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Due to the shortage of wireless resources and the emergence of a large number of users, determining how to guarantee the quality-of-service (QoS) requirements of users and make more users work in the same spectrum has become an urgent research topic. In this paper, we study a multi-user MIMO (MU-MIMO) cellular network system model in which cellular users (CUs) share the same spectrum resource with multiple device-to-device (D2D) pairs. To maximize the number of admitted D2D pairs sharing the same spectrum with the CUs, a joint power allocation and channel gain (JPACG) algorithm is proposed. The optimization problem is divided into two steps to be solved. First, the power allocation of CUs without D2D pairs admitted is solved. Then, the optimization problem is transformed into minimizing the interference to CUs when CUs are treated as primary users. The admittance order of D2D pairs is determined by the transmission power and channel gain. The proposed algorithm uses a convex optimization algorithm to solve the problem of power allocation joint interference channel gain in order to maximize the number of admitted D2D pairs under the constraints of the signal-to-interference-plus-noise ratio (SINR) threshold and maximum transmission power. In addition, the effect of the number of admitted D2D pairs on the total sum rate of all users is also analyzed. The simulation results show that the proposed JPACG algorithm can achieve better performance in admitting D2D pairs. [ABSTRACT FROM AUTHOR]

The inclusion of the sensing functionality in the coming generations of cellular networks, necessitates a rethink of dense cell deployments. In this paper, we analyze and optimize dense cell topologies for dual-functional radar-communication (DFRC) cellular networks. With the aid of tools from stochastic geometry, we derive new analytical expressions of the potential area spectral efficiencies in $(\textrm {bit}/\textrm {sec}/m^{2})$ of radar and communication systems using zero-forcing precoding scheme. Based on the new formulations of the potential area spectral efficiencies, the energy efficiency (bit/Joule) of DFRC systems is provided in a closed-form formula. Then, an optimization problem to obtain the optimal base station (BS) density that maximizes the network-level energy efficiency is formulated and investigated. Our analytical and numerical results demonstrate that the inclusion of the sensing functionality clearly differentiates the optimal BS topologies for the DFRC systems against classical communication-only systems.

Communications in the mmWave and THz bands will be a key technological pillar for next-generation wireless networks. However, the increase in frequency results in an increase in path loss, which must be compensated for by using large antenna arrays. This introduces challenging issues due to power consumption, signalling overhead for channel estimation, hardware complexity, and slow beamforming and beam alignment schemes, which are in contrast with the requirements of next-generation wireless networks. In this paper, we propose the adoption of a retro-directive antenna array (RAA) at the user equipment (UE) side, where the signal sent by the base station (BS) is reflected towards the source after being conjugated and phase-modulated according to the UE data. By making use of modified Power Methods for the computation of the eigenvectors of the resulting round-trip channel, it is shown that, in single and multi-user multiple-input multiple-output (MIMO) scenarios, ultra-low complexity UEs can establish parallel communication links automatically with the BS in a very short time. This is done in a blind way, also by tracking fast channel variations while communicating, without the need for ADC chains at the UE as well as without explicit channel estimation and time-consuming beamforming and beam alignment schemes.

Terahertz (THz) communications with a frequency band 0.1 – 10 THz are envisioned as a promising solution to future high-speed wireless communication. Although with tens of gigahertz available bandwidth, THz signals suffer from severe free-spreading loss and molecular-absorption loss, which limit the wireless transmission distance. To compensate for the propagation loss, the ultra-massive multiple-input-multiple-output (UM-MIMO) can be applied to generate a high-gain directional beam by beamforming technologies. In this paper, a review of beamforming technologies for THz UM-MIMO systems is provided. Specifically, we first present the system model of THz UM-MIMO and identify its channel parameters and architecture types. Then, we illustrate the basic principles of beamforming via UM-MIMO and discuss the far-field and near-field assumptions in THz UM-MIMO. Moreover, an important beamforming strategy in THz band, i.e., beam training, is introduced wherein the beam training protocol and codebook design approaches are summarized. The intelligent-reflecting-surface (IRS)-assisted joint beamforming and multi-user beamforming in THz UM-MIMO systems are studied, respectively. The spatial-wideband effect and frequency-wideband effect in the THz beamforming are analyzed and the corresponding solutions are provided. Further, we present the corresponding fabrication techniques and illuminate the emerging applications benefiting from THz beamforming. Open challenges and future research directions on THz UM-MIMO systems are finally highlighted.

