In this work, we address the problem of a joint robust transmission, reflection and reception strategy design at an active reconfigurable intelligent surface (RIS)-assisted underlay multiple input multiple output (MIMO) cognitive radio networks in which a secondary transmitter serves multiple secondary receivers simultaneously. As such, we study the impact of imperfection in channel state information (CSI) at the secondary transmitter following a norm-bounded error model. A sum mean squared error (MSE) minimization problem is formulated by jointly optimizing the transmit beamforming matrix (BFM) at the secondary transmitter, the linear reception filters (LRF) at the secondary receivers and the reflection coefficient matrix (RCM) at the RIS subject to the constraints of available transmission power at the secondary transmitter, maximum allowed interference power towards the primary receiver and the permissible amplification range at each RIS element. Due to the coupled nature of the BFM, LRF and RCM variables, the formulated problem is non-convex and, thus, cannot be solved using conventional optimization methods. As a result, we propose an alternating optimization (AO) based iterative algorithm that determines the optimal BFM, LRF and RCM using semidefinite programming and inner approximation methods with guaranteed convergence. We also discuss the impact of the limited power budget at active RIS on the performance of the considered network. We present simulation-based numerical results to validate the efficacy and robustness of the proposed algorithm. Moreover, we also highlight the superiority of the use of the active RIS over passive RIS in the considered communication network.

In this work we consider a full-duplex (FD) and amplify-and-forward (AF) relay with multiple antennas, where hardware impairments of the FD relay are taken into account. Due to the inter-dependency of the transmit relay power and the residual self-interference in an FD-AF relay, we observe a distortion loop that degrades the system performance when relay dynamic range is not high. In this regard, we analyze the relay function, and an optimization problem is formulated to maximize the signal to distortion-plus-noise ratio (SDNR) under relay and source transmit power constraints. Due to the problem complexity, we propose a gradient-projection-based (GP) algorithm to obtain an optimal solution. Moreover, a nonalternating sub-optimal solution is proposed by assuming a rank-1 relay amplification matrix, and separating the design of the relay process into multiple stages (MuStR1). The proposed MuStR1 method is then enhanced by introducing an alternating update over the optimization variables, denoted as AltMuStR1 algorithm. Numerical simulations show that compared to GP, the proposed (Alt)MuStR1 algorithms significantly reduce the required computational complexity at the expense of a slight performance degradation. Moreover, as the hardware impairments increase, or for a system with a high transmit power, the impact of applying a distortion-aware design is significant.

In this work, the feasibility of spectrum sharing between a multiple-input multiple-output (MIMO) radar system (RS) and a MIMO cellular system (CS), comprising of a full duplex (FD) base station (BS) serving multiple downlink and uplink users at the same time and frequency is investigated. While a joint transceiver design technique at the CS’s BS and users is proposed to maximise the probability of detection (PoD) of the MIMO RS, subject to constraints of quality of service (QoS) of users and transmit power at the CS, null-space based waveform projection is used to mitigate the interference from RS towards CS. In particular, the proposed technique optimises the performance of PoD of RS by maximising its lower bound, which is obtained by exploiting the monotonically increasing relationship of PoD and its non-centrality parameter. Numerical results show the utility of the proposed spectrum sharing framework, but with certain trade-offs in performance corresponding to RS’s transmit power, RS’s PoD, CS’s residual self interference power at the FD BS and QoS of users.

We introduce a new robust, outage minimum, () () beamforming scheme to combat the random path blockages typical of systems. Unlike state-of-the-art methods, which are of limited applicability in practice due to their combinatorial nature which leads to prohibitive complexity, the proposed method is based on a stochastic-learning-approach, which learns crucial blockage patterns without resorting to the well-known worst-case optimization framework. Simulation results demonstrate the superior performance of the proposed method both in terms of outage probability and effective achievable rate.

In this paper, we address the linear precoding and decoding design problem for a bidirectional orthogonal frequency-division multiplexing communication system, between two multiple-input multiple-output (MIMO) full-duplex (FD) nodes. The effects of hardware distortion as well as the channel state information error are taken into account. In the first step, we transform the available time-domain characterization of the hardware distortions for FD MIMO transceivers to the frequency domain, via a linear Fourier transformation. As a result, the explicit impact of hardware inaccuracies on the residual self-interference and inter-carrier leakage is formulated in relation to the intended transmit/received signals. Afterwards, linear precoding and decoding designs are proposed to enhance the system performance following the minimum-mean-squared-error and sum rate maximization strategies, assuming the availability of perfect or erroneous channel state information. The proposed designs are based on the application of alternating optimization over the system parameters, leading to a necessary convergence. Numerical results indicate that the application of a distortion-aware design is essential for a system with a high hardware distortion, or for a system with a low thermal noise variance.