Showing papers on "Telecommunications link published in 2020"
TL;DR: In this paper, the authors proposed a three-phase pilot-based channel estimation framework for IRS-assisted uplink multiuser communications, in which the user-BS direct channels and the users-IRS-BS reflected channels of a typical user were estimated in Phase I and Phase II, respectively, while the users reflected channels were estimated with low overhead in Phase III via leveraging their strong correlation with those of the typical user under the case without receiver noise at the BS.
Abstract: In intelligent reflecting surface (IRS) assisted communication systems, the acquisition of channel state information is a crucial impediment for achieving the beamforming gain of IRS because of the considerable overhead required for channel estimation Specifically, under the current beamforming design for IRS-assisted communications, in total $KMN+KM$ channel coefficients should be estimated, where $K$ , $N$ and $M$ denote the numbers of users, IRS reflecting elements, and antennas at the base station (BS), respectively For the first time in the literature, this paper points out that despite the vast number of channel coefficients that should be estimated, significant redundancy exists in the user-IRS-BS reflected channels of different users arising from the fact that each IRS element reflects the signals from all the users to the BS via the same channel To utilize this redundancy for reducing the channel estimation time, we propose a novel three-phase pilot-based channel estimation framework for IRS-assisted uplink multiuser communications, in which the user-BS direct channels and the user-IRS-BS reflected channels of a typical user are estimated in Phase I and Phase II, respectively, while the user-IRS-BS reflected channels of the other users are estimated with low overhead in Phase III via leveraging their strong correlation with those of the typical user Under this framework, we analytically prove that a time duration consisting of $K+N+\max (K-1,\lceil (K-1)N/M \rceil)$ pilot symbols is sufficient for perfectly recovering all the $KMN+KM$ channel coefficients under the case without receiver noise at the BS Further, under the case with receiver noise, the user pilot sequences, IRS reflecting coefficients, and BS linear minimum mean-squared error channel estimators are characterized in closed-form
571 citations
TL;DR: An adaptive access scheme is proposed, which adapts the access latency to guarantee reliable massive access for practical systems with unknown channel sparsity level and the state evolution of the proposed GMMV-AMP algorithm is derived to predict its performance.
Abstract: This paper considers massive access in massive multiple-input multiple-output (MIMO) systems and proposes an adaptive active user detection and channel estimation scheme based on compressive sensing. By exploiting the sporadic traffic of massive connected user equipments and the virtual angular domain sparsity of massive MIMO channels, the proposed scheme can support massive access with dramatically reduced access latency. Specifically, we design non-orthogonal pseudo-random pilots for uplink broadband massive access, and formulate the active user detection and channel estimation as a generalized multiple measurement vector compressive sensing problem. Furthermore, by leveraging the structured sparsity of the uplink channel matrix, we propose an efficient generalized multiple measurement vector approximate message passing (GMMV-AMP) algorithm to realize joint active user detection and channel estimation based on a spatial domain or an angular domain channel model. To jointly exploit the channel sparsity present in both the spatial and the angular domains for enhanced performance, a Turbo-GMMV-AMP algorithm is developed for detecting the active users and estimating their channels in an alternating manner. Finally, an adaptive access scheme is proposed, which adapts the access latency to guarantee reliable massive access for practical systems with unknown channel sparsity level. Additionally, the state evolution of the proposed GMMV-AMP algorithm is derived to predict its performance. Simulation results demonstrate the superiority of the proposed active user detection and channel estimation schemes compared to several baseline schemes.
262 citations
TL;DR: A deep denoising neural network assisted compressive channel estimation for mmWave IRS systems to reduce the training overhead and demonstrate the superiority of the proposed solution over state-of-the-art solutions.
Abstract: Integrating large intelligent reflecting surfaces (IRS) into millimeter-wave (mmWave) massive multi-input-multi-ouput (MIMO) has been a promising approach for improved coverage and throughput. Most existing work assumes the ideal channel estimation, which can be challenging due to the high-dimensional cascaded MIMO channels and passive reflecting elements. Therefore, this paper proposes a deep denoising neural network assisted compressive channel estimation for mmWave IRS systems to reduce the training overhead. Specifically, we first introduce a hybrid passive/active IRS architecture, where very few receive chains are employed to estimate the uplink user-to-IRS channels. At the channel training stage, only a small proportion of elements will be successively activated to sound the partial channels. Moreover, the complete channel matrix can be reconstructed from the limited measurements based on compressive sensing, whereby the common sparsity of angular domain mmWave MIMO channels among different subcarriers is leveraged for improved accuracy. Besides, a complex-valued denoising convolution neural network (CV-DnCNN) is further proposed for enhanced performance. Simulation results demonstrate the superiority of the proposed solution over state-of-the-art solutions.