This paper tackles the problem of downlink data transmission in massive multiple-input multiple-output (MIMO) systems where user equipments (UEs) exhibit high spatial correlation and channel estimation is limited by strong pilot contamination. Signal subspace separation among UEs is, in fact, rarely realized in practice and is generally beyond the control of the network designer (as it is dictated by the physical scattering environment). In this context, we propose a novel statistical beamforming technique, referred to as MIMO covariance shaping, that exploits multiple antennas at the UEs and leverages the realistic non-Kronecker structure of massive MIMO channels to target a suitable shaping of the channel statistics performed at the UE-side. To optimize the covariance shaping strategies, we propose a low-complexity block coordinate descent algorithm that is proved to converge to a limit point of the original nonconvex problem. For the two-UE case, this is shown to converge to a stationary point of the original problem. Numerical results illustrate the sum-rate performance gains of the proposed method with respect to spatial multiplexing in scenarios where the spatial selectivity of the base station is not sufficient to separate closely spaced UEs. [ABSTRACT FROM AUTHOR]

Tensors are multi-way arrays which can be used to model systems spanning many domains. This work proposes to use tensors for characterizing, analyzing, and designing multi-domain communication systems. Most modern day communication systems make use of coding and modulation across different domains such as space, frequency, time. Hence a unified mathematical framework characterizing such a multiple-domain system in an intuitive manner is well needed. In this paper, we present such a unified framework that characterizes a communication system with $N$ input domains, $M$ output domains and an $M+N$ domains multi-linear tensor channel. The proposed framework is generic where the physical interpretation of the domains is system specific. We illustrate a few examples from multi-antenna multi-carrier and multi-user systems that fit the proposed framework. Assuming a fixed tensor channel, we provide an information theoretic analysis by deriving its Shannon capacity and input power allocation under a variety of power constraints. In this paper we show how the tensor framework’s suitability to mathematically describe a family of power constraints can be used to design and analyze various multiple domain communication systems. The tensor based approach extends water-filling from a matrix setting to tensors, encapsulating the effects of multiple domains thereby allowing joint multi-domain precoding. We show that the capacity pre-log for a tensor channel increases exponentially in the number of domains, indicating the potential of tensor based multi-domain communication systems to provide the large information transmission rates envisaged for 5G and beyond systems. We also show the application of the tensor framework in characterizing the capacity and rate regions of multi-user MIMO channels. Both multiple access and interference channels are considered where the tensor based approach leads to a coordinated users transmission scheme. Such a scheme ensures higher achievable sum rates as compared to independent user transmissions.

Propelled by multi-user MIMO (MU-MIMO) technology, unmanned aerial vehicles (UAVs) as mobile hotspots have recently emerged as an attractive wireless communication paradigm. Rate adaptation (RA) becomes indispensable to enhance UAV communication robustness against UAV mobility-induced channel variances. However, existing MU-MIMO RA algorithms are mainly designed for ground communications with relatively stable channel coherence time, which incurs channel measurement staleness and sub-optimal rate selections when coping with highly dynamic air-to-ground links. In this paper, we propose SensRate, a new uplink MU-MIMO RA algorithm dedicated for low-altitude UAVs, which exploits inherent on-board sensors used for flight control with no extra cost. We propose a novel channel prediction algorithm that utilizes sensor-estimated flight states to assist channel direction prediction for each client and estimate inter-user interference for optimal rates. We provide an implementation of our design using a commercial UAV and show that it achieves an average throughput gain of $1.24\times $ and $1.28\times $ compared with the bestknown RA algorithm for 2- and 3-antenna APs, respectively. [ABSTRACT FROM AUTHOR]

Multi-user multiple input multiple output (MU-MIMO) serves multiple users simultaneously by sharing the same channel and bandwidth. The performance degrades in presence of multi-user interference. The multi-user beamforming (precoding) techniques came into existence to tackle these interference. This paper investigates the performance of downlink MU-MIMO by optimally designing the transmit beamforming with known channel state information at the transmitter (CSIT). Several linear precoding algorithms are employed to accomplish this goal. However, linear precoding techniques such as maximum ratio transmission (MRT), zero forcing (ZF) and minimum mean square error (MMSE) do not provide achievable performance at all signal to noise ratios (SNRs) equally. The transmit beamforming can be designed optimally by formulating the convex optimization problem. This optimal multi-user beamforming improves the performance at all SNRs. Practically, it is hard to get the optimal solution for non convex problems. Therefore, multi-user performance is improved over linear precoding algorithms at all signal levels by introducing joint processing techniques of both linear and non-linear types. Tomlinson Harashima precoding (THP) is a non-linear precoding technique which provides the best solution to improve the performance at high SNR by minimizing the multi-user interference. This paper uses two non-linear algorithms based THP such as MMSE-THP and ZF-THP. The performance of the schemes have been evaluated both by analysis and simulations. It has been observed that MMSE-THP attains significant performance improvement over ZF-THP. [ABSTRACT FROM AUTHOR]

