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The papers in this special section focuses on signal processing advances in wireless transmission of power and information. Wireless power transfer (WPT) and wireless information and power transfer (WIPT) have received growing attention in the research community in the past few years. In this special issue, a total of fourteen papers present state-of-the-art results in the broad area of wireless transmission of information and power with a special emphasis on signal processing advances. The special issue starts with a guest editor-authored tutorial overview paper that reviews the signal processing, machine learning, sensing, and computing techniques, challenges and opportunities in future networks based on WPT and WIPT. [ABSTRACT FROM AUTHOR]

For high‐resolution radar, traditional direction of arrival (DOA) angle measuring algorithms for point‐like targets cannot be directly applied to extended targets. Instead, pixel‐based detection and clustering must be performed first, followed by angle estimation. However, the presence of factors such as clutter and system noise makes it impossible to guarantee a one‐to‐one correspondence between the multiple scattering points of an extended target and the real target, which may result in significant discrepancies between the angle estimation and localization results and the real target. To address this issue, this paper proposes and discusses a radar extended target arrival angle estimation method based on effective phase clustering. The proposed method selects effective target scatterers through the interferometric phase information between channels, clusters these points, and then estimates the DOA angle of the extended target, effectively reducing the impact of interference scatterers on DOA angle estimation and improving the angular measurement accuracy. The effectiveness of the proposed method was validated by the measured data obtained from an airborne digital beamforming radar system. [ABSTRACT FROM AUTHOR]

The scientific world is becoming more open to the public and fellow researchers. Open access publishing is becoming accepted, even if some publishers are resisting. The next step is the open code and data paradigm, which was briefly discussed in the "From the Editor" column in the November 2018 issue of IEEE Signal Processing Magazine (SPM) [1]. In this column, I follow up on this topic by sharing my experiences, best practices, and thoughts about reproducible research. [ABSTRACT FROM AUTHOR]

Recently, the first version of the fifth-generation (5G) new radio (NR) standard with massive multiple-input multiple-output (MIMO) has been finished by the 3rd Generation Partnership Project (3GPP), with initial deployments occurring in 2018. Despite the major advances in 5G, there are still many challenges remaining. 6G and beyond will require even higher data rates, lower latencies, better energy efficiency, and improved robustness. Multiple antenna technologies, which have played important roles in nearly all recent wireless standards, will be key to addressing these challenges. MIMO research continues to evolve, and new MIMO research topics such as enhanced massive MIMO techniques and array architectures hold much potential for 6G and beyond. Cell-free massive MIMO utilize a large number of distributed access points (APs) that jointly serve users in a coordinated fashion, using only local channel state information at each AP. While the performance of cell-free massive MIMO can be analyzed using a similar methodology as in cellular massive MIMO, the fundamental limits, signal processing, and resource allocation are substantially different. In order to reduce the hardware cost and energy consumption in millimeter wave (mmWave) massive MIMO systems, beamspace MIMO has been proposed to significantly reduce the number of required radio-frequency (RF) chains by using lens antenna arrays or phase shifters. Alternatively, the intelligent reflecting surface (IRS) concept involves electromagnetically controllable surfaces that can be integrated into large-scale infrastructure such as building walls, airports, and stadiums. There are active and partially passive forms of large intelligent surface (LIS), and variants with either large antenna spacing or continuous aperture. There are also some substantial differences between the new multiple antenna technologies and traditional MIMO systems, such as transceiver design and propagation models. This special issue aims to highlight recent research on multiple antenna technologies. [ABSTRACT FROM AUTHOR]

This paper studies the downlink of a cloud radio access network (C-RAN) in which a centralized processor (CP) communicates with mobile users through base stations (BSs) that are connected to the CP via finite-capacity fronthaul links. Information theoretically, the downlink of a C-RAN is modeled as a two-hop broadcast-relay network. Among the various transmission and relaying strategies for such model, this paper focuses on the compression strategy, in which the CP centrally encodes the signals to be broadcast jointly by the BSs, then compresses and sends these signals to the BSs through the fronthaul links. We characterize an achievable rate region for a generalized compression strategy with Marton’s multicoding for broadcasting and multivariate compression for fronthaul transmission. We then compare this rate region with the distributed decode-forward (DDF) scheme, which achieves the capacity of the general relay networks to within a constant gap, and show that the difference lies in that DDF performs Marton’s multicoding and multivariate compression jointly as opposed to successively as in the compression strategy. A main result of this paper is that under the assumption that the fronthaul links are subject to a sum capacity constraint, this difference is immaterial; so, for the Gaussian network, the compression strategy based on successive encoding can already achieve the capacity region of the C-RAN to within a constant gap, where the gap is independent of the channel parameters and the power constraints at the BSs. As a further result, for C-RAN under individual fronthaul constraints, this paper also establishes that the compression strategy can achieve to within a constant gap to the sum capacity. [ABSTRACT FROM AUTHOR]

