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Multiple-input multiple-output (MIMO) and reconfigurable intelligent surfaces (RIS) enable network system designers to create intelligent, energy-efficient network systems. Higher-order beamforming gain through the deployment of multiple RISs ensures enhanced system performance. To achieve this, a cooperative design of passive beamforming is required. This paper presents RIS passive beamforming design for a double RIS-assisted multi-user multiple-input multiple-output (muMIMO) system. The authors explore machine learning (ML)-based techniques, specifically fully complex-multilayer perception (FC-MLP) and extreme learning machine (ELM), that do not require any prior knowledge of the mathematical formulations of the network system, for RIS passive beamforming design frameworks. The proposed ML-based frameworks are compared to the alternating optimization (AO) as well as to the deep deterministic policy gradient (DDPG)-based techniques in terms of average spectral efficiency (SE), as a function of transmit power, their configurations, RISs’ locations and number of RISs’ cells, as well as their computational complexities. The proposed ELM framework outperforms the AO and FC-MLP methods, and demonstrates descent performance when compared to the DDPG method.

Massive MIMO technology has significantly enhanced the performance of 5G networks, and its potential is poised for further expansion in the upcoming 6G standard. Large antenna arrays at the transmitter, combined with multiple antennas at each user can enable multi-user multi-layer MIMO data transmission. In this paper, we study the potential benefits of a cylindrical array arrangement in a 3D scenario, compared to the conventional trisector planar array configuration. In fact, cylindrical arrays allow for more uniform coverage of the cell, don’t suffer from beam broadening, and can mitigate the sector-edge problem. For this purpose, we assess the effectiveness of multi-user and multi-layer precoding and combining strategies using cylindrical arrays outfitted with both single and dual-polarized antenna elements, under the most recent 3GPP-compliant 3D channel model for 5G, and by taking into account impairments due to an imperfect channel estimation. Results and comparisons with planar arrays in terms of sum-rate, spectral efficiency and outage are provided for outdoor Line-of-Sight (LOS), outdoor Non-LOS, and indoor users. They highlight the benefits of using cylindrical arrays in realistic 3D scenarios, even when the planar arrays of the different sectors are coordinated to operate as a single array. This makes them an interesting solution for next-generation 6G networks.

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.

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.

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]

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]

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.

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]

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.

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]

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]

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 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]

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 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]

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]

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]

This paper focuses on the design of vector perturbation (VP) precoding for coordinated multi-point (CoMP) multi-user downlink transmission. Precoding is performed by individual base stations (BSs) in a distributed manner using only the downlink channel coefficients and user data local to a BS. A cascade precoder structure with an outer precoder managing the inter-cell interference (ICI) and an inner precoder performing mean-squared-error (MSE) minimization-based VP to mitigate the intra-cell interference is proposed. Three different outer precoding techniques are considered. In the first technique, the outer precoder is designed to fully eliminate the ICI by trading off the degrees of freedom (DoFs) available through multiple antennas. While the proposed technique outperforms existing conventional-VP based designs, a large portion of DoF is consumed by the ICI elimination. To overcome this issue, in the second technique, interference alignment-based outer precoding that minimizes the total leakage interference is proposed. To further improve the system performance, in the third approach, precoding by joint minimization of total leakage interference plus MSE is performed. Numerical results show that the proposed cascade precoding structure is an efficient way to use the DoF of CoMP multi-user downlink transmission.