Massive multi-user multiple-input multiple-output (MU-MIMO) communication is a crucial technique for next-generation wireless systems because it enables high-throughput and ultra-reliable data transmission. However, high power consumption is a challenging problem in conventional transceiver designs, where each RF chain at the transmitter requires a pair of high-resolution digital-to-analog converters (DACs), which are the primary power sources in front-end circuits. Therefore, quantized precoding algorithms were recently proposed to address this issue. They used low-resolution DACs at the transmitter and compensated for the distortion caused by the quantization effect at the baseband. This paper proposes a constellation-range gain-controlled lattice-reduction-aided (CR-GCLRA) one-bit precoding algorithm for massive MU-MIMO systems with high-order quadrature amplitude modulation (QAM) signaling. Lattice reduction (LR) preprocessing is adopted for performance enhancement. A novel gain-control mechanism is proposed to address the constellation range expansion problem in conventional LR preprocessing. We designed and implemented a CR-GCLRA one-bit precoding processor by using TSMC 40-nm CMOS technology to accelerate the one-bit precoding processing for 64-QAM $4 \times 64$ MU-MIMO system. The proposed one-bit precoding processor can achieve a throughput of 43.92 Mbits per second at a clock speed of 269 MHz with 271 mW power consumption and $0.51\mu \text{s}$ latency.

In this paper, we consider a secure full-duplex (FD) multi-user multiple-input multiple-output (MU-MIMO) network where an FD base station simultaneously communicates with users and transfers energy for untrusted energy harvesting (EH) devices. Considering that the EH users are untrusted and try to wiretap the signals, the base station adds artificial noise (AN) signals into its transmitted signals to enhance the system security. We focus on two single-objective optimization problems including (i) the sum secrecy rate maximization problem, (ii) the harvested energy maximization problem, and a multi-objective optimization problem of the secure information and EH trade-off. The goal is to jointly optimize the transmit covariance matrices of the precoding and AN jamming under the constraints on transmit power budgets, harvested energy requirements, and minimum secrecy rates. Three considered optimization problems are highly non-convex due to the non-convexity of the sum secrecy rate. To handle this challenge, we first recast the sum secrecy rate as the inner convex approximation one thanks to the difference of two convex functions (DC) approach. Then, the sequential convex programming (SCP) approach is utilized to iteratively find the optimal solution. The numerical simulations are provided to investigate the impacts of the system settings and residual self-interference on the achievable system performances in terms of the secrecy rate and harvested energy. Furthermore, the numerical results also reveal the interesting trade-offs between the secrecy rate and harvested energy. [ABSTRACT FROM AUTHOR]

To maximize the spectral efficiency while satisfying the increasing mobile traffic demand, LTE can offload downlink traffics to unlicensed UNII bands at 5GHz via the standardized technology LTE-LAA (Licensed Assisted Access). In the scenario where LTE-LAA inevitably coexists with already pervasive WiFi devices which were designed without the consideration of LTE-LAA, recent studies have reported that the asymmetric hidden terminal (AHT) problem, caused by the difference in the listen-before-talk clear-channel-assessment (CCA) thresholds of WiFi and LTE-LAA, severely degrades the throughput and delay performance of both LTE-LAA and WiFi. As a remedy to the AHT problem, we propose a multi-antenna multi-user transmit precoding and power control techniques for LTE-LAA base stations to make neighboring Wi-Fi APs defer their new transmissions, thereby peacefully coexisting with them. The proposed scheme does not require any technical amendment or extra work on the conventional Wi-Fi. Based on our realistic simulator with essential MAC and PHY functions implemented, it is confirmed that the proposed scheme improves the throughput and delay performance of LTE-LAA while maintaining or even improving that of Wi-Fi. [ABSTRACT FROM AUTHOR]

Effective suppression of inadvertent or deliberate jamming signals is crucial to ensure reliable wireless communication. However, as demonstrated in this paper, when the transmitted jamming signals are highly correlated, and especially when the correlation coefficient varies, nullifying the jamming signals can be challenging. Unlike existing techniques that often assume uncorrelated jamming signals or non-zero but constant correlation, we analyze the impact of the non-zero and varying correlations between transmitted jamming signals on the suppression of the jamming signals. Specifically, we observe that by varying the correlation coefficients between transmitted jamming signals, jammers can “virtually change” the jamming channels hence their nullspace, even when these channels do not physically change. This makes most jamming suppression techniques that rely on steering receiving beams towards the nullspace of jamming channels no longer applicable. To tackle the problem, we develop techniques to effectively track the jamming nullspace and correspondingly update receiving beams. Monte Carlo simulations show that our proposed techniques can suppress/nullify jamming signals for all considered scenarios with non-zero and varying correlation coefficients amongst transmitted jamming signals. [ABSTRACT FROM AUTHOR]