This paper considers the downlink of a cognitive radio (CR) network formed by multiple primary and secondary transmitters, where each multiantenna transmitter serves a preknown set of single-antenna users. This paper assumes that the secondary and primary transmitters can simultaneously transmit their data over the same frequency bands to achieve high system spectrum efficiency. This paper considers the downlink balancing problem of maximizing the minimum signal-to-interference-plus-noise ratio (SINR) of the secondary transmitters subject to both the total power constraint of the secondary transmitters and the maximum interference constraint at each primary user due to secondary transmissions. This paper proposes solving the problem using the alternating direction method of multipliers, which leads to a distributed implementation through limited information exchange across the coupled secondary transmitters. This paper additionally proposes a solution that guarantees feasibility at each iteration. Simulation results demonstrate that the proposed solution converges to the centralized solution in a reasonable number of iterations. [ABSTRACT FROM AUTHOR]

This paper presents a wideband signal (WS) beamforming method based on Inception convolutional neural network (ICNN), named as WS-ICNN algorithm. Firstly, an Inception module is constructed via some convolutional layers with feature maps of different sizes and a pooling layer. It can not only extract different scale information of covariance matrix, but also excavate the spatial correlation information about received wideband signals, so that the inception module can help neural network improve the beamforming output performance. On this basis, an ICNN model is established, which is suitable for wideband beamforming. Then, in order to obtain a good training label for the proposed ICNN model, a taper matrix and a second-order cone programming problem are introduced to calculate a wideband beamforming weight vector label. Based on this label, the training process of the proposed ICNN model is accomplished. Finally, the well-trained ICNN model accepts the input of the covariance matrix, and output the beamforming weight vector. Simulation results demonstrate the performance of the proposed algorithm in the cases of direction-of-arrival estimation error and sensor position error. [ABSTRACT FROM AUTHOR]

Providing higher precision Direction of Arrival (DOA) estimation has become a hot topic in the field of array signal processing for parameter estimation in recent years. However, when the physical aperture of the actual array is small, its aperture limitation means that even with super-resolution estimation algorithms, the achievable estimation precision is limited. This paper takes a novel approach by constructing an optimization algorithm using the covariance fitting criterion based on the array output's covariance matrix to fit and obtain the covariance matrix of a large aperture virtual array, thereby providing high-precision angular resolution through virtual aperture expansion. The covariance fitting expansion analysis and discussion are unfolded for both uniform linear arrays (ULAs) and sparse linear arrays (SLAs) under four different scenarios. Theoretical analysis and simulation experiments demonstrate that these methods can enhance the effective performance of angle estimation, especially in low signal-to-noise ratios (SNRs) and at small angular intervals by fitting virtual extended aperture data. [ABSTRACT FROM AUTHOR]

The papers in this special issue focus on array signal processing for angular models in massive MIMO communications. In recent years, there has been tremendous research progress in massive MIMO from communications theory viewpoint. An emerging trend is to explore the angular models of the propagation channels and design the massive system with array signal processing techniques. Particularly, fundamental concepts in array signal processing (e.g., direction of arrival/departure - DOA/DOD), can be conceptualized and exploited. [ABSTRACT FROM AUTHOR]

In array processing, sparse arrays are capable of resolving $\mathcal {O}(N^2)$ uncorrelated sources with $N$ sensors. Sparse arrays have this property because they possess uniform linear array (ULA) segments of size $\mathcal {O}(N^2)$ in the difference coarray, defined as the differences between sensor locations. However, the coarray structure of sparse arrays is susceptible to sensor failures and the reliability of sparse arrays remains a significant but challenging topic for investigation. In the companion paper, a theory of the $k$ -essential family, the $k$ -fragility, and the $k$ -essential Sperner family were presented not only to characterize the patterns of $k$ faulty sensors that shrink the difference coarray, but also to provide a number of insights into the robustness of arrays. This paper derives closed-form characterizations of the $k$ -essential Sperner family for several commonly used array geometries, such as ULA, minimum redundancy arrays (MRA), minimum holes arrays (MHA), Cantor arrays, nested arrays, and coprime arrays. These results lead to many insights into the relative importance of each sensor, the robustness of these arrays, and the DOA estimation performance in the presence of sensor failure. Broadly speaking, ULAs are more robust than coprime arrays, while coprime arrays are more robust than maximally economic sparse arrays, such as MRA, MHA, Cantor arrays, and nested arrays. [ABSTRACT FROM AUTHOR]

This paper provides an analytical performance characterization of both the uplink (UL) and downlink (DL) user-centric network multiple-input multiple-output (MIMO) systems, where a cooperating base station (BS) cluster is formed for each user individually and the clusters for different users may overlap. In this model, cooperating BSs (each equipped with multiple antennas) jointly perform zero-forcing beamforming to the set of single-antenna users associated with them. As compared with a baseline network MIMO system with disjoint BS clusters, the effect of user-centric clustering is that it improves signal strength in both the UL and DL, while reducing cluster-edge interference in the DL. This paper quantifies these effects by assuming that BSs and users form Poisson point processes and by further approximating both the signal and interference powers using Gamma distributions of appropriate parameters. We show that BS cooperation provides significant gain as compared to single-cell processing for both the UL and DL, but the advantage of user-centric clustering over the baseline disjoint clustering system is significant for the DL cluster-edge users only. Although the analytic results are derived with the assumption of perfect channel state information and infinite backhaul between the cooperating BSs, they nevertheless provide architectural insight into the design of the future cooperative cellular networks. [ABSTRACT FROM AUTHOR]

