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We propose new transmission schemes and receiver algorithms for unsourced multiple access (UMA) in MIMO and massive MIMO channels. Each active transmitter’s information bits are first channel encoded. The coded bits are divided into sub-blocks and each sub-block is modulated and transmitted. For both MIMO and massive MIMO channels, the conventional nonlinear modulation can be employed where each sub-block of coded bits is mapped to a transmitted signal vector. For the massive MIMO channel, we propose a new hybrid modulation scheme to reduce the receiver complexity, where the first sub-block is nonlinearly modulated, and the subsequent sub-blocks are linearly modulated and spread by the first sub-block signal. We also propose sparsity-exploiting blind receiver algorithms. Specifically, for the MIMO case, we exploit the codeword sparsity inherent in the UMA system, and a channel clustering technique, to estimate the channel and the transmitted signal of each transmitter. For the massive MIMO, in addition to the codeword sparsity, we further exploit the channel sparsity and user sparsity in estimating the channel and transmitted signal of each transmitter. The proposed receiver algorithms for both MIMO and massive MIMO channels output either hard or soft estimates of the coded bits, and therefore single-user channel decoding of the information bits can be performed for each transmitter. Extensive simulation results are provided to demonstrate the performances of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the security problem of a multi-eavesdrop multiple-input-single-output (MISO) wiretap channel, where an N-antenna transmitter communicates with a single-antenna legitimate user in the presence of multiple single-antenna smart eavesdroppers. To overcome the security risk of the traditional secure constructive interference-based (CI-based) scheme when facing the smart eavesdroppers, we propose a novel deception scheme (DS) via a random transmission strategy, where the eavesdroppers are expected to decode the deception symbols correctly but unable to distinguish the authenticity of the decoded symbol. Then, an efficient algorithm is proposed for the deception signal-to interference-plus-noise (SINR)-balancing problem when perfect channel state information (CSI) is assumed. Furthermore, we consider a practical scenario where only imperfect CSI is available, and explore two different methods for the deception optimization problem, i.e., convexification relaxation approach (CRA) and Lagrangian relaxation approach (LRA), respectively. For both CSI cases, a closed-form solution to the considered CI-based deception scheme is obtained. Simulation results validate the superiority of the proposed approach over traditional secure precoding schemes, and also demonstrate the significant computation efficiency improvements for the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) symbiotic radio (SR) system is proposed, in which an RIS, operating as a secondary transmitter (STx), sends messages to a multi-antenna secondary receiver (SRx) by using cognitive backscattering communication, and simultaneously, it enhances the primary transmission from a multi-antenna primary transmitter (PTx) to a multi-antenna primary receiver (PRx) by intelligently reconfiguring the wireless environment. We are interested in the joint design of active transmit beamformer at the PTx and passive reflecting beamformer at the STx to minimize the total transmit power at the PTx, subject to the signal-to-noise-ratio (SNR) constraint for the secondary transmission and the rate constraint for the primary transmission. Due to the non-convexity of the formulated problem, we decouple the original problem into a series of subproblems using the alternating optimization method and then iteratively solve them. The convergence performance and computational complexity of the proposed algorithm are analyzed. Furthermore, we develop a low-complexity algorithm to design the reflecting beamformer by solving a backscatter link enhancement problem through the semi-definite relaxation (SDR) technique. Then, theoretical analysis is performed to reveal the insights of the proposed system. Finally, simulation results are presented to validate the effectiveness of the proposed algorithms and the superiority of the proposed system. [ABSTRACT FROM AUTHOR]