Abstract Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-limited mobile devices from computation-intensive tasks, which enables devices to offload workloads to nearby MEC servers and improve the quality of computation experience. In this paper, an MEC enabled multi-user multi-input multi-output (MIMO) system with stochastic wireless channels and task arrivals is considered. In order to minimize long-term average computation cost in terms of power consumption and buffering delay at each user, a deep reinforcement learning (DRL)-based dynamic computation offloading strategy is investigated to build a scalable system with limited feedback. Specifically, a continuous action space-based DRL approach named deep deterministic policy gradient (DDPG) is adopted to learn decentralized computation offloading policies at all users respectively, where local execution and task offloading powers will be adaptively allocated according to each user’s local observation. Numerical results demonstrate that the proposed DDPG-based strategy can help each user learn an efficient dynamic offloading policy and also verify the superiority of its continuous power allocation capability to policies learned by conventional discrete action space-based reinforcement learning approaches like deep Q-network (DQN) as well as some other greedy strategies with reduced computation cost. Besides, power-delay tradeoff for computation offloading is also analyzed for both the DDPG-based and DQN-based strategies.

This paper presents an analysis of the effect that the radiation pattern of the antenna element that makes up the base station array has on the structure of multi-user multiple-input multiple-output (MU-MIMO) channels. In this paper, the analysis focuses on the uplink. It is well known that the antennas are an inseparable part of the radio channel. The use of more or less directional antennas as elements of the base station array of a MIMO system influences the channel in two fundamental parameters that affect the performance of MU-MIMO systems: on the one hand, the coherence bandwidth, which determines the necessary overhead in the channel estimation; and, on the other hand, the orthogonality of the subchannels between the multiple users and the base station, which influences the degree with which the condition of “favorable propagation” is fulfilled. Based on an experimental analysis using both omnidirectional and directional antennas, conclusions are drawn about how these two parameters are affected and their influence on the spectral efficiency obtainable. The measurement campaign was carried out in an indoor environment in the 3 to 4 GHz band.

The in-band full-duplex (IBFD) mechanism offers many advantages over standard half duplex (HD) systems: it can potentially double the capacity and increase spectrum utilization efficiency. While IBFD renders many conventional research problems irrelevant, it unearths several new problems that urgently need attention. We focus on a the design of a Licensed Shared Access (LSA) based spectrum sharing framework with IBFD multi-cell multi-user multiple-input multiple-output (MIMO) communication network as the licensee, which operates in the service region of a multi-user MIMO incumbent network. We propose a modified LSA controller framework for the protection zone scenario of LSA, whereby the LSA controller ensures that the licensees always have access to data rates above a given threshold, for which aggregate interference towards the incumbents is also kept at minimum. Accordingly, we propose a hierarchical two-step beamforming framework, where i) the LSA controller first seeks the quality of service (QoS) requirements from the licensee network. Based on the QoS requirements, beamformers are designed to minimize the aggregate interference towards the incumbents. ii) In the second step, the interference minimized in the first step is considered as a constraint for the new beamformer design problem that maximizes the aggregate uplink and downlink throughput of the licensee network. Numerical results demonstrate the precedency of the proposed algorithms for IBFD LSA when compared to baseline HD LSA, whereby we observe i) over 60% improvement in throughput performance for the licensee network and ii) a reduction in interference exposure towards the incumbents from the licensees by at least 6.8315dB.

In this letter, we analyze the coverage performance of multi-user multiple-input multiple-output (MU-MIMO) downlink cellular networks. We model the locations of the base stations and mobile users using Poisson Point Processes and employ maximum ratio transmission precoding at the base station. Our analysis builds upon Prony’s method to approximately calculate the coverage probability using the sum of exponentials which facilitates the computation of Laplace transform of both intra-cell and inter-cell interference. We demonstrate the accuracy of derived approximate coverage probability expressions through numerical simulations for different MU-MIMO configurations. [ABSTRACT FROM AUTHOR]