Interference management is a fundamental issue in device-to-device (D2D) communications whenever the transmitter-and-receiver pairs are located in close proximity and frequencies are fully reused, so active links may severely interfere with each other. This paper devises an optimization strategy named FPLinQ to coordinate the link scheduling decisions among the interfering links, along with power control and beamforming. The key enabler is a novel optimization method called matrix fractional programming (FP) that generalizes previous scalar and vector forms of FP in allowing multiple data streams per link. From a theoretical perspective, this paper provides a deeper understanding of FP by showing a connection to the minorization-maximization (MM) algorithm. From an application perspective, this paper shows that as compared to the existing methods for coordinating scheduling in the D2D network, such as FlashLinQ, ITLinQ, and ITLinQ+, the proposed FPLinQ approach is more general in allowing multiple antennas at both the transmitters and the receivers, and further in allowing arbitrary and multiple possible associations between the devices via matching. Numerical results show that FPLinQ significantly outperforms the previous state-of-the-art in a typical D2D communication environment. [ABSTRACT FROM AUTHOR]

The performance of cloud radio access network (C-RAN) is limited by the finite capacities of the backhaul links connecting the centralized processor (CP) with the base-stations (BSs), especially when the backhaul is implemented in a wireless medium. This paper proposes the use of wireless multicast together with BS caching, where the BSs pre-store the contents of popular files, to augment the backhaul of C-RAN. For a downlink C-RAN consisting of a single cluster of BSs and wireless backhaul, this paper studies the optimal cache size allocation strategy among the BSs and the optimal multicast beamforming transmission strategy at the CP such that the user’s requested messages are delivered from the CP to the BSs in the most efficient way. We first state a multicast backhaul rate expression based on a joint cache-channel coding scheme, which implies that larger cache sizes should be allocated to the BSs with weaker channels. We then formulate a two-timescale joint cache size allocation and beamforming design problem, where the cache is optimized offline based on the long-term channel statistical information, while the beamformer is designed during the file delivery phase based on the instantaneous channel state information. By leveraging the sample approximation method and the alternating direction method of multipliers, we develop efficient algorithms for optimizing the cache size allocation among the BSs, and quantify how much more caches should be allocated to the weaker BSs. We further consider the case with multiple files having different popularities and show that it is in general not optimal to entirely cache the most popular files first. Numerical results show considerable performance improvement of the optimized cache size allocation scheme over the uniform allocation and other heuristic schemes. [ABSTRACT FROM AUTHOR]

The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive noise. To address this challenge, this paper introduces a novel DOA estimation algorithm that leverages mixed fractional lower-order correntropy (MFLOCR) in the context of Alpha-stable distributed impulsive noise. Correntropy is used as a measure of the similarity of the signals, using a Gaussian function to smooth extreme values and provide greater robustness against impulsive noise. By utilizing diverse kernel lengths to jointly regulate the kernel function, the concept of correntropy is expanded and implemented in the fractional lower-order moment (FLOM) algorithm for received signals. Subsequently, the MFLOCR is derived by adjusting the resulting form of correntropy. Finally, an enhanced DOA estimation algorithm is proposed that combines the MFLOCR operator with the MUSIC algorithm, specifically tailored for impulsive noise environments. Furthermore, a proof of boundedness is provided to validate the effectiveness of the proposed approach in such noisy conditions. Simulation experiments confirmed that the proposed method outperforms existing DOA estimation methods in the context of intense impulsive noise, a low generalized signal-to-noise ratio (GSNR), and a smaller number of snapshots. [ABSTRACT FROM AUTHOR]

The 46 papers focusing on the theme of "Theories and Methods for Advanced Wireless Relays" have been divided into two groups to be published in two separate issues. This first issue includes 23 papers on relay performance bound, MIMO relay beamforming, relay channel estimation, two-way and shared relays, full-duplex relays, and security for relay networks. The second issue includes papers on coding for relay networks, medium access control for relays, implementation ans system performance studies. [ABSTRACT FROM PUBLISHER]

This paper has two main objectives. First, it describes the practical challenges of field trials and proposes a developed test method. Second, the test method is used to compare the uplink (UL) performance with different antenna technologies when user equipment (UE) does not have a line of sight (LOS) to the evolved node B. Both passive and active antenna configurations were used in the performance evaluation. Modern cellular networks have high demands for capacity, reliability, and availability. The verification of a network’s configuration and technological features is essential to guarantee network performance, and the performance of a network must be verified by the laboratory testing or field trials; such trials produce the experimental knowledge of technology features and configurations. Technological and environmental factors must also be considered before performing mobile network field testing. This paper showed that moving UE produces more reliable and repeatable results than measurements with stationary UE. Our antenna configuration comparison study revealed that in the UL direction, active antenna system beam control could significantly increase the UL capacity in non-LOS conditions. [ABSTRACT FROM AUTHOR]

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE. [ABSTRACT FROM AUTHOR]

