Multi-antenna Base Station Research Articles

Contextual Bandit Learning for Machine Type Communications in the Null Space of Multi-Antenna Systems

Ensuring an effective coexistence of conventional broadband cellular users with machine type communications (MTCs) is challenging due to the interference from MTCs to cellular users. This interference challenge stems from the fact that the acquisition of channel state information (CSI) from machine type devices (MTD) to cellular base stations (BS) is infeasible due to the small packet nature of MTC traffic. In this paper, a novel approach based on the concept of opportunistic spatial orthogonalization (OSO) is proposed for interference management between MTC and conventional cellular communications. In particular, a cellular system is considered with a multi-antenna BS in which a receive beamformer is designed to maximize the rate of a cellular user, and, a machine type aggregator (MTA) that receives data from a large set of MTDs. The BS and MTA share the same uplink resources, and, therefore, MTD transmissions create interference on the BS. However, if there is a large number of MTDs to chose from for transmission at each given time for each beamformer, one MTD can be selected such that it causes almost no interference on the BS. A comprehensive analytical study of the characteristics of such an interference from several MTDs on the same beamformer is carried out. It is proven that, for each beamformer, an MTD exists such that the interference on the BS is negligible. To further investigate such interference, the distribution of the signal-to-interference-plus-noise ratio (SINR) of the cellular user is derived, and, subsequently, the distribution of the outage probability is presented. However, the optimal implementation of OSO requires the CSI of all the links in the BS, which is not practical for MTC. To solve this problem, an online learning method based on the concept of contextual multi-armed bandits (MAB) learning is proposed. The receive beamformer is used as the context of the contextual MAB setting and Thompson sampling: a well-known method of solving contextual MAB problems is proposed. Since the number of contexts in this setting can be unlimited, approximating the posterior distributions of Thompson sampling is required. Two function approximation methods, a) linear full posterior sampling, and, b) neural networks are proposed for optimal selection of MTD for transmission for the given beamformer. Simulation results show that is possible to implement OSO with no CSI from MTDs to the BS. Linear full posterior sampling achieves almost 90% of the optimal allocation when the CSI from all the MTDs to the BS is known.

Supervised-Learning for Multi-Hop MU-MIMO Communications With One-Bit Transceivers

This paper considers a nonlinear multi-hop multi-user multiple-input multiple-output (MU-MIMO) relay channel, in which multiple users send information symbols to a multi-antenna base station (BS) with one-bit analog-to-digital converters via intermediate relays, each with one-bit transceiver. To understand the fundamental limit of the detection performance, the optimal maximum-likelihood (ML) detector is proposed with the assumption of perfect and global channel state information (CSI) at the BS. This multi-user detector, however, is not practical due to the unrealistic CSI assumption and the overwhelming detection complexity. These limitations are addressed by presenting a novel detection framework inspired by supervised-learning. The key idea is to model the complicated multihop MU-MIMO channel as a simplified channel with much fewer and learnable parameters. One major finding is that, even using the simplified channel model, a near ML detection performance is achievable with a reasonable amount of pilot overheads in a certain condition. In addition, an online supervised-learning detector is proposed, which adaptively tracks channel variations. The idea is to update the model parameters with a reliably detected data symbol by treating it as a new training (labelled) data. Lastly, a multi-user detector using a deep neural network is proposed. Unlike the model-based approaches, this model-free approach enables to remove the errors in the simplified channel model, while increasing the computational complexity for parameter learning. Via simulations, the detection performances of classical, model-based, and model-free detectors are thoroughly compared to demonstrate the effectiveness of the supervised-learning approaches in this channel.

