The use of a reconfigurable intelligent surface (RIS) for aiding user-specific transmission has been widely explored. However, little attention has been devoted to utilizing RIS for assisting cell-specific transmission, where the RIS needs to reflect signals in a broad angular range. Furthermore, although modern communication systems operate in two polarizations, the majority of the works on RIS consider a uni-polarized surface, only reflecting the signals in one polarization. To fill these gaps, we study a downlink broadcasting scenario where a base station (BS) sends a cell-specific signal to all the users residing at unknown locations with the assistance of a dual-polarized RIS. We utilize the duality between the auto-correlation function and power spectrum in the space/spatial-frequency domain to design configurations for broad-beam reflection. We first consider a free-space line-of-sight BS-RIS channel and show that the RIS configuration matrices must form a Golay complementary array pair for broad-beam radiation. We also present how to form Golay complementary array pairs based on known Golay complementary sequence pairs. We then consider an arbitrary BS-RIS channel and propose an algorithm based on stochastic optimization to find RIS configurations that produce a practically broad beam by relaxing the requirement on uniform broadness. Numerical simulations are finally conducted to corroborate the analyses and evaluate the performance.

We propose an efficient method for designing broad beams with spatially flat array factor and efficient power utilization for cell-specific coverage in communication systems equipped with large antenna arrays. To ensure full power efficiency, the method is restricted to phase-only weight manipulations. Our framework is based on the discovered connection between dual-polarized beamforming and polyphase Golay sequences. Exploiting this connection, we propose several methods for array expansion from smaller to larger sizes, while preserving the radiation pattern. In addition, to fill the gaps in the feasible array sizes, we introduce the concept of$\epsilon $-complementarity that relaxes the requirement on zero side lobes of the sum aperiodic autocorrelation function of a sequence pair. Furthermore, we develop a modified Great Deluge algorithm (MGDA) that finds$\epsilon $-complementary pairs of arbitrary length, and hence enables broad beamforming for arbitrarily-sized uniform linear arrays. We also discuss the extension of this approach to two-dimensional uniform rectangular arrays. Our numerical results demonstrate the superiority of the proposed approach with respect to existing beam-broadening methods.

In this paper, we propose a novel deep-learning based precoding framework, called deep MIMO autoprecoder, that can be used to learn an optimal precoding policy for multiple-input multiple-output (MIMO) precoding problems, for which optimal solutions are not known. We divide the proposed framework into two steps: action value estimation and optimal action prediction. In the first step, reinforcement learning (RL) is applied to learn and estimate the state-action value function (also called Q-value function) of each discrete precoder in a codebook through interactions with environments of MIMO systems, while for the second step, a supervised learning model is used to predict an optimal precoder in a continuous precoder space that corresponds to the output from the first step. The proposed framework can be implemented by means of two deep neural networks: a deep RL algorithm, called deep Q-network (DQN), and a new neural network, that we call deep Q-value-to-policy network (DQPN). More specifically, an environment-specific DQN is trained for a MIMO scenario in order to estimate the Q-values of each precoder in environmental states. Meanwhile, a single universal DQPN is pretrained offline on the most general MIMO channel model that allows it to achieve generalization over unseen channels. The numerical results verify the effectiveness of the deep MIMO autoprecoder and show that the proposed precoding framework can outperform the conventional approximation algorithm.

Next-generation wireless communication systems will rely on multiple-antenna deployments enabling the beamforming functionality. In contrast to data transmission that benefits from user-specific beamforming (i. e., focusing the radiation at a particular user terminal), broadcasting of control information over a public channel requires cell-specific beamforming (i.e., cell-wide coverage). This paper presents a simple beamforming technique for cell-specific signaling for a four-port linear antenna array, based on exploiting two orthogonal polarizations and a maximum-ratio combining receiver. For instance, it is shown that by carefully tuning the excitation weights it is possible to design radiation patterns with any half-power beamwidth in the range from that of a narrow DFT beam to that of a single antenna element, while preserving the constant-modulus property of the excitation weights, thereby assuring excellent power utilization. The paper, furthermore, shows a direct connection between the underlying beamforming technique and the well-known concept of Alamouti space-time coding.

This paper studies the asymptotic performance of multi-hop amplify-and-forward relay multiple-antenna communication channels. Each multi-antenna relay terminal in the considered network amplifies the received signal, sent by a source, and retransmits it upstream toward a destination. Achievable ergodic rates of the relay channel with both jointly optimal detection and decoding and practical separate-decoding receiver architectures for arbitrary signaling schemes, along with average bit error rates for various types of detectors are derived in the regime where the number of antennas at each terminal grows large without a bound. To overcome the difficulty of averaging over channel realizations, we apply a large-system analysis based on the replica method from statistical physics. The validity of the large-system analysis is further verified through Monte Carlo simulations of realistic finite-sized systems.