The compressive sensing method is an effective way of under-sampling array signal processing, and the measurement matrix is a key technology of compressive sensing processing, which is of decisive significance to improve the performance of under-sampling array signal processing. In this paper, an under-sampling array signal processing method based on an improved Hadamard measurement matrix was proposed. This method improved the construction of the measurement matrix by a compressing zero method capable of reducing the redundant data, enhancing the non-correlation between columns, improving the RIP condition, and improving the performance of the compressive sensing method. The performance was verified by simulation and real data from the lake, and the results showed that the method proposed in this paper has obvious performance advantages compared with the Toeplitz Circular Matrix, the m-sequence-based matrix, and the partial Hadamard matrix under the same conditions. [ABSTRACT FROM AUTHOR]

As revolutionary technologies that can actively change the communication link signal, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have emerged as reliable, economical and convenient wireless communication solutions for a variety of practical scenarios. Therefore, this paper focuses on an IRS empowered UAV downlink communication network, where the dynamic UAV establishes a cascade link via IRS to provide signal enhancement services for multiple users. Considering constraints of transmit power, flight speed and area at the UAV and the reflecting constraints at the IRS, the block coordinate descent (BCD) method based on resource allocation, reflecting design and trajectory optimization is adopted to maximize the sum-rate of all users. The proposed problem is converted by using quadratic transformation and Lagrangian dual transformation. Then applying for the approximate linear method and Iterative Rank Minimization (IRM) to optimize the transmit power of UAV and phase shift of IRS respectively. Since additional reflection propagation paths by IRS, the complexity of the channel model makes the trajectory design difficult. To tackle this problem, this paper proposes a UAV trajectory optimization method based on enhanced reinforcement learning with the fixed initial location and destination. In the end, the convergence of the proposed scheme is effectively verified by simulations. Moreover, abundant simulation comparisons between the proposed scheme and other benchmark schemes demonstrate the validity and high performance gains of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper studies the outage capacity of a network consisting of a multitude of heterogeneous mobile users and operating according to the classical opportunistic beamforming framework. The base station is located at the center of the cell, which is modeled as a disk of finite radius. The random user locations are modeled using a homogeneous spatial Poisson point process. The received signals are impaired by both fading and location dependent path loss. For this system, we first derive an expression for the beam outage probability. This expression holds for all path loss models that satisfy some mild conditions. Then, we focus on two specific path loss models (i.e., an unbounded model and a more realistic bounded one) to illustrate the applications of our results. In the large system limit, where the cell radius tends to infinity, the beam outage capacity and its scaling behavior are derived for the selected specific path loss models. This paper also studies opportunistic schemes that achieve fairness among the heterogeneous users. Numerical evaluations are performed to give further insights and to illustrate the applicability of the outage capacity results even to a cell having a small finite radius. [ABSTRACT FROM PUBLISHER]

Massive MIMO and millimeter-wave communication (mmWave) have recently emerged as two key technologies for building 5G wireless networks and beyond. To reconcile the conflict between the large antenna arrays and the limited amount of radio-frequency (RF) chains in mmWave systems, the so-called hybrid beamforming becomes a promising solution and has received a great deal of attention in recent years. However, existing research on hybrid beamforming focused mostly on the physical layer or signal processing aspects. So far, there is a lack of theoretical understanding of how hybrid beamforming could affect mmWave network optimization. In this paper, we consider the impacts of hybrid beamforming on utility-optimality and queuing delay in mmWave cellular network optimization. Our contributions in this paper are three-fold: i) we develop a joint hybrid beamforming and congestion control algorithmic framework for mmWave network utility maximization; ii) we reveal a pseudoconvexity structure in the hybrid beamforming scheduling problem, which leads to simplified analog beamforming protocol design; and iii) we theoretically characterize the scalings of utility-optimality and delay with respect to channel state information (CSI) accuracy in digital beamforming. [ABSTRACT FROM AUTHOR]

This paper presents a modified orthogonal propagator method (OPM) with spline interpolation for direction of arrival (DoA) estimation. The conventional OPM is based on the noise-free signal model. When noise exists, the main contribution of noise (additive temporally and spatially white Gaussian noise) is in the diagonal elements of the data covariance matrix. Therefore, the influence of noise can be minimized by restoring the noise free diagonal elements through an interpolation procedure and the propagator can then be directly extracted from the denoised data covariance matrix, which is the main idea of the proposed method in this paper. The performance of the proposed modified OPM is tested through numerical simulations. Simulation results show that the proposed modified OPM achieves low root-mean square errors (RMSEs) with small computational load in DoA estimation. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning approach to the adaptive and sequential beamforming design problem for the initial access phase in a mmWave environment with a single-path channel. For a single-user scenario where the problem is equivalent to designing the sequence of sensing beamformers to learn the angle of arrival (AoA) of the dominant path, we propose a novel deep neural network (DNN) that designs the adaptive sensing vectors sequentially based on the available information so far at the base station (BS). By recognizing that the AoA posterior distribution is a sufficient statistic for solving the initial access problem, we use the posterior distribution as the input to the proposed DNN for designing the adaptive sensing strategy. However, computing the posterior distribution can be computationally challenging when the channel fading coefficient is unknown. To address this issue, this paper proposes to use an estimate of the fading coefficient to compute an approximation of the posterior distribution. Further, this paper shows that the proposed DNN can deal with practical beamforming constraints such as the constant modulus constraint. Numerical results demonstrate that compared to the existing adaptive and non-adaptive beamforming schemes, the proposed DNN-based adaptive sensing strategy achieves a significantly better AoA acquisition performance. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is capable of intelligently manipulating the phases of the incident electromagnetic wave to improve the wireless propagation environment between the base-station (BS) and the users. This paper addresses the joint user scheduling, RIS configuration, and BS beamforming problem in an RIS-assisted downlink network with limited pilot overhead. We show that graph neural networks (GNN) with permutation invariant and equivariant properties can be used to appropriately schedule users and to design RIS configurations to achieve high overall throughput while accounting for fairness among the users. As compared to the conventional methodology of first estimating the channels then optimizing the user schedule, RIS configuration and the beamformers, this paper shows that an optimized user schedule can be obtained directly from a very short set of pilots using a GNN, then the RIS configuration can be optimized using a second GNN, and finally the BS beamformers can be designed based on the overall effective channel. Numerical results show that the proposed approach can utilize the received pilots more efficiently than the conventional channel estimation based approach, and can generalize to systems with an arbitrary number of users. [ABSTRACT FROM AUTHOR]