NOMA-Enabled Layered Video Multicast in Wireless-Powered Relay Systems

This letter proposes a novel layered video multicast scheme for wireless-powered relay systems, where the multi-antenna base station multicasts layered video signals over non-orthogonal multiple access (NOMA) to heterogeneous user groups with the help of a dedicated relay. To minimize the average transmission power of the base station, an optimization problem is formulated to jointly design the energy signal power and layered video multicast beamformers, while guaranteeing all the users’ quality of experience (QoE), i.e., fluent video playback and successful decoding of video signals. Since the formulated optimization problem is non-convex, a low-complexity algorithm is developed to obtain a local minimum, and a novel initial feasible point search algorithm is proposed to initialize the solving process. Simulation results reveal that the proposed scheme works well and can greatly reduce the power consumption.

Multi-objective Antenna Selection in a Full Duplex Base Station

The use of full-duplex (FD) communication systems is a new way to increase spectral efficiency. For this reason, it has received serious attention in the new generation of wireless communication systems. The main challenge of FD systems is self-interference that needs to be reduced appropriately. In this paper, we have considered an FD multi-antenna base station in a cellular network and we have used the antenna selection technique to resolve the self-interference issue. We have also provided a new criterion to select an appropriate antenna. In this new criterion, the antenna selection is modeled as a multi-objective optimization problem. Here, the antennas which simultaneously minimizes interference channel gain and maximizes uplink (UL) and downlink (DL) channel gains are selected for transmission and reception. The base station has to perform well in both the UL and DL and reduce the self-interference simultaneously. Therefore, the multi-objective criterion has a better performance than the single-objective criterion. Although, in conventional antenna selection with the single-objective criterion, only the function of the UL and DL channels or the interference channel is considered. Finally, the simulations show that the new criterion has a higher throughput rate than the other conventional single-objective antenna selection techniques.

Asynchronous Designs for Multiuser MIMO Wireless Powered Communication Networks

This paper considers multiuser multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). In this system, a multiantenna power beacon (PB) transfers wireless energy to several multiantenna users, and then the users transmit data to a multiantenna base station. Time durations for the energy harvesting (EH) operations at each user can be differently set based on the users’ channel conditions, which results in an asynchronous protocol on the EH and the information transmission. We maximize the sum rate performance of the asynchronous WPCN by jointly optimizing the energy precoding matrices at the PB, the information precoding matrices, and the EH time durations at the users. By using convex optimization techniques, the optimal algorithm for the sum rate maximization problem is provided with analytical expressions of the optimal precoding matrices. Simulation results verify that the proposed optimal algorithm outperforms conventional schemes.

Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication

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.

Secrecy Throughput Maximization for Full-Duplex Wireless Powered IoT Networks Under Fairness Constraints

In this paper, we study the secrecy throughput of a full-duplex wireless powered communication network (WPCN) for Internet of Things (IoT). The WPCN consists of a full-duplex multiantenna base station (BS) and a number of sensor nodes. The BS transmits energy all the time, and each node harvests energy prior to its transmission time slot. The nodes sequentially transmit their confidential information to the BS, and the other nodes are considered as potential eavesdroppers. We first aim to optimize the sum secrecy throughput of the nodes. The optimization variables are the duration of the time slots and the BS beamforming vectors in different time slots. The optimization problem is shown to be nonconvex. To tackle the problem, we propose a suboptimal two stage approach, referred to as sum secrecy throughput maximization (SSTM). In the first stage, the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during information transmission time slots. Then, the optimal beamforming vector in the initial noninformation transmission time slot and the optimal time slots are derived. We then consider secrecy throughput fairness among the nodes and propose max–min fair (MMF) and proportional fair (PF) algorithms. The MMF algorithm maximizes the minimum secrecy throughput of the nodes, while the PF achieves a good tradeoff between the sum secrecy throughput and fairness among the nodes. Through the numerical simulations, we first demonstrate the superior performance of the SSTM to uniform time slotting and beamforming in different settings. Then, we show the effectiveness of MMF and PF algorithms.

