemes in terms of average downloading latency and cache hit rate.

A Belief Propagation Algorithm for Multipath-Based SLAM

Abstract: We present a simultaneous localization and mapping (SLAM) algorithm that is based on radio signals and the association of specular multipath components (MPCs) with geometric features. Especially in indoor scenarios, robust localization from radio signals is challenging due to diffuse multipath propagation, unknown MPC-feature association, and limited visibility of features. In our approach, specular reflections at flat surfaces are described in terms of virtual anchors (VAs) that are mirror images of the physical anchors (PAs). The positions of these VAs and possibly also of the PAs are unknown. We develop a Bayesian model of the SLAM problem and represent it by a factor graph, which enables the use of belief propagation (BP) for efficient marginalization of the joint posterior distribution. The resulting BP-based SLAM algorithm detects the VAs associated with the PAs and estimates jointly the time-varying position of the mobile agent and the positions of the VAs and possibly also of the PAs, thereby leveraging the MPCs in the radio signal for improved accuracy and robustness of agent localization. The algorithm has a low computational complexity and scales well in all relevant system parameters. Experimental results using both synthetic measurements and real ultra-wideband radio signals demonstrate the excellent performance of the algorithm in challenging indoor environments.

Auction-Based Time Scheduling for Backscatter-Aided RF-Powered Cognitive Radio Networks

Abstract: This paper investigates the time scheduling for a backscatter-aided radio-frequency-powered cognitive radio network, where multiple secondary transmitters transmit data to the same secondary gateway in the backscatter mode and the harvest-then-transmit mode. With many secondary transmitters connected to the network, the total transmission demand of the secondary transmitters may frequently exceed the transmission capacity of the secondary network. As such, the secondary gateway is more likely to assign the time resource, i.e., the backscattering time in the backscatter mode and the transmission time in the harvest-then-transmit mode, to the secondary transmitters with higher transmission valuations. Therefore, according to a variety of demand requirements from secondary transmitters, we design two auction-based time scheduling mechanisms for the time resource assignment. In the auctions, the secondary gateway acts as the seller as well as the auctioneer, and the secondary transmitters act as the buyers to bid for the time resource. We design the winner determination, the time scheduling, and the pricing schemes for both the proposed auction-based mechanisms. Furthermore, the economic properties, such as individual rationality and truthfulness, and the computational efficiency of our proposed mechanisms are analytically evaluated. The simulation results demonstrate the effectiveness of our proposed mechanisms.

Resource Allocation for Wireless-Powered IoT Networks With Short Packet Communication

Abstract: Internet-of-Things (IoT) is a promising technology to connect massive machines and devices in the future communication networks. In this paper, we study a wireless-powered IoT network (WPIN) with short packet communication (SPC), in which a hybrid access point (HAP) first transmits power to the IoT devices wirelessly, then the devices in turn transmit their short data packets achieved by finite blocklength codes to the HAP using the harvested energy. Different from the long packet communication in conventional wireless network, SPC suffers from transmission rate degradation and a significant packet error rate. Thus, conventional resource allocation in the existing literature based on Shannon capacity achieved by the infinite blocklength codes is no longer optimal. In this paper, to enhance the transmission efficiency and reliability, we first define effective-throughput and effective-amount-of-information as the performance metrics to balance the transmission rate and the packet error rate, and then jointly optimize the transmission time and packet error rate of each user to maximize the total effective-throughput or minimize the total transmission time subject to the users’ individual effective-amount-of-information requirements. To overcome the non-convexity of the formulated problems, we develop efficient algorithms to find high-quality suboptimal solutions for them. The simulation results show that the proposed algorithms can achieve similar performances as that of the optimal solution via exhaustive search, and outperform the benchmark schemes.

Secure Short-Packet Communications for Mission-Critical IoT Applications

Abstract: In applications of the Internet of Things (IoT), the use of short packets is expected to meet the stringent latency requirement in ultra-reliable low-latency communications; however, the incurred security issues and the impact of finite blocklength coding on the physical-layer security are not well understood. This paper investigates the performance of secure short-packet communications in a mission-critical IoT system with an external multi-antenna eavesdropper. An analytical framework is proposed to approximate the average achievable secrecy throughput of the system with finite blocklength coding. To gain more insight, a simple case with a single-antenna access point (AP) is considered first, in which the secrecy throughput is approximated in a closed form. Based on that result, the optimal blocklengths to maximize the secrecy throughput with and without the reliability and latency constraints, respectively, are derived. For the case with a multi-antenna AP, following the proposed analytical framework, closed-form approximations for the secrecy throughput are obtained under both beamforming and artificial-noise-aided transmission schemes. The numerical results verify the accuracy of the proposed approximations and illustrate the impact of the system parameters on the tradeoff between transmission latency and reliability under a secrecy constraint.

Coverage and Rate Analysis for Vertical Heterogeneous Networks (VHetNets)

Abstract: In this paper, we leverage concepts from stochastic geometry to investigate the downlink performance of a vertical heterogeneous network (VHetNet) comprising aerial base stations (ABSs) and terrestrial base stations (TBSs). We model the ABSs as a 2D Poisson point process (PPP) deployed at a particular altitude while the TBSs are modelled as a 2D PPP deployed on the ground. The proposed analytical framework adopts an appropriate air-to-ground (A2G) channel model that incorporates line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. We begin the main technical part of the analysis by deriving analytical expressions for the distribution of the distances between a typical user and the closest LoS ABS, NLoS ABS, and TBS. After that, we derive expressions for the probabilities that a typical user is associated with a NLoS ABS, LoS ABS, or TBS. Under the assumption that A2G and terrestrial channels experience Nakagami- $m$ fading with different $m$ parameters, we derive an expression for the Laplace transform of interference power. Furthermore, we derive exact and approximate analytical expressions for the coverage probability and achievable rate. We show that these approximations match the simulations with negligible errors for small SINR thresholds and $m$ parameters of Nakagami- $m$ fading.