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]

In this paper, we investigate the limitation of multi-user multiple-input multiple-output (MIMO) in ultra-dense networks (UDNs) and investigate how to overcome the limitation by designing efficient interference management strategies. Specifically, it is shown that the area spectral efficiency (ASE), an indicator to network capacity, would approach zero with over-deployed base stations (BSs) even when multi-user MIMO is applied. Worse still, it manifests that the multi-user gain of MIMO cannot be harvested in UDN due to the overwhelming interference. In particular, the maximal ASE is shown to be degraded by increasing the number of served users in each cell. To alleviate the bottleneck brought by interference, we have designed and optimized two simple but efficient BS activation policies. It is shown that the application of the optimized BS activation policy could improve network capacity scaling law and boost the potential of multi-user MIMO in UDN. Remarkably, the ASE is shown to increase with BS density $\lambda $ even when $\lambda $ and user density are sufficiently large. Moreover, the maximal ASE in the $\lambda \rightarrow \infty $ regime is shown to increase with the number of served users in each cell, which contradicts with the all-BS-on case. [ABSTRACT FROM AUTHOR]

In this paper, the problem of transmit beam design and user scheduling is investigated for multiuser (MU) multiple-input single-output (MISO) nonorthogonal multiple access (NOMA) downlink. First, Pareto-optimal beam design is solved for two-user MISO broadcast channels (BCs) with successive interference cancelation (SIC), and certain properties of Pareto-optimal beam vectors are obtained. Based on this result, a beam design and user scheduling method is proposed for MU-MISO NOMA. In the proposed method, simultaneously served users are grouped into multiple clusters with two users in each cluster, and a hierarchical beam structure composed of zero forcing intercluster beamforming and two-user Pareto-optimal intracluster beamforming is applied. The proposed method has the ability to control the rates of strong and weak users and hence provides great flexibility to MU-MISO NOMA operation. In addition, the problem of Pareto-optimal beam design for general $M$ -user MISO BCs with SIC is solved. [ABSTRACT FROM AUTHOR]

In this paper, a new two-phase-feedback-based user-scheduling-and-beamforming method is proposed for multi-user multiple-input multiple-output downlink in the context of two-stage beamforming. The key ideas of the proposed method are: 1) to use a set of orthogonal reference beams and construct a cone around each reference beam to select “nearly-optimal” semi-orthogonal users based only on channel quality indicator feedback; and 2) to apply post-user-selection beam design with zero-forcing beamforming (ZFBF) based on channel state information (CSI) feedback only from the selected users. It is proven that the proposed scheduling-and-beamforming method is asymptotically optimal as the number of users increase. Furthermore, the proposed scheduling-and-beamforming method almost achieves the performance of the existing semi-orthogonal user selection with ZFBF that requires full CSI for all users, with a significantly reduced amount of required channel information which is even less than that required by random beamforming. [ABSTRACT FROM PUBLISHER]

Vector perturbation (VP) is a nonlinear precoding technique that achieves near-capacity performance in multiuser multiple-input multiple-output systems at the expense of large complexity due to the search for the optimum perturbation vector. In this paper, we present the hybrid Tomlinson–Harashima VP (TH-VP) algorithm, a novel zero-forcing precoding scheme, which combines TH precoding to remove interuser interference, and VP precoding to equalize each user's multiple spatial streams. We show that the two nonlinear techniques can be integrated in a single optimization and that the proposed algorithm has lower computational requirements than any other. The performance of TH-VP is analyzed and simulation results show that TH-VP outperforms conventional zero-forcing VP and approaches the performance of dirty paper coding. [ABSTRACT FROM AUTHOR]

In this paper, an energy harvesting scheme for a multi-user multiple-input-multiple-output secrecy channel with artificial noise (AN) transmission is investigated. Joint optimization of the transmit beamforming matrix, the AN covariance matrix, and the power splitting ratio is conducted to minimize the transmit power under the target secrecy rate, the total transmit power, and the harvested energy constraints. The original problem is shown to be non-convex, which is tackled by a two-layer decomposition approach. The inner layer problem is solved through semi-definite relaxation, and the outer problem, on the other hand, is shown to be a single-variable optimization that can be solved by 1-D line search. To reduce computational complexity, a sequential parametric convex approximation method is proposed to find a near-optimal solution. This paper is then extended to the imperfect channel state information case with norm-bounded channel errors. Furthermore, tightness of the relaxation for the proposed schemes is validated by showing that the optimal solution of the relaxed problem is rank-one. Simulation results demonstrate that the proposed SPCA method achieves the same performance as the scheme based on 1-D but with much lower complexity. [ABSTRACT FROM PUBLISHER]