Hardware non-idealities in wireless transmitter electronics cause distortion that is not captured by conventional linear channel models; in fact, error-vector magnitude (EVM) measurements in conformance testing conceptually reduce their collective effect to an additive noise component at each subcarrier. Motivated by the EVM, the present paper considers a `binoisy' multiple-input multiple-output (MIMO) channel model where the additional non-idealities manifest themselves as an additive distortion noise term at the transmit side. Through this extended MIMO relation, the effects of hardware impairments on the achievable rates of different digital modulation schemes are studied via large system analysis. The numerical results illustrate how tolerable EVM levels depend non-trivially on various factors, including the signal-to-noise ratio, modulation order and the level of asymmetry in antenna array configurations.

Hardware impairments in radio-frequency components of a wireless system cause unavoidable distortions to transmission that are not captured by the conventional linear channel model. In this paper, a “binoisy” single-user multiple-input multiple-output (SU-MIMO) relation is considered where the additional distortions are modeled via an additive noise term at the transmit side. Through this extended SU-MIMO channel model, the effects of transceiver hardware impairments on the achievable rate of multi-antenna point-to-point systems are studied. Channel input distributions encompassing practical discrete modulation schemes, such as, QAM and PSK, as well as Gaussian signaling are covered. In addition, the impact of mismatched detection and decoding when the receiver has insufficient information about the non-idealities is investigated. The numerical results show that for realistic system parameters, the effects of transmit-side noise and mismatched decoding become significant only at high modulation orders.

The present paper studies the asymptotic performance of dual-hop amplify-and-forward multiple-input multiple-output relay communication systems. In the corresponding setup, a relay amplifies the signal received from a source, retransmitting it towards a destination, while the direct source-destination link is absent. Ergodic achievable rates under separate decoding, along with the average bit error rate under various detection schemes are derived in the regime where the number of antennas at each terminal grows without bound. To overcome the mathematical difficulty of averaging over both channel realizations and input signals we apply large-system analysis based on the replica method from statistical physics. The validity of the large-system analysis is further verified through Monte Carlo simulations, providing particularly good accuracy at low SNR.

This paper investigates cooperation for secrecy in cognitive radio networks. In particular, we consider a four-node cognitive scenario where the secondary receiver is treated as a potential eavesdropper with respect to the primary transmission. The cognitive transmitter can help the primary transmission, and it should also ensure that the primary message is not leaked to the secondary user. We consider two cognitive scenarios depending on whether the secondary transmitter knows the primary message or not. In the first case, the secondary transmitter is unaware of the primary transmitter's message and acts as a helping interferer to enhance the secrecy of the primary transmission, whereas in the second case, relaying of the primary message is also within its capabilities. First, we find achievable rate regions for these two scenarios in the case of AWGN channels. We then investigate three different optimization problems: the maximization of the primary rate, the maximization of the secondary rate and the minimization of the secondary transmit power. For these optimization problems, we find closed-form expressions in important special cases. Furthermore, we analyze the cooperation between the primary and secondary transmitters from a game-theoretic perspective. We model their interaction as a Stackelberg game, for which we define and find the Stackelberg equilibrium. Finally, we use numerical examples to illustrate the rate regions, the three optimizations, and the impact of the Stackelberg game on the achievable rates and on the transmission strategies of the secondary transmitter.

Presence of multiple antennas on both sides of a communication channel promises significant improvements in system throughput and power efficiency. In effect, a new class of large multiple-input multiple-output (MIMO) communication systems has recently emerged and attracted both scientific and industrial attention. To analyze these systems in realistic scenarios one has to include such aspects as co-channel interference, multiple access and spatial correlation. In this paper, we study the properties of correlated MIMO multiple-access channels in the presence of external interference. Using the replica method from statistical physics, we derive the ergodic sum-rate of the communication for arbitrary signal constellations when the numbers of antennas at both ends of the channel grow large. Based on these asymptotic expressions, we also address the problem of sum-rate maximization using statistical channel state information and linear precoding. The numerical results demonstrate that when the interfering terminals use discrete constellations, the resulting interference becomes easier to handle compared to Gaussian signals. Thus, it may be possible to accommodate more interfering transmitter-receiver pairs within the same area as compared to the case of Gaussian signals. In addition, we demonstrate numerically for the Gaussian and QPSK signaling schemes that it is possible to design precoder matrices that significantly improve the achievable rates at low-to-mid range of signal-to-noise ratios when compared to isotropic precoding.

The present paper investigates the achievable data rates of multi-hop amplify-and-forward multi-antenna relay channels with arbitrary number of hops K. Each multi-antenna terminal in the system amplifies the received signal and retransmits it upstream. To analyze the ergodic end-to-end mutual information of the system, one has to perform averaging over the fading coefficients. To overcome this difficulty we apply large-system analysis, based on the assumption that the number of antennas grows without bound at every terminal. Using the replica method, we derive an explicit asymptotic expression for the ergodic mutual information between the input and output of the K-hop channel with no restrictions on the channel inputs. Numerical results support the validity of the replica analysis and show that the result is tight even for small antenna arrays.