Joint admission control and beamforming in max–min fairness networks

The max–min fairness (MMF) strategy has been widely employed to manage wireless networks since it guarantees fairness among users. However, with a large number of users awaiting service, the network tends to be congested and the quality-of-service (QoS) will degrade substantially. This motivates us to study the MMF problem jointly with the consideration of admission control. Specifically, the authors consider a downlink network consisting of a multi-antenna base station (BS) and multiple single-antenna users. By jointly optimising the admissible users and the BS transmit beamformers, they aim to maximise the minimum signal-to-interference-plus-noise-ratio of the admissible users, such that high QoS and fairness can be guaranteed simultaneously for them. This problem is essentially NP-hard, and hence they pursue an efficient approximate solution to it. To this end, they first reformulate this problem from the perspective of sparse optimisation, and then develop a low-complexity algorithm to iteratively solve the approximate problem. Moreover, to facilitate the algorithm's implementation, they further recast the subproblem in each iteration, such that it fits into the framework of the alternating direction methods of multipliers. Finally, an efficient distributed algorithm is designed, with each step being simply computed in a closed form.

Sparsity Learning-Based Multiuser Detection in Grant-Free Massive-Device Multiple Access

In this paper, we study the multiuser detection (MUD) problem for a grant-free massive-device multiple access (MaDMA) system, where a large number of single-antenna user devices transmit sporadic data to a multi-antenna base station (BS). Specifically, we put forth two MUD schemes, termed random sparsity learning multiuser detection (RSL-MUD) and structured sparsity learning multiuser detection (SSL-MUD) for the time-slotted and non-time-slotted grant-free MaDMA systems, respectively. In RSL-MUD, active users generate and transmit data packets with random sparsity. In SSL-MUD, we introduce a sliding-window-based detection framework, and the user signals in each observation window naturally exhibit structured sparsity. We show that by exploiting the sparsity embedded in the user signals, we can recover the user activity state, the channel, and the user data in a single phase, without using pilot signals for channel estimation and/or active user identification. To this end, we develop a message-passing-based statistical inference framework for the BS to blindly detect the user data without any prior knowledge of the identities and the channel state information (CSI) of active users. The simulation results show that our RSL-MUD and SSL-MUD schemes significantly outperform their counterpart schemes in both reducing the transmission overhead and improving the error behavior of the system.

Joint Mode Selection and Transceiver Design for Device-to-Device Communications Underlaying Multi-User MIMO Cellular Networks

Consider a network consisting of one multi-antenna base station (BS) and multiple pairs of multi-antenna user equipment’s (UEs). For each UE pair, the communication between transmitter and receiver is established either through BS or via device-to-device (D2D) link. We assume that the D2D transmission and cellular transmission are equally prioritized and share the same resources. To improve the network throughput, we maximize the sum rate by jointly optimizing the transmission mode of each UE pair and the associated transceivers. Due to the NP-hardness of this problem, we first perform some efficient approximation to it and then design an iterative algorithm, which is guaranteed to converge to a stationary solution by solving a series of weighted minimum mean square error (WMMSE) problems. The proposed algorithm has two distinguishing features. First, it only solves the WMMSE problem inexactly in each iteration, which thereby has a simplified algorithm structure and accelerated convergence behavior than the classical one. Second, we further fit the WMMSE problem into the alternating direction method of multipliers (ADMM) framework, making it amenable to parallel and distributed computation. Finally, the approximated problem is solved efficiently and distributively, with simple closed-form solutions in each step.

Physical‐layer security for cellular multiuser two‐way relaying networks with single and multiple decode‐and‐forward relays