In this paper, we study the waveform and passive beamforming design for intelligent reflecting surface (IRS)-aided wireless power transfer (WPT). Generalized multi-user and low complexity single-user algorithms are demonstrated based on alternating optimization (AO) framework to maximize the weighted sum output direct current (DC), subject to the transmit power constraints and passive beamforming modulus constraints. The input signal waveform and IRS passive beamforming phase shifts are jointly designed as a function of users’ individual frequency-selective channel state information (CSI). The energy harvester nonlinearity is explored and two IRS deployment schemes, namely frequency selective IRS (FS-IRS) and frequency flat IRS (FF-IRS), are modeled and analyzed. This paper highlights the fact that IRS can provide an extra passive beamforming gain on output DC power over conventional WPT designs and significantly influence the waveform design by leveraging the benefit of passive beamforming, frequency diversity and energy harvester nonlinearity. Even though FF-IRS exhibits lower output DC than the ideal FS-IRS, it still achieves substantially increased DC power over conventional WPT designs. Performance evaluations confirm the significant benefits of a joint waveform and passive beamforming design accounting for the energy harvester nonlinearity to boost the performance of single-user and multi-user WPT systems. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is a newly emerged technology that can increase the energy and spectral efficiency of wireless communication systems. This paper considers an IRS-aided multi-user multiple-input single-output (MISO) communication system, and presents a detailed analysis and optimization framework for the weighted sum-rate (WSR) of the downlink transmission over Rician fading channels. Unlike most of the prior works where the active beamformer at the base station (BS) and passive beamformer at the IRS are jointly designed based on the instantaneous channel state information (CSI), this paper proposes a low-complexity transmission protocol where the IRS passive beamforming and BS power allocation coefficient vector are optimized in the large timescale based on the statistical CSI, and the BS transmit beamforming is designed in the small timescale based on only the instantaneous CSI of the effective BS-user channels. Therefore, the channel training overhead in each channel coherence interval under our proposed protocol is independent of the number of IRS reflecting elements, which is in sharp contrast to most of the prior works. By considering maximum-ratio transmit beamforming at the BS, we derive a lower bound of the ergodic WSR in closed-form. Then, we propose an efficient algorithm to jointly optimize the IRS passive beamforming and BS power allocation coefficient vector for maximizing the ergodic WSR lower bound. Numerical results validate the tightness of our derived WSR bound and show that the proposed scheme outperforms various existing schemes in terms of complexity or capacity performance. [ABSTRACT FROM AUTHOR]

This two-part paper aims to quantify the cost of device activity detection in an uplink massive connectivity scenario with a large number of devices but device activities are sporadic. Part I of this paper shows that in an asymptotic massive multiple-input multiple-output (MIMO) regime, device activity detection can always be made perfect. Part II of this paper subsequently shows that despite the perfect device activity detection, there is nevertheless significant cost due to device detection in terms of overall achievable rate, because of the fact that nonorthogonal pilot sequences have to be used in order to accommodate the large number of potential devices, resulting in significantly larger channel estimation error as compared to conventional massive MIMO systems with orthogonal pilots. Specifically, this paper characterizes each active user's achievable rate using random matrix theory under either maximal-ratio combining (MRC) or minimum mean-squared error (MMSE) receive beamforming at the base station (BS), assuming the statistics of their estimated channels as derived in Part I. The characterization of user rate also allows the optimization of pilot sequences length. Moreover, in contrast to the conventional massive MIMO system, the MMSE beamforming is shown to achieve much higher rate than the MRC beamforming for the massive connectivity scenario under consideration. Finally, this paper illustrates the necessity of user scheduling for rate maximization when the number of active users is larger than the number of antennas at the BS. [ABSTRACT FROM AUTHOR]