Pilot contamination, caused by the reuse of pilots among interfering cells, remains a significant obstacle that limits the performance of massive multi-input multi-output antenna systems. To handle this problem, less aggressive reuse of pilots involving allocation of additional pilots for interfering users is closely examined in this paper. Hierarchical pilot reuse methods are proposed, which effectively mitigate pilot contamination and increase the net throughput of the system. Among the suggested hierarchical pilot reuse schemes, the optimal way of assigning pilots to different users is obtained in a closed-form solution, which maximizes the net sum-rate in a given coherence time. Simulation results confirm that when the ratio of the channel coherence time to the number of users in each cell is sufficiently large, less aggressive reuse of pilots yields significant performance advantage relative to the case, where all cells reuse the same pilot set. [ABSTRACT FROM PUBLISHER]

It is known that superposition signaling in Gaussian interference networks is capable of improving the achievable rate region. However, the problem of maximizing the rate gain offered by superposition signaling is computationally prohibitive, even in the simplest case of two-user single-input single-output interference networks. This paper examines superposition signaling for the general multiple-input multiple-output broadcast Gaussian interference networks. The problem of maximizing either the sum rate or the minimal user’s rate under superposition signaling and dirty paper coding is solved by a computationally efficient path-following procedure, which requires only a convex quadratic program for each iteration but ensures convergence at least to a locally optimal solution. Numerical results demonstrate the substantial performance advantage of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, the performance of non-orthogonal multiple access (NOMA) is investigated and optimized in a downlink space division multiple access network with a multi-antenna base station and randomly deployed users, under a general channel state information (CSI) limited feedback framework. We first propose a dynamic user scheduling and grouping strategy by leveraging limited feedback. Based on that, an analytical framework is proposed to obtain the outage probability of the network in closed form. The diversity order and the impacts of the number of feedback bits on the outage performance of NOMA are analyzed. Furthermore, the net throughput, which captures the network-wide throughput with the uplink feedback cost considered, is maximized by optimizing the number of feedback bits. Numerical results are demonstrated to verify our analytical findings and show that different from the perfect CSI case, there always exists a performance floor of outage probability in the considered network due to limited feedback. Moreover, the optimal number of feedback bits for net throughput maximization increases as the channel coherence time becomes longer. [ABSTRACT FROM AUTHOR]

The constantly growing wireless bandwidth demand is pushing wireless networks to multi-tier architectures consisting of a macrocell tier and a number of dense small cell deployment tiers. In such a multi-tier multi-cell environment, the classic problem of associating users to base stations becomes both more challenging and more critical to the overall network performance. Most previous analytical work is focused on designing static user-cell association algorithms, which, to achieve optimality, are periodically applied whenever there are new user arrivals, thus potentially inducing a large number of re-associations for previously arrived users. On the other hand, practical online algorithms that do not allow any such user re-association are often based on heuristics and may not have any performance guarantees. In this paper, we propose online algorithms for the multi-tier multi-cell user association problem that have provable performance guarantees, which improve previously known bounds by a sizable amount. The proposed algorithms are motivated by online combinatorial auctions, while capturing and leveraging the relative sparsity of choices in wireless networks as compared with auction setups. Our champion algorithm is a \frac 12-a^-1 approximation algorithm, where $a$ is the maximum number of feasible associations for a user and is, in general, small due to path loss. Our analysis considers the state-of-the-art wireless technologies, such as massive and multiuser MIMO, and practical aspects of the system such as the fact that highly mobile users have a preference to connect to larger cell tiers to keep the signaling overhead low. In addition to establishing formal performance bounds, we also conduct simulations under realistic assumptions, which establish the superiority of the proposed algorithm over existing approaches under real-world scenarios. [ABSTRACT FROM AUTHOR]