This article investigates the intelligent reflecting surface (IRS)-aided multi-antenna covert communications. In particular, with the help of an IRS, a favorable communication environment can be established via controllable intelligent signal reflection, which facilitates the covert communication between a multi-antenna transmitter (Alice) and a legitimate full-duplex receiver (Bob) in the existence of a watchful warden (Willie). In order to shelter the desired communication, Bob generates jamming signals with a varying power to confuse Willie. The beamforming vector employed by Alice and the passive phase shifts of the IRS are optimized jointly to maximize the covert rate under the constraints of the successful detection probability at Willie and the communication outage experienced by Bob. We focus on the worst case by characterizing the minimum successful detection probability at Willie. The formulated problem is non-convex, due to the coupling between the beamforming vector of Alice and the phase shifts of the IRS, and the unit modulus constraint on the phase shifts of the IRS. To tackle the above issues, we first employ the penalty dual decomposition (PDD) method to handle the coupling effect. After that, we apply the successive convex approximation (SCA) method to develop an iterative algorithm for locating a Karush-Kuhn-Tucker (KKT) solution of the joint design problem. Moreover, we show that our proposed iterative algorithm can be adapted to handle the multi-antenna Willie case. Simulation results validate the effectiveness of the proposed iterative algorithm and show the great potential brought by the IRS for covert communications. [ABSTRACT FROM AUTHOR]

In this article, we propose a three-dimensional (3D) multi-input multi-output (MIMO) channel model for air-to-ground (A2G) communications in unmanned aerial vehicles (UAV) environments, where the UAV transmitter and ground receiver are in motion in the air and on the ground, respectively. A novel angular estimation algorithm is proposed to estimate the real-time azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) based on the non-stationary nature of the channel model. In the model, we investigate the time-varying spatial cross-correlation functions (CCFs) and temporal auto-correlation functions (ACFs) with respect to the different moving directions and velocities of the UAV transmitter and ground receiver. Furthermore, we derive and study the Doppler power spectral densities (PSDs) and power delay profiles (PDPs) of the proposed channel model. Numerical results show that characteristics of the proposed channel model are very close to those of practical measurements, which provide a new and practical approach to evaluate the performance of next generation UAV-MIMO communication systems. [ABSTRACT FROM AUTHOR]

In this work, we investigate the problem of secure communications in a downlink large-scale multi-antenna assisted simultaneous wireless information and power transfer (SWIPT) system, where a base station (BS) transmits signals to serve a number of information decoding (ID) and energy harvesting (EH) users. Considering that the EH users can potentially eavesdrop the ID users’ confidential information, we study the robust joint design of the hybrid analog-digital (A/D) beamforming (BF) matrices and of the artificial redundant signal (ARS) covariance matrix at the BS, where the aim is to maximize the worst-case sum secrecy rate for the ID users under a transmit power constraint, a nonlinear EH constraint and a unit-modulus constraint on the entries of the analog BF matrix. The corresponding optimization problem is very challenging due to the nonlinear and nonconvex objective function and constraints. Using innovative optimization techniques, we first transform the original problem into an equivalent but more tractable form, and then develop a novel joint iterative algorithm based on the penalty-concave-convex procedure (CCCP) for solving the resultant problem. We show that the proposed penalty-CCCP based algorithm for ARS-aided robust joint hybrid BF design converges to a Karush-Kuhn-Tucker solution of the original problem, and also analyze its computational complexity. Our simulation results verify that the resultant robust joint hybrid BF design algorithm relying on ARS significantly outperforms the conventional hybrid BF benchmark algorithms and efficiently achieves the performance of the fully-digital BF with reduced number of radio frequency chains and energy consumption. [ABSTRACT FROM AUTHOR]

We consider a homogeneous cellular network where a multi-antenna base station (BS) in each cell transmits messages to its intended user over a common frequency band. To improve the system capacity of this multi-cell multi-input single-output (MISO) interference channel, one of the state-of-the-art algorithms, namely, downlink-beamforming coordination, allows all BSs to cooperate with one another to mitigate the effect of inter-cell interference. However, most existing algorithms are suboptimal and impractical in a dynamic wireless environment, due to the high computational complexity and the overhead involved in collecting global channel state information (CSI). In this study, we exploit deep reinforcement learning (DRL) and propose a distributed dynamic downlink-beamforming coordination (DDBC) method with partial observability of the CSI. Each BS is able to train its own deep Q-network and employs appropriate beamformer depending on its environment, which is observed through a designed limited-information exchange protocol. The simulation results show that the proposed DRL-based DDBC method, with a considerably lower system overhead, achieves a system capacity that is very close to that of the fractional programming algorithm with global and instantaneous CSI measurements. In addition, this work demonstrates the potential of utilizing DRL to solve DDBC problems in a more practical manner. [ABSTRACT FROM AUTHOR]