Angle-based localization of a source at unique coordinates in the three-dimensional (3-D) space utilizes traditionally the two-dimensional (2-D) angle of arrival (AOA) measurements from planar arrays. This paper investigates the positioning performance and develops an optimal solution for the 3-D localization using one-dimensional (1-D) space angle (SA) measurements that has the appeal of involving linear arrays only. The localization performance by SA is less understood and the positioning algorithms from the literature are suboptimal and have restrictions on the altitudes and orientations of the linear array receivers. This paper establishes the basic concept of using SA for localization, and conducts analytical comparison of SA and AOA positionings by cross arrays, where the contrasts in the angle observation accuracy, the geometric dilution effect and overall localization performance are elaborated. A solution that can reach the Cramér-Rao Lower Bound performance under Gaussian noise is also proposed that does not have restriction on the placement of the linear array receivers. It consists of an initial solution by semidefinite relaxation and a refined solution by an algebraic estimator. Simulations validate the theoretical investigation and the algorithm performance. [ABSTRACT FROM AUTHOR]

In concurrent spectrum sharing scenarios between radars and wireless communication systems, one strict approach to protect the radar from undesired interference is locating the base stations outside a guard (or exclusion) zone of the radar. As a result, users inside that zone experience bad communication coverage. This paper uses Reconfigurable Intelligent Surfaces (RISs) to improve the base station coverage of users in the radar exclusion zone. The paper shows, through analysis and simulation, that joint optimization of base station and RIS locations improves the probability of coverage in the exclusion zone over the shared spectrum without disturbing the radar operation. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning approach to a class of active sensing problems in wireless communications in which an agent sequentially interacts with an environment over a predetermined number of time frames to gather information in order to perform a sensing or actuation task for maximizing some utility function. In such an active learning setting, the agent needs to design an adaptive sensing strategy sequentially based on the observations made so far. To tackle such a challenging problem in which the dimension of historical observations increases over time, we propose to use a long short-term memory (LSTM) network to exploit the temporal correlations in the sequence of observations and to map each observation to a fixed-size state information vector. We then use a deep neural network (DNN) to map the LSTM state at each time frame to the design of the next measurement step. Finally, we employ another DNN to map the final LSTM state to the desired solution. We investigate the performance of the proposed framework for adaptive channel sensing problems in wireless communications. In particular, we consider the adaptive beamforming problem for mmWave beam alignment and the adaptive reconfigurable intelligent surface sensing problem for reflection alignment. Numerical results demonstrate that the proposed deep active sensing strategy outperforms the existing adaptive or nonadaptive sensing schemes. [ABSTRACT FROM AUTHOR]

As more and more people choose high-speed rail (HSR) as a means of transportation for short trips, there is ever growing demand of high quality of multimedia services. With its rich spectrum resources, millimeter wave (mm-wave) communications can satisfy the high network capacity requirements for HSR. Also, it is possible for receivers (RXs) to be equipped with antenna arrays in mm-wave communication systems due to its short wavelength. However, as HSRs run with high speed, the received signal power (RSP) varies rapidly over a cell and it is the lowest at the edge of the cell compared to other locations. Consequently, it is necessary to conduct research on RX beamforming for HSR in mm-wave band to improve the quality of the received signal. In this paper, we focus on RX beamforming for a mm-wave train-ground communication system. To improve the RSP, we propose an effective RX beamforming scheme based on deep reinforcement learning (DRL), and develop a deep Q-network (DQN) algorithm to train and determine the optimal RX beam direction with the purpose of maximizing average RSP. Through extensive simulations, we demonstrate that the proposed scheme has better performance than the four baseline schemes in terms of average RSP at most positions on the railway. [ABSTRACT FROM AUTHOR]

A sensor array collects spatial samples of propagating wave fields, and a beamformer performs spatial filtering to preserve the desired signal while suppressing interfering signals and noise arriving from directions other than the direction of interest. Given a signal with wideband frequency, using a uniform array is one of the most common approaches to obtain broadband beamforming. In this work, a function formulating the relations between the sensor coefficients and its beam pattern over frequency is introduced. The function is called inverse beam pattern transform. The inverse beam pattern transform mainly contains the coordinate transform and inverse Fourier transform. From the view of spatial aliasing, the inter-distance of the sensors should be less than half of the minimum wavelength of the signal. However, from the bijection of the new function and broadband beamforming perspective, this paper proposes the other lower and upper bounds for the inter-distance. Within these bounds, the new function is the bijective function which can be applied to design the uniform array with broadband beamforming. [ABSTRACT FROM AUTHOR]

Massive Multi-Input Multi-Output (MIMO) and Coordinated MultiPoint (CoMP) technologies in Cloud-RAN (C-RAN) architecture become inevitable trend due to the advent of next-generation mobile applications, which are traffic-intensive, such as ultra high definition (UHD) video. In this paper, we study a joint beam activation and user scheduling problem in a 5G cellular network with massive MIMO and CoMP utilizing orthogonal random beamforming technique. This paper aims to minimize total Remote Radio Heads’ (RRHs’) energy expenditure in a dynamic C-RAN architecture while ensuring finite service time for all user traffic arrivals in the communication coverage. We leverage Lyapunov drift-plus-penalty framework to transform an original long-term average problem into a series of per-slot modified problems. Since the provided per-slot problem is combinatorial and nonlinear optimization problem, we are inspired by a greedy algorithm to design energy and delay guaranteed joint beam activation and user scheduling policy, namely BEANS. We prove that the proposed BEANS ensures finite upper bounds of average RRH energy consumption and average queue backlogs for all traffic arrival rates within constant ratio of capacity region and all energy-delay tradeoff parameters. These proofs are the first attempt to theoretically demonstrate guarantees of energy and queue bounds in a framework consisting of possibly negative submodular objective function and non-matriod constraints. Finally, via extensive simulations, we compare the capacity region and energy-queue backlog tradeoff of BEANS with optimal and existing algorithms, and show that BEANS attains up to 65% of energy saving for the same average queue backlog compared to the algorithms which do not take traffic dynamics and energy consumption into considerations. [ABSTRACT FROM AUTHOR]