214 citations
TL;DR: This work proposes a deep learning based fast beamforming design method which separates the problem into power allocation and virtual uplink beamforming (VUB) design and designs a heuristic solution structure of the downlink beamforming through the virtual equivalent uplink channel based on optimum MMSE receiver.
Abstract: Beamforming is considered as one of the most important techniques for designing advanced multiple-input and multiple-output (MIMO) systems. Among existing design criterions, sum rate maximization (SRM) under a total power constraint is a challenge due to its nonconvexity. Existing techniques for the SRM problem only obtain local optimal solutions but require huge amount of computation due to their complex matrix operations and iterations. Unlike these conventional methods, we propose a deep learning based fast beamforming design method without complex operations and iterations. Specifically, we first derive a heuristic solution structure of the downlink beamforming through the virtual equivalent uplink channel based on optimum MMSE receiver which separates the problem into power allocation and virtual uplink beamforming (VUB) design. Next, beamforming prediction network (BPNet) is designed to perform the joint optimization of power allocation and VUB design. Moreover, the BPNet is trained offline using two-step training strategy. Simulation results demonstrate that our proposed method is fast while obtains the comparable performance to the state-of-the-art method.
211 citations
Posted Content•
TL;DR: In this article, a channel estimation framework based on the PARAllel FACtor (PARAFAC) decomposition is proposed to unfold the resulting cascaded channel model for the downlink of a RIS-empowered multi-user MISO downlink communication systems.
Abstract: Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low-power configuration, which has increased potential in enabling massive connectivity and low-latency communications. Accurate and low-overhead channel estimation in RIS-based systems is one of the most critical challenges due to the usually large number of RIS unit elements and their distinctive hardware constraints. In this paper, we focus on the downlink of a RIS-empowered multi-user Multiple Input Single Output (MISO) downlink communication systems and propose a channel estimation framework based on the PARAllel FACtor (PARAFAC) decomposition to unfold the resulting cascaded channel model. We present two iterative estimation algorithms for the channels between the base station and RIS, as well as the channels between RIS and users. One is based on alternating least squares (ALS), while the other uses vector approximate message passing to iteratively reconstruct two unknown channels from the estimated vectors. To theoretically assess the performance of the ALS-based algorithm, we derived its estimation Cramer-Rao Bound (CRB). We also discuss the achievable sum-rate computation with estimated channels and different precoding schemes for the base station. Our extensive simulation results show that our algorithms outperform benchmark schemes and that the ALS technique achieve the CRB. It is also demonstrated that the sum rate using the estimated channels reached that of perfect channel estimation under various settings, thus, verifying the effectiveness and robustness of the proposed estimation algorithms.
198 citations
TL;DR: Results show that the UC approach, which requires smaller backhaul overhead and is more scalable that the CF deployment, also achieves generally better performance than the CF approach for the vast majority of the users, especially on the uplink.
Abstract: Recently, the so-called cell-free (CF) massive multiple-input multiple-output (MIMO) architecture has been introduced, wherein a very large number of distributed access points (APs) simultaneously and jointly serve a much smaller number of mobile stations (MSs). The paper extends the CF approach to the case in which both the APs and the MSs are equipped with multiple antennas, proposing a beamfoming scheme that, relying on the zero-forcing strategy, does not require channel estimation at the MSs. We contrast the originally proposed formulation of CF massive MIMO with a user-centric (UC) approach wherein each MS is served only by a limited number of APs. Exploiting the framework of successive lower-bound maximization, the paper also proposes and analyzes power allocation strategies aimed at either sum-rate maximization or minimum-rate maximization, both for the uplink and downlink. Results show that the UC approach, which requires smaller backhaul overhead and is more scalable that the CF deployment, also achieves generally better performance than the CF approach for the vast majority of the users, especially on the uplink.
192 citations
TL;DR: In this paper, the authors proposed a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploited statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI at the transmitter.
Abstract: Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks, in particular 5G and beyond networks, to provide global wireless access with enhanced data rates. Massive multiple-input multiple-output (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI (iCSI) at the transmitter. We first establish the massive MIMO channel model for LEO satellite communications and simplify the transmission designs via performing Doppler and delay compensations at user terminals (UTs). Then, we develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL) receiver in closed-form, aiming to maximize the average signal-to-leakage-plus-noise ratio (ASLNR) and the average signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some channel condition. Motivated by this, we propose a space angle based user grouping (SAUG) algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. The proposed algorithm is asymptotically optimal in the sense that the lower and upper bounds of the achievable rate coincide when the number of satellite antennas or UT groups is sufficiently large. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems. Notably, the proposed sCSI based precoder and receiver achieve the similar performance with the iCSI based ones that are often infeasible in practice.