Conventional authentication mechanisms routinely used for validating communication devices are facing significant challenges. This is mainly due to their reliance on both ‘spoofable’ digital credentials and static binary characteristic, and inevitable misdetection in physical layer authentication using time-varying attributes, leading to the cascading risks of security and trust. To circumvent these impediments, we develop an adaptive trust management based soft authentication and progressive authorization scheme by intelligently exploiting the time-varying communication link-related attribute of the transmitter to improve wireless security. First of all, the trust relationship between the transmitter and receiver is established based on the evaluation of selected physical layer attribute for fast authentication and multiple-level authorization. Through the designed trust model, the transmitter is authorized by the specific level of services/resources corresponding to its trust level, so that soft security is achieved. To dynamically update the trust level of the transmitter, we propose an online conformal prediction-based adaptive trust adjustment algorithm relying on the real-time validation of its attribute estimates at the receiver, thus resulting in progressive authorization. The performance of our scheme is theoretically analyzed in terms of its individual risk and individual satisfaction. Our simulation results demonstrate that the proposed scheme significantly improves the security performance and robustness in time-varying environments, and performs better than the static binary authentication scheme and existing physical layer authentication benchmarker. [ABSTRACT FROM AUTHOR]

In a Non-Orthogonal Unicast and Multicast (NOUM) transmission system, a multicast stream intended to all the receivers is superimposed in the power domain on the unicast streams. One layer of Successive Interference Cancellation (SIC) is required at each receiver to remove the multicast stream before decoding its intended unicast stream. In this paper, we first show that a linearly-precoded 1-layer Rate-Splitting (RS) strategy at the transmitter can efficiently exploit this existing SIC receiver architecture. By splitting the unicast messages into common and private parts and encoding the common parts along with the multicast message into a super-common stream decoded by all users, the SIC is better reused for the dual purpose of separating the unicast and multicast streams as well as better managing the multi-user interference among the unicast streams. We further propose multi-layer transmission strategies based on the generalized RS and power-domain Non-Orthogonal Multiple Access (NOMA). Two different objectives are studied for the design of the precoders, namely, maximizing the Weighted Sum Rate (WSR) of the unicast messages and maximizing the system Energy Efficiency (EE), both subject to Quality of Service (QoS) rate requirements of all messages and a sum power constraint. A Weighted Minimum Mean Square Error (WMMSE)-based algorithm and a Successive Convex Approximation (SCA)-based algorithm are proposed to solve the WSR and EE problems, respectively. Numerical results show that the proposed RS-assisted NOUM transmission strategies are more spectrally and energy efficient than the conventional Multi-User Linear-Precoding (MU–LP), Orthogonal Multiple Access (OMA) and power-domain NOMA in a wide range of user deployments (with a diversity of channel directions, channel strengths and qualities of channel state information at the transmitter) and network loads (underloaded and overloaded regimes). It is superior for the downlink multi-antenna NOUM transmission. [ABSTRACT FROM AUTHOR]

In this paper, we consider a cooperative diffusion-based molecular communication (MC) network consisting of single source, single decode-and-forward relay, and single destination. The source and the relay nodes use different types of molecules for information transmission. Considering both the relay and the direct links at the destination node, we have two different received signals that can be used by the destination node to retrieve the data transmitted by the source node. Therefore, diversity arises at the destination node. For detection, we adopt the energy detector applying the diversity combining technique. Since the optimum relay position in relay-aided networks plays a crucial role to enhance their performance, we study the relay location optimization problem. We first derive a closed-form expression for the bit error probability of the proposed detection scheme in which the received energy from the source and the relay nodes are linearly combined at the destination node. Then, in order to find the optimum relay position as well as the optimal destination decision threshold for a given number of molecules released by the source and the relay nodes, we formulate a joint optimization problem whose objective is to minimize the bit error probability of the network. To solve this optimization problem, we propose an iterative algorithm based on the block coordinate descent algorithm and study its convergence behavior. Numerical results show that the error performance can be improved by optimizing the position of the relay node. Our analyses help us in designing a reliable cooperative diffusion-based MC network. [ABSTRACT FROM PUBLISHER]