This paper proposes a direction of arrival (DOA) estimation method for an acoustic source using linear sensor arrays on the basis of generalized regression neural network (GRNN). The real and imaginary parts of the received data of linear sensor arrays in the frequency domain are vectorized and spliced into a one-dimensional sequence as the input feature. The application of this method is studied in three scenarios on noiseless, noisy, and hybrid training sets. Simulations show that the GRNN algorithm has higher accuracy at high SNRs than the support vector machine (SVM), convolutional neural network (CNN) and multiple signal classification (MUSIC) methods, and only the GRNN method can estimate the DOA effectively at low SNRs. According to the different accuracy requirements in practical applications, this paper also provides the selection rules for an appropriate training set for the GRNN method. Therefore, the GRNN method can achieve effective the DOA estimation in different SNR environments of many scenarios. [ABSTRACT FROM AUTHOR]

Synthetic aperture sonar (SAS) interferometry is a technique for very high resolution imaging and mapping of the seabed. In SAS interferometry, the seabed depth estimation performance is a function of the system, the geometry, the signal‐to‐noise ratio (SNR) and the filter size, equivalent to the achieved horizontal resolution. A strong driver for SNR is the imaging range and grazing angle. The variation of these parameters over a typical SAS swath gives rise to a large variation in the depth estimation performance. To mitigate the negative effect of this, we suggest to use an adaptive phase estimation filter size, such that the standard deviation of the depth estimate is proportional to the horizontal resolution. We demonstrate the suggested adaptive filter size method on long range data collected using a HUGIN Superior autonomous underwater vehicle (AUV) equipped with a HISAS 1032 Dual receiver interferometric SAS. Our technique increases the valid area coverage when the SNR is marginal, at the expense of reduced horizontal resolution.Synthetic aperture sonar (SAS) interferometry is a technique for very high resolution imaging and mapping of the seabed. In SAS interferometry, the seabed depth estimation performance is a function of the system, the geometry, the signal‐to‐noise ratio (SNR) and the filter size, equivalent to the achieved horizontal resolution. In this paper we suggest to use an adaptive phase estimation filter size, such that the standard deviation of the depth estimate is proportional to the horizontal resolution. [ABSTRACT FROM AUTHOR]

Using two electrically long dipoles to estimate the polarizations of impinging sources, this paper pioneers signal-processing algorithms in closed forms. The existing literature on crossed-dipoles polarimetry are restricted to electrically short dipoles with an electrical length of $(L/\lambda) \le (1/10)$ , which have subpar efficiency in radiation and in reception. In contrast, this paper allows electrically long dipoles—two of them here are perpendicularly oriented but may be collocated or may be separated by a known displacement. Using such a pair of electrically long dipoles for polarization estimation, this paper proposes new closed-form formulas and derives the associated Cramér–Rao bounds. [ABSTRACT FROM AUTHOR]

Hybrid transceiver designs for millimeter wave massive multiple-input multiple-output systems are feasible candidates to reduce the volume of radio frequency (RF) chains, decomposing the signal processing into the analog and digital domains. The existing schemes heavily depend on the singular value decomposition to obtain subchannels with uneven power gains, causing bit error rate (BER) performance degradation. In this paper, we propose a hybrid transceiver design based on the uniform channel decomposition (UCD), yielding subchannels with identical gains to improve the BER performance. Inspired by the UCD concept, we derive an equivalent optimization problem and propose two schemes, namely, phase-extraction and iterative update, to determine the RF beamformers, yielding an effective baseband channel with the greatest possible geometric mean of singular values. We apply the UCD with a minimum mean squared error criterion to complete the baseband beamforming. Finally, we combine the hybrid UCD beamforming with the vertical-Bell Labs layered space-time and dirty paper coding, to eliminate the inter-subchannel interference. An asymptotic analysis of the scheme performance is also pursued. The simulation results show that the proposed hybrid scheme outperforms the conventional schemes on the transmission BER, which achieves a spectral efficiency close to that of the fully-digital counterpart. [ABSTRACT FROM AUTHOR]