MU-MIMO and OFDMA are two key techniques in IEEE 802.11ax standard. Although these two techniques have been intensively studied in cellular networks, their joint optimization in Wi-Fi networks has been rarely explored as OFDMA was introduced to Wi-Fi networks for the first time in 802.11ax. The marriage of these two techniques in Wi-Fi networks creates both opportunities and challenges in the practical design of MAC-layer protocols and algorithms to optimize airtime overhead, spectral efficiency, and computational complexity. In this paper, we present DeepMux, a deep-learning-based MU-MIMO-OFDMA transmission scheme for 802.11ax networks. DeepMux mainly comprises two components: deep-learning-based channel sounding (DLCS) and deep-learning-based resource allocation (DLRA), both of which reside in access points (APs) and impose no computational/communication burden on Wi-Fi clients. DLCS reduces the airtime overhead of 802.11 protocols by leveraging the deep neural networks (DNNs). It uses uplink channels to train the DNNs for downlink channels, making the training process easy to implement. DLRA employs a DNN to solve the mixed-integer resource allocation problem, enabling an AP to obtain a near-optimal solution in polynomial time. We have built a wireless testbed to examine the performance of DeepMux in real-world environments. Our experimental results show that DeepMux reduces the sounding overhead by 62.0% ~ 90.5% and increases the network throughput by 26.3% ~ 43.6%. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) and massive multi-input–multi-output (mMIMO) communications are the most key enabling technologies for next generation wireless networks to have large available spectrum and throughput. mMIMO is a promising technique for increasing the spectral efficiency of wireless networks, by deploying large antenna arrays at the base station (BS) and perform coherent transceiver processing. Implementation of mMIMO systems at mmWave frequencies resolve the issue of high path-loss by providing higher antenna gains. The motivation for this research work is that mmWave and mMIMO operations will be much more popular in 5G NR, considering the wide deployment of mMIMO in major frequency bands as per 3rd generation partnership project. In this paper, a downlink multi-user mMIMO (MU-mMIMO) hybrid beamforming communication system is designed with multiple independent data streams per user and accurate channel state information. It emphasizes the hybrid precoding at transmitter and combining at receiver of a mmWave MU-mMIMO hybrid beamforming system. Results of this research work give the tradeoff between multiple data streams per user and required number of BS antennas. It strongly recommends for higher number of parallel data streams per user in a mmWave MU-mMIMO systems to achieve higher order throughputs. [ABSTRACT FROM AUTHOR]

This paper considers simultaneous wireless information and power transfer enabled full-duplex multi-user multiple-input multiple-output cognitive networks. By taking into account imperfect channel state information (CSI) of the links toward primary users (PUs), this paper aims to simultaneously optimize two design objectives, namely the achievable sum-rate and sum harvested energy in the secondary network. Hence, the design problem is modelled as a multi-objective optimization problem (MOOP) subject to the transmit power constraint at the base station and robust harmful interference constraints at the PUs. Then, to find the Pareto set, the MOOP is rewritten to a single-objective problem (SOOP) by using the modified weighted Tchebycheff method. However, it is mathematically difficult to solve the transformed SOOP due to the non-concavity of the sum-rate function and the semi-infinite nature of the robust interference constraints. To overcome this challenge, we use the difference of two convex functions (DC) technique and S-procedure theory to recast the design optimization problem as the sequential convex programming. Various numerical simulations are conducted to illustrate the Pareto optimal solution sets, the robustness of interference constraints against CSI uncertainties, and trade-offs between the achievable sum-rate and harvested energy. [ABSTRACT FROM AUTHOR]

Massive MIMO is commonly described as a large number of base station (BS) antennas serving a smaller number of single-antenna users. However, adding a second antenna to the user handset opens the possibility to exploit multiplexing techniques and obtain higher throughput. This paper is based on a measurement campaign comprising a BS with 64 elements reconfigurable into three shapes: 1) a very large array with 6-m aperture; 2) a large array with 2m aperture; and 3) a compact two dimensional array with 25 cm by 28 cm sides. We study the throughput in single-user and multi-user scenarios using both non-linear optimal and linear precoders. The experimental results show that the throughput increases when adding a second antenna, but the increase is lower than in Gaussian channels due to the intra-user correlation. However, increasing the number of BS antennas, massive MIMO achieves more benefits from the second antenna. A large number of users in the system and the inter-user correlation reduce the benefits of a second antenna in the user handset.

Along with the proliferation of mobile devices and applications, IEEE 802.11 wireless local area network (WLAN) has become one of the most popular Internet access technologies. This leads to dense deployment of WLAN devices, and the calls for better user experience in such high-density WLAN environments are mounting. To keep pace with this increasing demand for high efficiency WLAN, IEEE 802.11ax, whose key advance is adopting uplink multi-user transmission (UL MU), is currently entering the final phase of standardization. The UL MU in 802.11ax WLAN, however, has a difficulty that stems from the intrinsically distributed nature of WLAN. Specifically, concurrent frames from multiple stations are loosely synchronous in time, thus resulting in symbol timing misalignment which leads to poor decoding performance. In this paper, we dissect the performance degradation caused by symbol timing misalignment and examine the required symbol timing conditions in receiving UL MU. Based on the analysis, we present a new two-step synchronization method to accommodate asynchronously arriving UL MU. We develop an 802.11ax link-level simulator and comparatively evaluate the proposed synchronization method via extensive simulations. Simulation results demonstrate that the proposed method successfully stabilizes UL MU data delivery even with large UL MU arrival timing deviations and the use of short cyclic prefix.