We propose a new hybrid precoding technique for massive multi-input multi-output (MIMO) systems using spatial channel covariance matrices in the analog precoder design. Applying a regularized zero-forcing precoder for the baseband precoding matrix, we find an unconstrained analog precoder that maximizes signal-to-leakage-plus-noise ratio (SLNR) while ignoring analog phase shifter constraints. Subsequently, we develop a technique to design a constrained analog precoder that mimics the obtained unconstrained analog precoder under phase shifter constraints. The main idea is to adopt an additional baseband precoding matrix, which we call a compensation matrix. We analyze the SLNR loss due to the proposed hybrid precoding compared to fully digital precoding, and determine which factors have a significant impact on this loss. In the simulations, we show that if the channel is spatially correlated and the number of users is smaller than the number of RF chains, the SLNR loss becomes negligible compared to fully digital precoding. The main benefit of our method stems from the use of spatial channel matrices in such a way that not only is each user's desired signal considered, but also the inter-user interference is incorporated in the analog precoder design. [ABSTRACT FROM PUBLISHER]

Millimeter wave (mmWave) communications are an important candidate technique in 5G networks for features, supporting ultra-dense small cells and mobile data offloading. However, ultra-dense nodes and increasing data traffic bring in vast interference. This paper investigates low complexity non-iterative interference alignment (IA) schemes for multiple-input multiple-output (MIMO) interference broadcast channels in mmWave communications. The authors focus on the three-cell mobile network model in which each base station supports no more than two users within its cell. There is already a closed-form IA solution for the case that one cell has two users, while the other two have one user in each, which can be denoted as {2,1,1}. This paper considers different settings and proposes corresponding IA schemes. First, two IA schemes based on multi-step for the asymmetric setting {2,2,1} are presented, five degree of freedom (DoF) could be achieved. Then, for the symmetric setting {2,2,2}, a novel IA solution with lower complexity and a joint method combining IA with non-iterative multi-user MIMO technique are proposed, and they can achieve six DoF. The simulation results indicate that our non-iterative schemes have similar sum-rate capacity performances with the iterative ones in existing work, and the complexity is effectively reduced. [ABSTRACT FROM AUTHOR]

We design novel transmission strategies to maximize the energy efficiency (EE) of the uplink multi-user multiple-input and multiple-output relay channel. In this channel, $K$ multi-antenna users communicate with a multi-antenna base station (BS) through a multi-antenna relay. To achieve the goal of EE maximization, we propose new iterative algorithms to jointly optimize the multi-user precoder and the relay precoder under transmit power constraints for two cases. In the first case, the perfect global channel state information (CSI) is available, while in the second case, the CSI between the relay and the BS is imperfect. To surmount the non-convexity of our formulated EE optimization problems in both cases, we introduce the parameter subtractive function into the proposed algorithms. Then, the EE parameter in the parameter subtractive function is updated by Dinkelbach’s algorithm in the perfect CSI case, and by the bisection method in the imperfect CSI case. Moreover, in the perfect CSI case, the relay precoder is optimized by the diagonalization operation and the multi-user precoder is optimized based on the weighted minimum mean square error method. Differently, in the imperfect CSI case, we apply the sign-definiteness lemma to promote the semidefinite programming formulation of the EE optimization problem. Furthermore, we present the numerical results to demonstrate that our proposed iterative algorithms have a good convergence rate in both cases. In addition, we show that our proposed iterative algorithms achieve a higher EE performance than the existing algorithms in both CSI cases. [ABSTRACT FROM PUBLISHER]