147 citations
12 Jun 2020
TL;DR: Analysis of a cellular network deployment where UAV-to-UAV (U2U) transmit-receive pairs share the same spectrum with the uplink of cellular ground users (GUEs) concludes that adopting an overlay spectrum sharing seems the most suitable approach for maintaining a minimum guaranteed rate for UAVs and a high GUE UL performance.
Abstract: We consider a cellular network deployment where UAV-to-UAV (U2U) transmit-receive pairs share the same spectrum with the uplink (UL) of cellular ground users (GUEs). For this setup, we focus on analyzing and comparing the performance of two spectrum sharing mechanisms: (i) underlay, where the same time-frequency resources may be accessed by both UAVs and GUEs, resulting in mutual interference, and (ii) overlay, where the available resources are divided into orthogonal portions for U2U and GUE communications. We evaluate the coverage probability and rate of both link types and their interplay to identify the best spectrum sharing strategy. We do so through an analytical framework that embraces realistic height-dependent channel models, antenna patterns, and practical power control mechanisms. For the underlay, we find that although the presence of U2U direct communications may worsen the uplink performance of GUEs, such effect is limited as base stations receive the power-constrained UAV signals through their antenna sidelobes. In spite of this, our results lead us to conclude that in urban scenarios with a large number of UAV pairs, adopting an overlay spectrum sharing seems the most suitable approach for maintaining a minimum guaranteed rate for UAVs and a high GUE UL performance.
130 citations
TL;DR: This paper provides essential knowledge of 3GPP NR sidelink transmissions, including the physical layer structure, resource allocation mechanisms, resource sensing and selection procedures, synchronization, and quality-of-service (QoS) management and performance evaluation to assess the gains brought from the new control channel design.
Abstract: Featuring direct communications between two user equipments (UEs) without signal relay through a base station, 3GPP sidelink transmissions have manifested their crucial roles in the Long-Term Evolution (LTE) Advanced (LTE-A) for public safety and vehicle-to-everything (V2X) services. With this successful development in LTE-A, the evolution of sidelink transmissions continues in 3GPP New Radio (NR), which renders sidelink an inevitable component as well as downlink and uplink. Targeting at offering low latency, high reliability and high throughout V2X services for advanced driving use cases, a number of new sidelink functions not provided in the LTE-A are supported in NR, including the feedback channel, grant-free access, enhanced channel sensing procedure, and new control channel design. To fully comprehend these new functions, this paper therefore provides essential knowledge of 3GPP NR sidelink transmissions, including the physical layer structure, resource allocation mechanisms, resource sensing and selection procedures, synchronization, and quality-of-service (QoS) management. Furthermore, this paper also provides performance evaluation to assess the gains brought from the new control channel design. As NR sidelink transmissions have been regarded as a foundation to provide advanced services other than V2X in future releases (e.g., advanced relay), potential enhancements are also discussed to serve the urgent demand in corresponding normative works.
109 citations
TL;DR: A massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI at the transmitter is proposed.
Abstract: Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks, in particular 5G and beyond networks, to provide global wireless access with enhanced data rates. Massive MIMO techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI (iCSI) at the transmitter. We first establish the massive MIMO channel model for LEO satellite communications and simplify the transmission designs via performing Doppler and delay compensations at user terminals (UTs). Then, we develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL) receiver in closed-form, aiming to maximize the average signal-to-leakage-plus-noise ratio (ASLNR) and the average signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some channel condition. Motivated by this, we propose a space angle based user grouping (SAUG) algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. The proposed algorithm is asymptotically optimal in the sense that the lower and upper bounds of the achievable rate coincide when the number of satellite antennas or UT groups is sufficiently large. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems. Notably, the proposed sCSI based precoder and receiver achieve the similar performance with the iCSI based ones that are often infeasible in practice.
106 citations
TL;DR: This paper investigates the channel model in the high mobility and heterogeneous network and proposed a novel deep reinforcement learning based intelligent TDD configuration algorithm to dynamically allocate radio resources in an online manner and achieves significant network performance improvement in terms of both network throughput and packet loss rate.
Abstract: Recently, the 5G is widely deployed for supporting communications of high mobility nodes including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the main components for constructing the wireless heterogeneous network (HetNet). To further improve the radio utilization, the Time Division Duplex (TDD) is considered to be the potential full-duplex communication technology in the high mobility 5G network. However, the high mobility of users leads to the high dynamic network traffic and unpredicted link state change. A new method to predict the dynamic traffic and channel condition and schedule the TDD configuration in real-time is essential for the high mobility environment. In this paper, we investigate the channel model in the high mobility and heterogeneous network and proposed a novel deep reinforcement learning based intelligent TDD configuration algorithm to dynamically allocate radio resources in an online manner. In the proposal, the deep neural network is employed to extract the features of the complex network information, and the dynamic Q-value iteration based reinforcement learning with experience replay memory mechanism is proposed to adaptively change TDD Up/Down-link ratio by evaluated rewards. The simulation results show that the proposal achieves significant network performance improvement in terms of both network throughput and packet loss rate, comparing with conventional TDD resource allocation algorithms.