Large-Scale Antenna System (LSAS) has played an important role in the emerging fifth-generation mobile systems (5G) due to its potential for excellent spectral efficiency. However, it may cause a mass of pilot overhead that is not conducive to the application of LSAS in massive Machine-Type Communications (mMTC), one of three typical traffic modes of 5G. In this paper, we present a pilot-efficient scheduling strategy for mMTC systems, in which the Base Stations (BS) are equipped with large-scale antennas, from a cross-layer perspective. Our scheme can not only schedule the massive devices to access the spectrum, but also allocate the BS’ power without the need for much pilot overhead. More particularly, the users allowed to access the spectrum can be selected based on their queue state information without any channel estimations, while the power allocation only needs the channel estimations for scheduled users. We shall show the optimality of the presented policy based on the Lyapunov optimization theory. To solve the Lyapunov optimization problem, we present a low complexity two-layer iteration algorithm for more practical purposes. Simulation results demonstrate the substantial gain of our presented method over existing scheduling protocols of massive MIMO. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel non-iterative coordinated beamforming algorithm for overlaid multicell coordinated beamforming systems where a macro base station acts as an anchor and computes the beamforming vectors. We propose beamforming vector design that improves the received signal-to-interference-plus-noise-ratio while reducing the computational complexity. We also show its asymptotic behaviors with respect to the number of antennas, confirming that the proposed solutions are useful for large-scale multiple-input multiple-output systems in heterogeneous and small cell networks. [ABSTRACT FROM AUTHOR]

In large-array millimeter-wave (mmWave) systems, hybrid pre-coding is one of the most attractive research topics. This paper first presents a flexible adaptive overlapped subarray (OSA) architecture for the analog precoder, whose architecture can be adjusted by a connection network. An objective function is formulated to maximize the energy efficiency (EE) in consideration of the insertion loss for the proposed adaptive OSA based hybrid precoder. The optimal scheme is intractable to achieve, so that we present a heuristic hybrid pre-coding scheme, where the digital precoder is designed based on the zero-forcing (ZF) rule and the analog precoder is designed based on simplified EE optimization in consideration of the insertion loss. We discuss the effect of non-ideal factors on the EE performance, such as quantized phases, imperfect channel state information (CSI), and faulty switches. Simulation results show that, when the initial transmit power is large, our proposed scheme offers a better EE performance than the fully digital pre-coding scheme and many other popular hybrid pre-coding schemes. Moreover, it is found that faulty switches make the double-edged effect on the EE performance when the numbers of activated phase shifters and combiners are pre-defined. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we investigate the downlink achievable ergodic spectral efficiency (SE) of a single-cell multi-user millimeter wave system, in which a uniform rectangular array is used at the base station (BS) to serve multiple single-antenna users. We adopt a three-dimensional channel model by considering both the azimuth and elevation dimensions under single-path propagation. We derive the achievable ergodic SE for this system in with maximum ratio transmission precoding. This analytical expression enables the accurate and quantitative evaluation of the effect of the number of BS antennas, signal-to-noise ratio (SNR), and the crosstalk (squared inner product between different steering vectors) which is a function of the angles of departure (AoD) of users and the inter-antenna spacing. Results show that the achievable ergodic SE logarithmically increases with the number of BS antennas and converges to a value in the high SNR regime. To improve the achievable ergodic SE, we also propose a user scheduling scheme based on feedback of users’ AoD information and obtain the maximum achievable ergodic SE. Furthermore, we consider a dense user scenario where every user’s AoD becomes nearly identical and then derive the system’s minimum achievable SE.

In modern wireless communication systems, orthogonal pilot signals has been generally employed in estimation of the channel state information. However, orthogonal pilot signals are inadequate for supporting the rapidly increasing requirements of communication throughput for 5G-and-beyond wireless environments, owing to the pilot contamination and short coherence times in high-mobility situations. To address these concerns, we present a new strategy for making use of non-orthogonal pilot sequences in channel estimation for multi-cell massive multiple-input multiple-output systems. First, we extend prior pilot assignment strategies based on the orthogonality of pilots to the general case of non-orthogonal pilot signals. Based on the proposed non-orthogonal pilot assignment strategy, we establish the minimal pilot length that fulfills a requirement for the channel estimate error, under a given degree of non-orthogonality. Then, we demonstrate validity of the pilot assignment strategy with the minimal length, which maximizes the entire network throughput. Simulation results show that our proposed method gives a significantly enhanced performance in terms of the net throughput compared to that with orthogonal pilot sequences. The performance gain becomes particularly significant with a higher density of users or shorter coherence time intervals. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This letter investigates the throughput of a dense multi-antenna network in the presence of channel state information (CSI) errors at transmitter. The Gauss–Markov auto-regressive model is put forth to characterize CSI errors. We focus on a class of cases that the variance of CSI error is independent of serving distances between users and BSs. An original expression for area spectral efficiency (ASE) is derived with the tools of stochastic geometry. Based on the expectations of effective channel gains, a more tractable approximation for ASE is further obtained. With the help of the tractable approximation, we consider the optimization problem for ASE maximization with respect to the number of active users. We demonstrate that ASE scales linearly with the number of antennas when the number of antennas is larger than a certain value. The CSI errors determine the increasing slope. [ABSTRACT FROM AUTHOR]