AbstractWe investigate the secrecy performance of cellular multiuser two‐way relay networks, where the bidirectional information exchange between a multiantenna base station (BS), and several users can be accomplished with the assistance of decode‐and‐forward relay(s) in the presence of a multiantenna passive eavesdropper. We employ low‐complexity antenna selection strategy at the BS coupled with user scheduling for maximizing the end‐to‐end signal‐to‐noise ratios (SNRs). To enhance the wiretap probability, the eavesdropper applies a maximal ratio combining technique to combine the received signals. Under such considerations, in the single‐relay scenario, we deduce the exact closed‐form secrecy outage probability (SOP) expression under Rayleigh fading channels. For more insights into the system secrecy diversity order, we derive the closed‐form asymptotic SOP expressions at high SNR based on the quality of the main and wiretap links. In the multirelay scenario, we formulate an opportunistic relay selection strategy, followed by its impact on the SOP performance in the presence of Rayleigh channels. To show the impact of multiple relays on the secrecy diversity order, we derive the asymptotic SOP expressions in the high SNR regime depending on the quality of the main and wiretap links. It is shown from the asymptotic SOP expressions under both single‐relay and multirelay scenarios that the secrecy diversity order reduces to zero in the high SNR regime, regardless of number of antennas, number of users, and number of relays. Our results also reveal that the SOP performance diminishes with an increased number of eavesdropper antennas. Numerical and simulation results verify our derived analytical findings.

Enhanced Multiuser Superposition Transmission Through Structured Modulation

The fifth-generation (5G) air interface, namely, dynamic multiple access (MA) based on multiuser superposition transmission (MUST) and orthogonal MA (OMA), may require a complicated scheduling and heavy signaling overhead. To address these challenges, we propose a unified MA scheme for future cellular networks, which we refer to as structured MUST (S-MUST). In S-MUST, we apply complex power allocation coefficients (CPACs) over multiuser legacy constellations to generate a composite constellation. In particular, the in-phase ( $I$ ) and quadrature ( $Q$ ) components of the legacy constellation of each user are separately multiplied by those of the CPACs. As such, the CPACs offer an extra degree of freedom for multiplexing users and guarantee fairness in symmetric broadcast channels. This new paradigm of superposition coding allows us to design $IQ$ separation at the user side, which significantly reduces the decoding complexity without degrading performance. Hence, it supports low-complexity frequency-selective scheduling that does not entail dynamical switching between MUST and OMA. We further propose to quantize the CPACs into complex numbers where $I$ and $Q$ components of each quantized coefficient are primes, facilitating parallel interference cancellation at each user via modulo operations; last but not least, we generalize the design of S-MUST to exploit the capabilities of multiantenna base stations. The proposed S-MUST exhibits an improved user fairness with respect to conventional MUST (134% spectral efficiency enhancement) and a lower system complexity compared with dynamically alternating MUST and OMA.

Multi-Antenna NOMA for Computation Offloading in Multiuser Mobile Edge Computing Systems

This paper studies a multiuser mobile edge computing (MEC) system in which one base station (BS) serves multiple users with intensive computation tasks. We exploit the multi-antenna non-orthogonal multiple access (NOMA) technique for multiuser computation offloading, such that different users can simultaneously offload their computation tasks to the multi-antenna BS over the same time/frequency resources, and the BS can employ successive interference cancelation (SIC) to efficiently decode all users’ offloaded tasks for remote execution. In particular, we pursue energy-efficient MEC designs by considering two cases with partial and binary offloading, respectively. We aim to minimize the weighted sum-energy consumption at all users subject to their computation latency constraints, by jointly optimizing the communication and computation resource allocation as well as the BS’s decoding order for SIC. For the case with partial offloading, the weighted sum-energy minimization is a convex optimization problem, for which an efficient algorithm based on the Lagrange duality method is presented to obtain the globally optimal solution. For the case with binary offloading, the weighted sum-energy minimization corresponds to a mixed Boolean convex optimization problem that is generally more difficult to be solved. We first use the branch-and-bound (BnB) method to obtain the globally optimal solution and then develop two low-complexity algorithms based on the greedy method and the convex relaxation, respectively, to find suboptimal solutions with high quality in practice. Via numerical results, it is shown that the proposed NOMA-based computation offloading design significantly improves the energy efficiency of the multiuser MEC system as compared to other benchmark schemes. It is also shown that for the case with binary offloading, the proposed greedy method performs close to the optimal BnB-based solution, and the convex relaxation-based solution achieves a suboptimal performance but with lower implementation complexity.