TL;DR: A machine learning (ML)-based time-division duplex scheme in which channel state information (CSI) can be obtained by leveraging the temporal channel correlation and can remarkably improve the prediction quality for both low and high mobility scenarios and offer great performance gains on the per-user achievable throughput.
Abstract: To support the ever increasing number of devices in massive multiple-input multiple-output (mMIMO) systems, an excessive amount of overhead is required for conventional orthogonal pilot-based channel estimation schemes. To circumvent this fundamental constraint, we design a machine learning (ML)-based time-division duplex scheme in which channel state information (CSI) can be obtained by leveraging the temporal channel correlation. The presence of the temporal channel correlation is due to the stationarity of the propagation environment across time. The proposed ML-based predictors involve a pattern extraction implemented via a convolutional neural network, and a CSI predictor realized by an autoregressive (AR) predictor or an autoregressive network with exogenous inputs recurrent neural network. Closed-form expressions for the user uplink and downlink achievable spectral efficiency and average per-user throughput are provided for the ML-based time division duplex schemes. Our numerical results demonstrate that the proposed ML-based predictors can remarkably improve the prediction quality for both low and high mobility scenarios, and offer great performance gains on the per-user achievable throughput.
08 Jun 2020
TL;DR: In this paper, the authors focus on the downlink of a RIS-assisted multi-user MISO communication system and present a method based on the PARAllel FACtor (PARAFAC) decomposition to unfold the resulting cascaded channel model.
Abstract: Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low power configuration enabling massive connectivity and low latency communications. Channel estimation in RIS-based systems is one of the most critical challenges due to the large number of reflecting unit elements and their distinctive hardware constraints. In this paper, we focus on the downlink of a RIS-assisted multi-user Multiple Input Single Output (MISO) communication system and present a method based on the PARAllel FACtor (PARAFAC) decomposition to unfold the resulting cascaded channel model. The proposed method includes an alternating least squares algorithm to iteratively estimate the channel between the base station and RIS, as well as the channels between RIS and users. Our selective simulation results show that the proposed iterative channel estimation method outperforms a benchmark scheme using genie-aided information. We also provide insights on the impact of different RIS settings on the proposed algorithm.
07 Jun 2020
TL;DR: In this paper, the authors considered an intelligent reflecting surface (IRS)-aided single-user system where an IRS with discrete phase shifts is deployed to assist the uplink communication.
Abstract: In this paper, we consider an intelligent reflecting surface (IRS)-aided single-user system where an IRS with discrete phase shifts is deployed to assist the uplink communication. A practical transmission protocol is proposed to execute channel estimation and passive beamforming successively. To minimize the mean square error (MSE) of channel estimation, we first formulate an optimization problem for designing the IRS reflection pattern in the training phase under the constraints of unit-modulus, discrete phase, and full rank. This problem, however, is NP-hard and thus difficult to solve in general. As such, we propose a low-complexity yet efficient method to solve it sub-optimally, by constructing a near-orthogonal reflection pattern based on either discrete Fourier transform (DFT)-matrix quantization or Hadamard-matrix truncation. Based on the estimated channel, we then formulate an optimization problem to maximize the achievable rate by designing the discrete-phase passive beamforming at the IRS with the training overhead and channel estimation error taken into account. To reduce the computational complexity of exhaustive search, we further propose a low-complexity successive refinement algorithm with a properly-designed initialization to obtain a high-quality suboptimal solution. Numerical results are presented to show the significant rate improvement of our proposed IRS training reflection pattern and passive beamforming designs as compared to other benchmark schemes.
07 Jun 2020
TL;DR: A compressive sensing (CS)-based CE solution for IRS-aided mmWave massive MIMO systems is proposed, whereby the angular channel sparsity of large-scale array at mmWave is exploited for improved CE with reduced pilot overhead.