The high density and heterogeneous mobility of users in many applications pose challenges for the channel acquisition in massive multiple-input-multiple-output (MIMO) systems. For such scenarios, we propose an intermittent channel estimation (ICE) scheme to save pilot resources, which utilizes the aged channel state information (CSI) based on the temporal correlations of user channels. The optimal CSI update pattern to maximize the achievable sum rate is obtained by solving a formulated multichain Markov decision process (MDP), which is denoted by ICE-MDP. Furthermore, to reduce the computational complexity of the MDP, we relax the constraint of the CSI update pattern design problem and convert it into a convex optimization problem, whose solution is denoted by ICE-CVX. The simulations validate the close-to-optimal performance and the computational efficiency of ICE-CVX and show that the ICE scheme can significantly outperform a conventional scheme which persistently updates the CSI of all users. [ABSTRACT FROM AUTHOR]

Multiple input multiple output (MIMO) systems have high potential to achieve maximum channel capacity in wireless communication systems. Multi-user (MU) MIMO network with transmit antennas T x at base station can schedule as many single antenna users in one time slot. This necessitates incorporation of a user scheduling strategy. Maximizing sumrate performance without under utilization of channel resources is a primary objective of such scheduling algorithms. Another key design factor for such techniques is to optimize fairness i.e. allowing equal opportunity to all communicating entities. In this paper, we present a simple approach to solve the scheduling problem in MU-MIMO systems while addressing the conflicting objectives of sumrate and fairness. Two variants of the proposed method are also discussed that provide options for maximizing the cumulative sumrate and improving the fairness. Simulations validate the proposed algorithm as a better choice over other conventional methods as it achieves optimal performance with relatively less computational complexity and without compromising fairness. Fairness results are verified by average sumrate method and Jain's Fariness Index. [ABSTRACT FROM AUTHOR]

In wireless local area networks (WLANs), the traditional carrier sense multiple access with collision avoidance (CSMA/CA) medium access control (MAC) protocol cannot use the full benefits from multiuser multiple-input multiple-output (MU-MIMO) technique due to random medium access of the users. In this paper, we propose a carrier sensing based MAC protocol for a MU-MIMO based WLAN with full utilization of MU-MIMO technique. By modeling the WLAN system under the proposed MAC protocol as a discrete time Markov chain, we develop an analytical model for computing the saturation throughput in presence of path loss, Rayleigh fading and log-normal shadowing. The analytical model is then validated via simulation. By means of numerical and simulation results, we demonstrate that the proposed MAC protocol significantly improves throughput performance than the traditional CSMA/CA MAC protocol. Further, we compare the performance of the proposed MAC protocol with a MU-MIMO MAC protocol called Uni-MUMAC protocol and find that the proposed MAC protocol performs better than the Uni-MUMAC protocol. We also explore the effect of some of the network and wireless channel parameters on the performance of the proposed MAC protocol. [ABSTRACT FROM AUTHOR]

In this paper, we present the first cross-layer analysis of wireless LANs operating under downlink multi-user multi-in multi-out (MU-MIMO), considering the fundamental role played by the closed-loop (TCP) traffic. In particular, we consider a scenario in which the access point transmits on the downlink via MU-MIMO, whereas stations must employ single-user transmissions on the uplink, as is the case in IEEE 802.11ac. With the help of analytical models built for different regimes that can occur in the considered system, we identify and explain crucial performance anomalies that can result in very low throughput in some scenarios, completely offsetting the theoretical gains achievable by MU-MIMO. We discuss solutions to mitigate the risk of this performance degradation and alternative uplink strategies allowing WLANs to approach their maximum theoretical capacity under MU-MIMO. [ABSTRACT FROM AUTHOR]

Channel state information (CSI) has been proposed to enhance physical layer security by functioning as a shared secret between a transmitter and a receiver, because it decorrelates over half wavelength distances and cannot be predicted based on locations of the transmitter and receiver in rich scattering environments. Consequently, CSI was employed to generate passwords, to authenticate the source of packets, and to inject artificial noise to thwart eavesdroppers. However, in this paper, we present CSIsnoop, and show that an attacker can infer CSI in a multi-user MIMO WLAN, even when both channel sounding sequences from the access point and CSI measurement feedback from the clients are encrypted during downlink (explicit) channel sounding, or when uplink (implicit) channel sounding is employed. The insights of CSIsnoop are that the CSI of clients can be computed based on transmit beamforming weights at the access point, and that the transmit beamforming weights can be estimated from downlink beamforming transmission. In other words, we reveal the fundamental conflict between using CSI to optimize PHY design by beamforming and ensuring the confidentiality of CSI. We implement CSIsnoop on a software defined radio and conduct experiments in various indoor environments. Our results show that on average CSIsnoop can infer CSI of the target client with an absolute normalized correlation of over 0.99, thereby urging reconsideration of the use of CSI as a tool to enhance physical layer security in multi-user MIMO WLANs. [ABSTRACT FROM AUTHOR]