This paper proposes a new frequency-domain pre-coding algorithm for the downlink multi-user MIMO system in a frequency-selective fading channel, where each user is equipped with multiple antennas. The proposed algorithm is based on the minimum sum-mean-square-error (Min-SMSE) criterion, which is able to eliminate both the multi-user and multi-antenna interferences, resulting in an improved bit error rate (BER) performance. Compared with the traditional optimal Min-SMSE algorithm, the proposed algorithm can reduce the computational complexity through simplifying the iterative and power allocation processes. Furthermore, the theoretical BER and ergodic sum-rate are derived in detail. In addition, we define and analyze the complexity of the pre-coding algorithms. Simulation results show that the proposed algorithm compares favorably with the optimal Min-SMSE and traditional block diagonalization (BD) algorithms in the sense of the ergodic sum-rate due to the approximately equal power allocation. The BER performance of the proposed algorithm is close to that of the optimal one and much better than that of the BD algorithm in both the uncorrelated and correlated channels. Moreover, the computational complexity of the sub-optimal Min-SMSE algorithm is significantly less than that of its optimal counterpart, especially in the low SNR region. [ABSTRACT FROM PUBLISHER]

Consumer electronic (CE) devices increasingly rely on wireless local area networks (WLANs). Next generation WLANs will continue to exploit multiple antenna systems to satisfy the growing need for WLAN system capacity. Multipleinput multiple-output (MIMO) antenna systems improve the spectral efficiency and single user throughput. Multi-user MIMO (MU-MIMO) systems exploit the spatial separation of users for increasing the sum-throughput. In an MU-MIMO system, efficient channel sounding is essential for achieving optimal performance. The system analysis in this paper provides insights into the rate at which to perform channel sounding. This paper shows that optimal sounding intervals exist for single user transmit beamforming (SU-TxBF) and MU-MIMO, and proposes a low-complexity dynamic sounding approach for practical MUMIMO WLAN deployments. The proposed approach adjusts the sounding interval adaptively based on the real-time learning outcomes in the given radio environment. Using real over-the-air channel measurements, significant throughput improvements (up to 31.8%) are demonstrated by adopting the proposed dynamic sounding approach, which is compliant with IEEE 802.11ac1. [ABSTRACT FROM PUBLISHER]

Compared with the well-known minimum mean-square error (mmse)-based turbo receivers, sorted QR decomposition (SQRD)-based data detection scheme has the advantages of lower complexity and better bit error rate (BER) due to the avoidance of matrix inversion and the use of successive interference cancelation. In this letter, we investigate SQRD-based data detection and soft interference cancelation in multi-user multiple-input multiple-output single-carrier frequency division multiple access system. We propose a new SQRD-based multi-user detection scheme by utilizing the a priori information from the channel decoder in the decomposition process through the use of a new extended channel matrix. The received signal vector for the current user’s data detection can be categorized into the expected signal, the interference, and the noise components, where the interference includes those from both the detected and the undetected users. Soft interference cancelation is then performed on the data. The residual interference from the detected users can be easily obtained due to the use of successive interference cancelation, whereas the interference from the undetected users can be obtained by using the a priori information from channel decoder. We further consider on how to extract the expected signal from the interference component, thus further improve the signal-to-interference-plus-noise ratio condition at the receiver. Simulation results show that the proposed scheme significantly improves the BER performance compared with the original SQRD-based multi-user detection scheme and the mmse-based turbo receivers. [ABSTRACT FROM PUBLISHER]

This letter investigates the area spectral efficiency (ASE) of a two-tier network with multi-antenna base stations (BSs). Based on a tractable approximation, some key properties of ASE are analytically revealed. We demonstrate that the additional deployment of small-cell BSs is efficient for improving ASE. For the purpose of ASE maximization, the optimal number of active users is derived for each tier, which has a linear relationship with the number of antennas. Furthermore, under the optimal condition, ASE will increase linearly with the number of antennas. Different from single-antenna networks, we observe that range expansion can have either a positive or a negative effect on ASE. [ABSTRACT FROM PUBLISHER]