Network-Coded NOMA With Antenna Selection for the Support of Two Heterogeneous Groups of Users

The combination of non-orthogonal multiple access (NOMA) and transmit antenna selection (TAS) techniques has recently attracted significant attention due to the low cost, low complexity, and high diversity gains. Meanwhile, random linear coding (RLC) is considered to be a promising technique for achieving high reliability and low latency in multicast communications. In this paper, we consider a downlink system with a multi-antenna base station and two multicast groups of single-antenna users, where one group can afford to be served opportunistically, while the other group consists of comparatively low-power devices with limited processing capabilities that have strict quality of service (QoS) requirements. In order to boost reliability and satisfy the QoS requirements of the multicast groups, we propose a cross-layer framework, including NOMA-based TAS at the physical layer and RLC at the application layer. In particular, two low-complexity TAS protocols for NOMA are studied in order to exploit the diversity gain and meet the QoS requirements. In addition, RLC analysis aims to facilitate heterogeneous users, such that sliding window-based sparse RLC is employed for computational restricted users, and conventional RLC is considered for others. Theoretical expressions that characterize the performance of the proposed framework are derived and verified through simulation results.

On Security and Throughput for Energy Harvesting Untrusted Relays in IoT Systems Using NOMA

In this paper, we analyze the secrecy and throughput of multiple-input single-output (MISO) energy harvesting (EH) Internet of Things (IoT) systems, in which a multi-antenna base station (BS) transmits signals to IoT devices (IoTDs) with the help of relays. Specifically, the communication process is separated into two phases. In the first phase, the BS applies transmit antenna selection (TAS) to broadcast the signal to the relays and IoTDs by using non-orthogonal multiple access (NOMA). Here, the relays use power-splitting-based relaying (PSR) for EH and information processing. In the second phase, the selected relay employs the amplify-and-forward (AF) technique to forward the received signal to the IoTDs using NOMA. The information transmitted from the BS to the IoTD risks leakage by the relay, which is able to act as an eavesdropper (EAV) (i.e., an untrusted relay). To analyze the secrecy performance, we investigate three schemes: random-BS-best-relay (RBBR), best-BS-random-relay (BBRR), and best-BS-best-relay (BBBR). The physical layer secrecy (PLS) performance is characterized by deriving closed-form expressions of secrecy outage probability (SOP) for the IoTDs. A BS transmit power optimization algorithm is also proposed to achieve the best secrecy performance. Based on this, we then evaluate the system performance of the considered system, i.e., the outage probability and throughput. In addition, the impacts of the EH time, the power-splitting ratio, the numbers of BS antennas, and the numbers of untrusted relays on the SOP and throughput are investigated. The Monte Carlo approach is applied to verify our analytical results. Finally, the numerical examples indicate that the system performance of BBBR is greater than that of RBBR and BBRR.

Unified Derivation of Optimal Feedback Rate in Downlink Cellular Systems With Multi-Antenna Base Stations

A downlink cellular network in which base stations (BSs) are distributed according to a homogeneous Poisson point process is considered. Each BS simultaneously serves multiple single-antenna users for downlink transmission; zero-forcing beamforming (ZFBF) is used for spatial division multiplexing. Each user quantizes and feeds back the channel state information (CSI) to the transmitter to ensure that at least partial CSI is available at the transmitter. The net spectral efficiency is introduced as a main performance metric; it measures the net profit achieved by using limited-feedback-based ZFBF. This paper mainly analyzes the optimal number of feedback bits that maximizes the net spectral efficiency. The optimal number is analyzed with respect to various important system parameters by extending and generalizing previous studies. Specifically, this paper analyzes the asymptotic behaviors of the optimal number with respect to the signal-to-noise ratio (SNR) and the channel coherence time; it does so by providing close approximations to the optimal number that is generally satisfied regardless of the values of the SNR, number of users, path loss exponent, and channel coherence time. It is demonstrated by simulation that the proposed approximations are extremely close to the optimal number on the region of interest of the system parameters.