The practical application of a new class of coprime arrays based on the Chinese remainder theorem (CRT) over quadratic fields is presented in this paper. The proposed CRT arrays are constructed by ideal lattices embedded from coprime quadratic integers with $\mathbf {B}_1$ and $\mathbf {B}_2$ being their matrix representations, respectively, whereby the degrees of freedom (DOF) surges to $O(|\det {(\mathbf {B}_1\mathbf {B}_2)}|)$ with $|\det (\mathbf {B}_1)| + |\det (\mathbf {B}_2)|$ sensors. The geometrical constructions and theoretical foundations were discussed in the accompanying paper in great detail, while this paper focuses on aspects of the application of the proposed arrays in two-dimensional (2-D) remote sensing. A generalization of CRT arrays based on two or more pairwise coprime ideal lattices is proposed with closed-form expressions on sensor locations, the total number of sensors, and the achievable DOF. The issues pertaining to the coprimality of any two quadratic integers are also addressed to explore all possible ideal lattices. Exploiting the symmetry of lattices, sensor reduction methods are discussed with the coarray remaining intact for economic maximization. In order to extend conventional angle estimation techniques based on uniformly distributed arrays to the method that can exploit any coarray configurations based on lattices, this paper introduces a hexagon-to-rectangular transformation to 2-D spatial smoothing, providing the possibility of finding more compact sensor arrays. Examples are provided to verify the feasibility of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper presents an in-situ active acoustic detection system that can detect and locate the subsea gas plumes simultaneously using a transceiver of an ultra-short baseline (USBL) positioning system. Seabed gas leaks, both natural and artificial, are causing concern worldwide. Conventional detection systems, including Raman spectrometers, side-scan sonars, and multibeam sonars, suffer from some limitations such as the effective range and real-time processing problem. We use an omnidirectional USBL transceiver to illuminate surroundings and detect echoes from gas plumes. A small hydrophone array in planar circular shape is used to beam-form the received echoes. An echo identification algorithm is proposed to separate the echoes from fixed objects and gas plumes. The results are verified against various experiments. The matched filtering and phased-array beamforming are applied to determine the relative distance and angle of arrival from gas plumes. A preliminary result on gas flow rate quantification is also studied. This paper describes the system design and algorithms in detail and presents experimental results in an anechoic tank, demonstrating the great potential of this system for subsea gas plumes detection and positioning. [ABSTRACT FROM AUTHOR]

The performance of the recently proposed orientational beamforming (OBF) system for ultra-wideband (UWB) signals degrades seriously in multiuser situation. Although various techniques have been proposed in the literature to suppress multiuser interference in the time-hopping UWB system, there has been no research on interference suppression in the OBF UWB system. In this paper, we propose a nonlinear OBF system via a modified radial basis function (RBF) neural network, named the RBF-OBF system, to reject orientational UWB interferences caused by multiuser transmission in the OBF system. The RBF neural network is selected mainly because of its simple structure, convenient training process, and fast convergence speed. However, the conventional Euclidean-distance-based RBF neural network is not suitable for the training process used in this work, as the training samples are noiseless orientational steering vectors in frequency domain. Therefore, a modified RBF neural network is proposed in this paper, which evaluates the similarity between an input vector and the center vector of a hidden layer neuron by their signed complex correlation coefficient. Numerous simulations demonstrate that compared to the conventional OBF system, the proposed RBF-OBF system can significantly reduce the bit error rates (BERs) under different noise and interference situations. The BER reduction rate of the proposed RBF-OBF system is higher than 92% in additive white Gaussian noise channel and higher than 89% in line-of-sight multipath channel. [ABSTRACT FROM AUTHOR]

The emergence of interference alignment (IA) as a degrees-of-freedom optimal strategy motivates the need to investigate whether IA can be leveraged to aid conventional network utility maximization algorithms, which are typically only capable of finding locally optimal solutions. To test the usefulness of IA in this context, this paper proposes a two-stage optimization framework for the downlink of a $G$ -cell multi-antenna network with $K$ users/cell. The first stage of the proposed framework focuses on nulling interference from a set of dominant interferers using IA, while the second stage optimizes transmit and receive beamformers to maximize a network-wide utility using the IA solution as the initial condition. Further, this paper establishes a set of new feasibility results for partial IA that can be used to guide the number of dominant interferers to be nulled in the first stage. Through simulations on specific topologies of a cluster of base stations, it is observed that the impact of IA depends on the choice of the utility function and the presence of out-of-cluster interference. In the absence of out-of-cluster interference, the proposed framework outperforms straightforward optimization when maximizing the minimum rate, while providing marginal gains when maximizing sum rate. However, the benefit of IA is greatly diminished in the presence of significant out-of-cluster interference. [ABSTRACT FROM AUTHOR]

Multiple transmitting antennas can considerably increase the downlink spectral efficiency by beamforming to multiple users at the same time. However, multiuser beamforming requires channel state information (CSI) at the transmitter, which leads to training overhead and reduces overall achievable spectral efficiency. In this paper, we propose and analyze a sequential beamforming strategy that utilizes full-duplex base station to implement downlink data transmission concurrently with CSI acquisition via in-band closed or open loop training. Our results demonstrate that full-duplex capability can improve the spectral efficiency of uni-directional traffic, by leveraging it to reduce the control overhead of CSI estimation. In moderate SNR regimes, we analytically derive tight approximations for the optimal training duration and characterize the associated respective spectral efficiency. We further characterize the enhanced multiplexing gain performance in the high SNR regime. In both regimes, the performance of the proposed full-duplex strategy is compared with the half-duplex counterpart to quantify spectral efficiency improvement. With experimental data and 3-D channel model from 3GPP, in a 1.4 MHz $8\times 8$ system LTE system with the block length of 500 symbols, the proposed strategy attains a spectral efficiency improvement of 130% and 8% with closed and open loop training, respectively. [ABSTRACT FROM PUBLISHER]