In this paper, downlink (DL) precoding and power allocation strategies are identified for a time-division-duplex multi-cell multi-user massive full-dimension MIMO network. Utilizing channel reciprocity, DL channel state information feedback is eliminated and the DL multi-user MIMO precoding is linked to the uplink (UL) direction of arrival (DoA) estimation through the estimation of signal parameters via rotational invariance technique. Assuming non-orthogonal/non-ideal spreading sequences of the UL pilots, the performance of the UL DoA estimation is analytically characterized and the characterized DoA estimation error is incorporated into the corresponding DL precoding and power allocation strategy. The simulation results verify the accuracy of our analytical characterization of the DoA estimation and demonstrate that the introduced multi-user MIMO precoding and power allocation strategy outperforms the existing zero-forcing-based massive MIMO strategies. [ABSTRACT FROM AUTHOR]

Cellular base band units of the future are expected to reside in a cloud data center which provides computation resources, content storage and caching, and a natural place to perform multi-user precoding, thus addressing both cost and performance concerns of cellular systems. Multi-user precoding relies on efficient user grouping schemes to maximize multiplexing gains. However, traditional user grouping schemes are unaware of data center constraints, and may induce a large number of data transfers across racks when fetching requested data to a certain rack for precoding. When congestion occurs in the data center network, the delay of data transfers across racks may exceed the channel coherence time. This would kill multi-user MIMO transmissions as channel state information becomes outdated. In this paper, we design a novel data-locality-aware user grouping schemes which preferentially group users whose requested data are located under the same rack. We also design user grouping algorithms which adapt to the congestion level in the cloud data center. Specifically, a regularized spectral efficiency maximization problem is proposed where the number of data transfers across racks is introduced as a regularization term. By adjusting the weight of the regularization term according to the congestion level, we gradually suppress data transfers across racks in forming user groups when congestion occurs. We reduce the above problem to a soft-capacitated facility location problem, and we devise a 2-approximation user grouping algorithm. At last, we conduct simulations which show that our proposed algorithm performs close to the optimal in practical scenarios, and study the tradeoff between higher spectral efficiency and lower data transfer cost. [ABSTRACT FROM AUTHOR]

Recent studies have shown that carrier-aggregated multi-user (MU) multi-input-multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems can deliver unprecedented improvements in network throughput and capacity when compared with existing systems. However, most of these studies fail to take into account the nonlinear constraints that limit the performance of such systems. This paper proposes an analytical framework to evaluate the end-to-end performance of a carrier-aggregated dual-band (DB) MU-MIMO-OFDM system in the presence of transmitter nonlinearity. Specifically, we derive the closed-form expression for signal-to-interference-plus-noise ratio (SINR) and further utilize this expression to evaluate the average capacity and symbol error rate of a nonlinear DB MU-MIMO-OFDM system in a multi-path Rayleigh fading channel. We show that the transmit preprocessing technique conventionally used to mitigate inter-user interference in a linear DB MU-MIMO-OFDM system fails to eliminate inter-user interference in a nonlinear DB MU-MIMO-OFDM system. As a consequence, there is an irreducible error floor which is function of number of aggregated bands as well as number of users present in the system. The irreducible error floor has been shown to considerably deteriorate the performance of nonlinear DB MU-MIMO-OFDM system. Simulation results are also reported to validate the efficacy of the proposed analysis. [ABSTRACT FROM AUTHOR]

MU-MIMO transmission relies on exploiting multi-user diversity among a multitude of users. In this paper, to enhance MU-MIMO transmission, techniques for designing and exploiting reconfigurable antennas (RAs) are developed. A single-element RA is capable of generating modes with different radiation pattern and polarization states resulting in multiple different channel states for each user. This capability expands the search space for user-pairing optimization. To reduce complexity, an iterative user and antenna mode selection algorithm is proposed. Based on parasitic tuning, an RA optimized for MU-MIMO transmission and capable of creating four pattern types is designed using a genetic algorithm and full-wave electromagnetic analyses. Pattern- and channel-aware mode-set generation methods are developed, where mode groups are determined to reduce channel estimation overhead and mode selection complexity while achieving superior performance. Simulation results indicate that the proposed schemes need a lower number of users and/or relax user selection requirements to benefit from MU-MIMO transmissions. It is also seen that more legacy antennas are needed to achieve similar MU-MIMO performance provided by the RA system. Simulations show up to 16-dB signal-to-noise ratio gains for a single cell with 12 users, where the base station has four RAs and four single-antenna users are simultaneously served via MU-MIMO transmission. [ABSTRACT FROM AUTHOR]