Joint Integer-Forcing Precoder Design for MIMO Multiuser Relay System

In this paper, we study a relay-aided multiuser amplify-and-forward relaying system where a multi-antenna base station (BS) transmits downlink to multiple single-antenna mobile stations (MSs) via a multi-antenna relay station (RS). We propose a novel precoder design scheme by using the joint integer-forcing method to achieve high rate with the algebraic structure of lattice codes. Inspired by the concept of decoding the linear equations in compute-and-forward relay systems, the linear precoding matrix is generalized as an integer matrix that is approximate to the channel matrix, thereby mitigating the multiuser interference (MUI) at the MSs. The power allocation optimization problem at the BS is taken into account to improve the performance of the scheme. The simulation results show the outstanding performance of the proposed scheme.

Low Complexity Hybrid Precoding for Multiuser Millimeter Wave Systems Over Frequency Selective Channels

We propose new hybrid precoding schemes for the downlink transmission in a multiuser millimeter wave (mmWave) system over frequency selective channels. In the system, the multiantenna base station employs the fully connected architecture and uses orthogonal frequency-division multiplexing to serve multiple mobile users. We first propose an alternating minimization based hybrid precoding scheme by transforming the wideband hybrid precoding problem to a narrowband hybrid precoding problem. We then propose a beamforming-based hybrid precoding scheme by exploiting the correlation of subchannels to profoundly reduce the computational complexity. Numerical results are presented to demonstrate the advantages of our proposed schemes relative to two existing schemes, as well as to examine the impact of system parameters on the achieved performance.

Generalized Signal-Space Alignment Based Physical-Layer Network Coding for Distributed MIMO Systems

We study an uplink distributed multiple-input-multiple-output (D-MIMO) system, where multiple users are served by multiple base stations (BSs) connected to a common central unit (CU). We propose a generalized signal-space alignment (SSA)-based physical-layer network coding (PNC) scheme, termed G-SSA-PNC. In G-SSA-PNC, the multi-antenna users encode their messages by using nested lattice coding, linearly precode the nested lattice codewords for SSA, and then broadcast to the multi-antenna BSs. The BSs linearly post-process the received signals to extract signals at certain aligned directions. PNC decoding is applied to those extracted signals to generate network-coded message combinations, and the decoded message combinations are then forwarded to the CU for user messages decoding. Compared with the original SSA-PNC scheme, our scheme is different in the following three aspects. First, the G-SSA-PNC employs an individual shaping lattice for nested lattice coding at each source, as in contrast to the use of a common shaping lattice in original SSA-PNC. Second, we derive the feasibility condition of PNC decoding under the new nested lattice coding scheme and show that the SSA precoding and the nested lattice coding needs to be jointly designed to meet the feasibility condition. Third, we formulate a sum rate maximization problem and propose a suboptimal solution to the problem. The numerical results demonstrate the superiority of the G-SSA-PNC in performance over the benchmark schemes, including original SSA-PNC, compress-and-forward, and interference alignment.

Rate-Energy Outage Analysis of MISO SWIPT With Multiple Energy Harvesting Sensors

We study a multi-input-single-output (MISO) transmission system consisting of a multi-antenna basestation (BS) and a single-antenna user in the presence of multiple single-antenna radio frequency (RF) energy harvesting (EH) sensor nodes, where the user decodes information and multiple EH sensor nodes accumulate energy through the received signal from the BS. The sensor nodes coordinate their transmissions to perform virtual MISO beamforming, which requires sufficient individual and sum transmission power across multiple sensor nodes. For this reason, we consider simultaneously both the individual and sum EH constraints for SWIPT networks. Specifically, under both the individual and sum EH constraints, we propose beam selection of random unitary beamforming to maximize the transmission rate of the MISO system. Then we derive the exact rate-energy outage probability in closed-form for the system with identical average value of harvested energy across all sensor nodes and the exact probability in recursive form for the system with distinct average value of harvested energy at each sensor node. Numerical results confirm that the analyses are exact and the outage performance improves as the number of antennas at the BS increases and/or as the average received power at the user and sensor nodes increases.