Abstract: This paper investigates the broadband channel estimation (CE) for intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) massive MIMO systems. The CE for such systems is a challenging task due to the large dimension of both the active massive MIMO at the base station (BS) and passive IRS. To address this problem, this paper proposes a compressive sensing (CS)-based CE solution for IRS-aided mmWave massive MIMO systems, whereby the angular channel sparsity of large-scale array at mmWave is exploited for improved CE with reduced pilot overhead. Specifically, we first propose a downlink pilot transmission framework. By designing the pilot signals based on the prior knowledge that the line-of-sight dominated BS-to-IRS channel is known, the high-dimensional channels for BS-to-user and IRS-to-user can be jointly estimated based on CS theory. Moreover, to efficiently estimate broadband channels, a distributed orthogonal matching pursuit algorithm is exploited, where the common sparsity shared by the channels at different subcarriers is utilized. Additionally, the redundant dictionary to combat the power leakage is also designed for the enhanced CE performance. Simulation results demonstrate the effectiveness of the proposed scheme.
TL;DR: This work forms the downlink channel prediction as a deep transfer learning (DTL) problem, and proposes the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained in the manner of classical deep learning and is fine-tuned for new environments.
Abstract: Artificial intelligence (AI) based downlink channel state information (CSI) prediction for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems has attracted growing attention recently. However, existing works focus on the downlink CSI prediction for the users under a given environment and is hard to adapt to users in new environment especially when labeled data is limited. To address this issue, we formulate the downlink channel prediction as a deep transfer learning (DTL) problem, and propose the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained in the manner of classical deep learning and is then fine-tuned for new environments. To further improve the transfer efficiency, we propose the meta-learning algorithm that trains the network by alternating inner-task and across-task updates and then adapts to a new environment with a small number of labeled data. Simulation results show that the direct-transfer algorithm achieves better performance than the deep learning algorithm, which implies that the transfer learning benefits the downlink channel prediction in new environments. Moreover, the meta-learning algorithm significantly outperforms the direct-transfer algorithm, which validates its effectiveness and superiority.
TL;DR: A framework for a novel perceptive mobile/cellular network that integrates radar sensing function into the mobile communication network is developed and a background subtraction method based on simple recursive computation is proposed, and a closed-form expression for performance characterization is provided.
Abstract: In this paper, we develop a framework for a novel perceptive mobile/cellular network that integrates radar sensing function into the mobile communication network. We propose a unified system platform that enables downlink and uplink sensing, sharing the same transmitted signals with communications. We aim to tackle the fundamental sensing parameter estimation problem in perceptive mobile networks, by addressing two key challenges associated with sophisticated mobile signals and rich multipath in mobile networks. To extract sensing parameters from orthogonal frequency division multiple access and spatial division multiple access communication signals, we propose two approaches to formulate it to problems that can be solved by compressive sensing techniques. Most sensing algorithms have limits on the number of multipath signals for their inputs. To reduce the multipath signals, as well as removing unwanted clutter signals, we propose a background subtraction method based on simple recursive computation, and provide a closed-form expression for performance characterization. The effectiveness of these methods is validated in simulations.
TL;DR: A principled definition of the random-access model is proposed, together with their information-theoretic analysis, to open the road towards unified benchmarking and performance comparison of various random- access solutions for the 5G/6G.
Abstract: We discuss the problem of designing channel access architectures for enabling fast, low-latency, grant-free, and uncoordinated uplink for densely packed wireless nodes. Specifically, we study random-access codes, previously introduced for the AWGN MAC, in the practically more relevant case of Rayleigh fading, when channel gains are unknown to the decoder. We propose a random coding achievability bound, which we analyze both non-asymptotically and asymptotically. As a candidate practical solution, we propose an explicit iterative coding scheme. The performance of such a solution is surprisingly close to the finite blocklength bounds. Our main findings are twofold. First, just like in the AWGN MAC, we see that jointly decoding a large number of users leads to a surprising phase transition effect, where, at spectral efficiencies below a critical threshold, a perfect multi-user interference cancellation is possible. Second, while the presence of Rayleigh fading significantly increases the minimal required energy-per-bit, the inherent randomization introduced by the channel makes it much easier to attain the optimal performance via iterative schemes. We hope that a principled definition of the random-access model, together with their information-theoretic analysis, will open the road towards unified benchmarking and performance comparison of various random-access solutions for the 5G/6G.
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TL;DR: This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system, where the phase shifts of the RIS are designed relying on statistical channel state information (CSI).
Abstract: This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system with statistical channel state information (CSI). The RIS is deployed to help conventional massive MIMO networks serve the users in the dead zone. We consider the Rician channel model and exploit the long-time statistical CSI to design the phase shifts of the RIS, while the maximum ratio combination (MRC) technique is applied for the active beamforming at the base station (BS) relying on the instantaneous CSI. Firstly, we reveal the power scaling laws and derive the closed-form expressions for the uplink achievable rate which holds for arbitrary numbers of base station (BS) antennas. Based on the theoretical expressions, we discuss the rate performance under some special cases and provide the average asymptotic rate when using random phase shifts. Then, we consider the sum-rate maximization and the minimum user rate maximization problems by optimizing the phase shifts at the RIS. However, these two optimization problems are challenging to solve due to the complicated data rate expression. To solve these problems, we propose a novel genetic algorithm (GA) with low complexity but can achieve considerable performance. Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional massive MIMO systems. Besides, our simulations demonstrate the feasibility of deploying large-size but low-resolution RIS in massive MIMO systems.