Base station cooperation in cellular networks has been recently recognized as a key technology for mitigating interference, providing thus significant improvements in the system performance. In this paper, we consider a simple scenario consisting of two one-dimensional cells, where the base stations have fixed locations, while the user terminals are randomly distributed on a line. Exploiting the replica method from statistical physics, we derive the ergodic sum-rate under arbitrary signaling for both cooperative and non-cooperative scenarios, when the system size grows large. The obtained results are analytically tractable and can be used to optimize the system parameters in a simple manner. The numerical examples show that the analysis provides good approximations for finite-sized systems.

In this paper we consider a scenario, where several mobile multi-antenna terminals communicate with a multi-antenna base station within a cellular communication system over the flat Rayleigh fading channel. In addition, several terminals from the neighboring cell cause interference. For such a scenario, we derive, using the replica method, the asymptotic sum-rate of the communication in the large-system limit for arbitrary signal constellations. Moreover, we show via numerical results that when the interfering terminals use a QPSK constellation, the resulting interference becomes easier to handle. In effect, we may be able to accumulate more interfering transmitter-receiver pairs within the same area as compared to the case of Gaussian signals. Monte-Carlo simulations show that the derived asymptotic expression matches well with the simulated values even for small numbers of antennas.

This paper explores the trade-off between cooperation and secrecy in cognitive radio networks. We consider a scenario consisting of a primary and a secondary system. In the simplest case, each system is represented by a pair of transmitter and receiver. We assume a secrecy constraint on the transmission in the sense that the message of the primary transmitter has to be concealed from the secondary receiver. Both situations where the secondary transmitter is aware and unaware of the primary message are investigated and compared. In the first case, the secondary transmitter helps by allocating power for jamming, which increases the secrecy of the first message. In the latter case, it can also act as a relay for the primary message, thus improving the reliability of the primary transmission. Furthermore, we extend our results to the scenario where the secondary system comprises multiple receivers. For each case we present achievable rate regions. We then provide numerical illustrations for these rate regions. Our main result is that, in spite of the secrecy constraint, cooperation is beneficial in terms of the achievable rates. In particular, the secondary system can achieve a significant rate without decreasing the primary rate below the benchmark rate achievable without the help of the secondary transmitter. Finally, we investigate the influence of the distances between users on the system's performance.

This paper studies the asymptotic sum-rate of the primary network within the relay-assisted multi-antenna cognitive radio system performing amplify-and-forward relaying. The achievable sum-rates are derived in the large-system limit by means of the replica method. A closed-form expression for the sum-rate of the primary network is obtained for large antenna arrays as a function of parameters obtained by solving a set of fixed-point equations. Numerical simulations confirm the validity of the results even for systems with only a few antennas at each terminal.

Cooperative communication is shown to be an efficient method of combating fading in wireless networks. By ”sharing” their antennas cooperating single-antenna nodes create a virtual multi-antenna system and therefore, benefit from spatial diversity. Network coding being a particular cooperative communication technique provides substantial gains in data rate, especially in networks with many sink nodes. By allowing intermediate nodes of a network to mix the incoming data streams, one can achieve the multicast capacity. Recently, Xiao and Skoglund showed that binary network coding (BNC) is not optimal for multi-user multi-relay networks in terms of diversity and proposed diversity network coding (DNC) scheme that outperforms BNC approach. Following this approach we examine potential diversity gains of using the DNC scheme for multi-source line networks. We show that the DNC technique outperforms both conventional time-orthogonal transmission and the BNC scheme. Further, the problem of optimal scheduling of the transmission is explored. We formulate the optimization problem and propose efficient solutions. Numerical results are presented to support theoretical findings.

A layered wireless network is considered, where information is transmitted in a multi-hop MIMO-like fashion from the source node layer through several intermediate layers of nodes before reaching the sink node layer. Analog network coding is investigated as the network transmission strategy, exploiting the inherent superposition features of the wireless channel. A general myopic transmission protocol is proposed, allowing overhearing across several layers. Special cases are investigated in terms of diversity and bit error rate when using different types of linear detectors. Conditions for successful decoding of all source node messages at each sink node are derived.

Optimal power allocation in a multi-hop cognitive radio network is investigated. Information transmitted from the source passes through several wireless relay nodes before reaching the destination. At each hop, the received signal is decoded, re-encoded and retransmitted to the following node. Transmissions at every hop are overheard by nearby nodes and therefore cause interference. We study optimal power allocation strategies that maximize the end-to-end throughput of the network under the constraint of strictly limited interference to external users. We show that for networks that can be modeled as a line topology the optimal solution is achieved when the capacities of every intermediate link are equal and the interference power constraint is satisfied with equality. High- and low-SNR approximations that simplify the problem of finding the optimal power allocation are presented as well. The numerical results show good performance compared to schemes with equal power allocation.