In this paper we consider orthogonal frequency division multiplexing (OFDM) based cognitive radio systems that operate as secondary users (SUs) in a spectrum overlay approach and study joint spectral shaping and power control subject to specified target signal-to-interference+noise ratio (SINR) as a constrained optimization problem. We discuss necessary and sufficient conditions for the optimal solution of this problem where the spectrum overlay constraints are satisfied and the specified target SINR value is achieved with minimum transmit power. We also present an algorithm that incrementally adapts the OFDM transmitter to reach the optimal solution and study the convergence speed of the proposed algorithm through simulations. [ABSTRACT FROM PUBLISHER]

This paper studies the transmit strategy for a secondary link or the so-called cognitive radio (CR) link under opportunistic spectrum sharing with an existing primary radio (PR) link. It is assumed that the CR transmitter is equipped with multi-antennas, whereby transmit precoding and power control can be jointly deployed to balance between avoiding interference at the PR terminals and optimizing performance of the CR link. This operation is named as cognitive beamforming (CB).Unlike prior study on CB that assumes perfect knowledge of the channels over which the CR transmitter interferes with the PR terminals, this paper proposes a practical CB scheme utilizing a new idea of effective interference channel (EIC), which can be efficiently estimated at the CR transmitter from its observed PR signals. Somehow surprisingly, this paper shows that the learning-based CB scheme with the EIC improves the CR channel capacity against the conventional scheme even with the exact CRto-PR channel knowledge, when the PR link is equipped with multi-antennas but only communicates over a subspace of the total available spatial dimensions. Moreover, this paper presents algorithms for the CR to estimate the EIC over a finite learning time. Due to channel estimation errors, the proposed CB scheme causes leakage interference at the PR terminals, which leads to an interesting learning-throughput tradeoff phenomenon for the CR, pertinent to its time allocation between channel learning and data transmission. This paper derives the optimal channel learning time to maximize the effective throughput of the CR link, subject to the CR transmit power constraint and the interference power constraints for the PR terminals. [ABSTRACT FROM AUTHOR]

This paper studies the asymptotic performance of a multiple-input multiple-output (MIMO) multiple-access (MA) wireless network where spatial correlations at both the transmitters (i.e., mobile stations) and the central receiver (i.e., base station) exist, and more than one multiantenna sets are employed at the receiver to provide macrodiversity. The sense of asymptotic behavior we consider is the large-system limit in which the numbers of antennas at each mobile station and antenna set go to infinity with their ratios fixed. Using the replica method, we derive analytical solutions to the asymptotic spectral efficiency (SE) of the MIMO-MA systems for any given input distributions (not necessarily Gaussian) from the mobile transmitters. Our results can be regarded as the generalization of many previously known results for degenerate cases. Another contribution of this paper is an efficient algorithm to determine the asymptotic optimum transmit-signal covariance matrices that can maximize the SE of the MIMO-MA network within the period in which the spatial channel covariance information (CCI) can be considered static, and assuming that only CCI is available at the transmitters. [ABSTRACT FROM AUTHOR]

Several instantaneous optimization strategies for rate and/or power allocation in the coded V-BLAST are studied analytically. Outage probabilities and system capacities of these strategies in a spatial multiplexing system are compared under generic settings. The conventional waterfilling algorithm is shown to be suboptimal for the coded V-BLAST and a new algorithm ("fractional water-filling") is proposed, which simultaneously maximizes the system capacity and minimizes the outage probability. Closed-form performance analysis of the considered algorithms is given, and the fractional water-filling algorithm is shown to attain the full MIMO channel diversity, significantly outperforming other strategies. Many of the results also apply to generic multi-stream transmission systems (e.g. spatial multiplexing on the channel eigenmodes, OFDM) or the systems relying on successive interference cancelation (multi-user detection, channel equalization). [ABSTRACT FROM AUTHOR]