TL;DR: A framework to study security, reliability and energy coverage performance of the downlink mmWave simultaneous wireless information and power transfer (SWIPT) unmanned aerial vehicle (UAV) networks under nonorthogonalmultiple access (NOMA) and orthogonal multiple access (OMA), where a UAV serves two types of authorized Internet of Things (IoT) devices in the presence of multiple passive eavesdroppers.
Abstract: Future wireless networks have requirements of large connections, low power consumption, high reliability, and strong security. Considering the abundant available bandwidth of millimeter wave (mmWave) and the large line-of-sight (LOS) link probability of air–ground channel, in this article, we develop a framework to study security, reliability and energy coverage performance of the downlink mmWave simultaneous wireless information and power transfer (SWIPT) unmanned aerial vehicle (UAV) networks under nonorthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes, where a UAV serves two types of authorized Internet of Things (IoT) devices in the presence of multiple passive eavesdroppers. Directional modulation (DM) is also used to improve the physical layer security (PLS) performance. First, the analytical expressions for connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) of the users with high rate security requirement (HRSR) under NOMA and OMA schemes are derived using stochastic geometry. Then, we optimize the active antenna selection of DM using adaptive genetic simulated annealing algorithm (AGSA) to further improve the secrecy performance based on the obtained analytical results. Finally, the closed-form expressions for the COP and energy-information coverage probability (EICP) of the energy-constrained users with low-rate requirement (ECLR) under NOMA and OMA schemes are obtained. The numerical and simulation results provide interesting insights into the influence of various parameters on the tradeoff between the reliability and security for the HRSR user, and the tradeoff between the reliability and energy coverage for the ECLR user. Moreover, the EST of the NOMA scheme outperforms OMA at low transmit power and high codeword transmission rate.
TL;DR: Simulation results are provided to verify the correctness of the derived expressions and it exhibits advantages of the proposed CR-assisted NOMA-V2X system in terms of outage probability and the throughput.
Abstract: In this study, the performance of a secondary network in cognitive radio (CR) is studied in the context of vehicle-to-everything (V2X). The non-orthogonal multiple access (NOMA) is effectively applied in this new system model, namely CR-assisted NOMA-V2X, and it is beneficial to serve group of vehicles. In our proposed system, two schemes related to vehicle-to-vehicle (V2V) transmissions are further considered to enhance performance of the vehicle that needs higher quality of service (QoS). However, the degradation performance can be predicted by evaluating downlink under impacts from interference from the primary network, imperfect channel state information (CSI) and imperfect successive interference cancellation (SIC). The outage performance gap among two vehicles exists since different power allocation factors were assigned to them. To validate the system performance, the outage probability is first derived in exact and approximate forms and then the throughput can be further achieved. The optimal throughput can be obtained by numerical simulations. Simulation results are provided to verify the correctness of the derived expressions and it exhibits advantages of the proposed CR-assisted NOMA-V2X system in terms of outage probability and the throughput.
TL;DR: This paper investigates the unmanned aerial vehicle (UAV)-aided simultaneous uplink and downlink transmission networks, where one UAV acting as a disseminator is connected to multiple access points (AP) and the other UAV acts as a base station (BS) collects data from numerous sensor nodes (SNs).
Abstract: In this paper, we investigate the unmanned aerial vehicle (UAV)-aided simultaneous uplink and downlink transmission networks, where one UAV acting as a disseminator is connected to multiple access points (AP), and the other UAV acting as a base station (BS) collects data from numerous sensor nodes (SNs). The goal of this paper is to maximize the system throughput by jointly optimizing the 3D UAV trajectory, communication scheduling, and UAV-AP/SN transmit power. We first consider a special case where the UAV-BS and UAV-AP trajectories are pre-determined. Although the resulting problem is an integer and non-convex optimization problem, a globally optimal solution is obtained by applying the polyblock outer approximation (POA) method based on the problem’s hidden monotonic structure. Subsequently, for the general case considering the 3D UAV trajectory optimization, an efficient iterative algorithm is proposed to alternately optimize the divided sub-problems based on the successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed design is able to achieve significant system throughput gain over the benchmarks. In addition, the SCA-based method can achieve nearly the same performance as the POA-based method with much lower computational complexity.
TL;DR: In this paper, the authors derived the closed-form expression of lower bound (LB) of achievable uplink data rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers.
Abstract: The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, which incurs transmission rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of achievable uplink data rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel low complexity algorithms to solve the achievable data rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.
TL;DR: It outperforms other deep learning based estimation method with comparable to minimum mean square error (MMSE) estimation performance and is compatible with any downlink pilot patterns making it compatible for modern wireless systems.
Abstract: In this letter we apply deep learning tools to conduct channel estimation for an orthogonal frequency division multiplexing (OFDM) system based on downlink pilots. To be specific, a residual learning based deep neural network specifically designed for channel estimation is introduced. Due to the compact network size as well as the underlying network architecture, the computation cost can be greatly reduced. Furthermore, this residual network architecture is compatible with any downlink pilot patterns making it compatible for modern wireless systems. The estimation error of the introduced residual learning approach is evaluated under 3rd Generation Partnership Project (3GPP) channel models. It outperforms other deep learning based estimation method with comparable to minimum mean square error (MMSE) estimation performance.
TL;DR: In this article, an uplink-aided high mobility downlink channel estimation scheme for the massive MIMO-OTFS networks is proposed, where the expectation maximization based variational Bayesian (EM-VB) framework is adopted to recover the uplink channel parameters including the angle, the delay, the Doppler frequency, and the channel gain for each physical scattering path.
Abstract: Although it is often used in the orthogonal frequency division multiplexing (OFDM) systems, application of massive multiple-input multiple-output (MIMO) over the orthogonal time frequency space (OTFS) modulation could suffer from enormous training overhead in high mobility scenarios. In this paper, we propose one uplink-aided high mobility downlink channel estimation scheme for the massive MIMO-OTFS networks. Specifically, we firstly formulate the time domain massive MIMO-OTFS signal model along the uplink and adopt the expectation maximization based variational Bayesian (EM-VB) framework to recover the uplink channel parameters including the angle, the delay, the Doppler frequency, and the channel gain for each physical scattering path. Correspondingly, with the help of the fast Bayesian inference, one low complex approach is constructed to overcome the bottleneck of the EM-VB. Then, we fully exploit the angle, delay and Doppler reciprocity between the uplink and the downlink and reconstruct the angles, the delays, and the Doppler frequencies for the downlink massive channels at the base station. Furthermore, we examine the downlink massive MIMO channel estimation over the delay-Doppler-angle domain. The channel dispersion of the OTFS over the delay-Doppler domain is carefully analyzed and is utilized to associate one given path with one specific delay-Doppler grid if different paths of any user have distinguished delay-Doppler signatures. Moreover, when all the paths of any user could be perfectly separated over the angle domain, we design the effective path scheduling algorithm to map different users' data into the orthogonal delay-Doppler-angle domain resource and achieve the parallel and low complex downlink 3D channel estimation. For the general case, we adopt the least square estimator with reduced dimension to capture the downlink delay-Doppler-angle channels. Various numerical examples are presented to confirm the validity and robustness of the proposed scheme.
TL;DR: Simulations show that in both uplink and downlink, although secrecy performance deteriorates in short-packet communications, the performance gains of NOMA over traditional orthogonal multiple access are significant, and analyzed the challenges and future trends in this emerging area.
Abstract: The Internet of Things (IoT) is expected to provide ubiquitous wireless machine-type communication devices and extensive information collection, resulting in an unprecedented amount of privacy and secrets exposed to the radio space. Security issues become a major restriction on the further development of IoT. However, secure transmissions in IoT are challenged by low complexity limitation and massive connectivity demand, especially by the use of short packets, which are expected to satisfy the delay requirement in ultra-reliable low-latency communications. Physical layer security can be employed without the constraints of packet length and number of connections. Nevertheless, due to the limitations of complexity, not all existing PLS techniques can be adopted in IoT. Non-orthogonal multiple access (NOMA) is a promising technique for increasing connectivity and reducing delay. Assuming an eavesdropper (Eve) is capable of the same detection capability as legitimate users, this article further exploits the inherent characteristics of NOMA to secure short-packet communications in IoT networks without introducing extra security mechanisms. Both downlink and uplink NOMA schemes are introduced to secure transmission by deliberately increasing the co-channel interference at Eve, which can be viewed as a special cooperative jamming strategy. Simulations show that in both uplink and downlink, although secrecy performance deteriorates in short-packet communications, the performance gains of NOMA over traditional orthogonal multiple access are significant. Finally, we analyze the challenges and future trends in this emerging area.
TL;DR: This article develops a general framework for the NOMA-based system by applying standard recommendations, configurations, and channel characteristics and derives the analytical expression for the achievable ergodic capacity of the considered network.
Abstract: Allowing multiple users access simultaneously, a power-domain nonorthogonal multiple access (NOMA) scheme can provide enhanced resource utilization efficiency and has been viewed as a promising strategy in the satellite networks, to provide high quality-of-service for large number of users within limited spectrum and time resources. Taking into account the random location information of served users, imperfect channel state information (CSI), and antenna-pointing error, we study the ergodic capacity of a NOMA-based uplink satellite network in this article. Specially, we first develop a general framework for the NOMA-based system by applying standard recommendations, configurations, and channel characteristics. Then, the analytical expression for the achievable ergodic capacity of the considered network is derived. Finally, numerical simulations are provided to attest the validity of theoretical results and show the impact of various key parameters, such as location information, link quality, transmission power, imperfect CSI, and antenna-pointing error, on the considered network.
TL;DR: Numerical results show that the proposed designs significantly outperform benchmark schemes, and the utilization of CoMP achieves much higher uplink throughput than interference coordination.
Abstract: This paper studies an unmanned aerial vehicle (UAV)-enabled two-user interference channel for wireless powered communication networks (WPCNs). In this system, two UAVs wirelessly charge two low-power Internet-of-things (IoT)-devices on the ground and collect information from them. We consider two scenarios when both UAVs cooperate in energy transmission and/or information reception via interference coordination and coordinated multi-point (CoMP), respectively. For both scenarios, the UAVs’ trajectories are designed to not only enhance the wireless power transfer (WPT) efficiency in the downlink, but also mitigate the co-channel interference for wireless information transfer (WIT) in the uplink. In particular, the objective is to maximize the uplink common (minimum) throughput of the two IoT-devices over a finite UAV mission period, by jointly optimizing the trajectories of both UAVs and the downlink/uplink wireless resource allocation, subject to the maximum flying speed and collision avoidance constraints for UAVs, as well as the individual energy neutrality constraints at IoT-devices. Under both scenarios with interference coordination and CoMP, we first obtain the optimal solutions to the two common-rate maximization problems for the special case with sufficiently long UAV mission duration. Next, we obtain high-quality solutions for the practical case with finite UAV mission duration by using the alternating optimization and successive convex approximation (SCA). Numerical results show that the proposed designs significantly outperform benchmark schemes, and the utilization of CoMP achieves much higher uplink throughput than interference coordination.
TL;DR: This paper investigates rate splitting (RS) for an uplink non-orthogonal multiple access (NOMA) system with a pair of near and far users adopting cyclic prefixed single carrier transmissions and proposes two kinds of RS schemes.
Abstract: In this paper, we investigate rate splitting (RS) for an uplink non-orthogonal multiple access (NOMA) system with a pair of near and far users adopting cyclic prefixed single carrier transmissions. Frequency-domain equalization is applied to assist successive interference cancellation at the base-station. Two kinds of RS schemes, namely, fixed RS (FRS) and cognitive RS (CRS) schemes, are proposed to realize RS for uplink NOMA with the aim of improving user fairness and outage performance in delay-limited transmissions. Corresponding to the split data streams, transmit power is allocated in either a fixed or cognitive manner for the FRS and CRS schemes, respectively. Based on achievable rate region analysis, the benefits of applying RS to uplink NOMA for enhancing the user fairness and outage performance are revealed. A modified Jain’s index is proposed to measure the user fairness for the considered delay-limited transmissions. Closed-form expressions are derived for the outage probabilities of the paired users, respectively, whereas the preferred system parameters are chosen based on asymptotic outage probability expressions. The enhanced user fairness and superior outage performance of the proposed RS schemes are corroborated by Monte Carlo simulation results.
TL;DR: Two RA protocols based on SA and uplink non-orthogonal multiple access are proposed and applied to wireless sensor networks and wireless powered sensor networks, verifying that the proposed protocols substantially increase the throughput and the number of connected devices compared to SA.
Abstract: Random access (RA) has recently been revisited and considered as a key technology for the medium access control layer of the Internet of Things applications. Compared to other RA protocols, slotted ALOHA (SA) has the advantages of low complexity and elimination of partially overlapping transmissions, reducing the number of collisions, however it may suffer from congestion as the traffic load and the number of devices increase. To this end, two RA protocols based on SA and uplink non-orthogonal multiple access are proposed and applied to wireless sensor networks and wireless powered sensor networks. More specifically, to reduce the number of collisions and increase the throughput of SA, while maintaining low complexity, two detection techniques are used to mitigate the interference, when two sources transmit information in the same time slot, namely successive interference cancellation (SIC) with optimal decoding order policy and joint decoding (JD). To evaluate the performance of the proposed protocols, the outage probability of SIC and JD is derived, which is used to express the average throughput attained by each protocol in closed-form. Finally, both the analytical results and the simulations verify that the proposed protocols substantially increase the throughput and the number of connected devices compared to SA.