Robust transceiver design in full-duplex MIMO cognitive radios

Abstract

We consider a full duplex (FD) multiple-input multiple-output (MIMO) underlay cognitive radio (CR) cellular network, in which an FD secondary base-station (BS) serves multiple half-duplex (HD) uplink (UL) and downlink (DL) secondary users (SUs) at the same time and frequency. We assume that the channel state information (CSI) available at the transmitters is imperfect, and the errors of the CSI are assumed to be norm bounded. Under the impact of channel uncertainty, we address the sum mean-squared-errors (MSE) minimization problem subject to individual power constraints at the UL SUs, a total power-constraint at the secondary BS, and the interference constraints on the primary users (PUs) by the secondary network. By transforming the problem into an equivalent semidefinite programming (SDP), we propose an iterative alternating algorithm to compute the transceiver matrices jointly. Moreover, to reduce the high computational complexity of the SDP method, we develop a cutting-set method, which solves the problem by alternating between an optimization step (transceiver design) and a pessimization step (worst-case channel analysis). Numerical results are presented to show the effectiveness and robustness of the proposed algorithms.

Resource Optimization in Full Duplex Non-Orthogonal Multiple Access Systems

“…It was stated that the cumulative effects of hardware imperfections can be reasonably approximated as an additive Gaussian noise. In conjunction to several other literature [11], [12], which also use the same hardware impairment model, we consider an additive white Gaussian term as "transmitter noise" ("receiver distortion") at each transmit (receive) antenna, whose variance is κ (β) times the power of the undistorted signal at the corresponding chain. Global CSI is assumed to be available at the BS and users.…”

“…The variance of c U L k and c 0 is given by κ (κ 1) times the transmit power of the signal at each antenna [3]. These distortions are statistically independent from the transmitted signals, and can be modeled as c U L k and c 0 as [12]…”

“…The variance of e DL j is given by β (β 1) times the power of the undistorted received signal at each receive antenna [3]. The receiver distortions are also statistically independent from the received signals and accordingly, e DL j and e 0 can be modeled as [12] e DL j ∼ CN 0, βvar y DL j − e DL j , e 0 ∼ CN (0, βvar (y 0 − e 0 )) .…”

Robust Transceiver Design in Full-Duplex MIMO Cognitive Radios

“…respectively, where n 0 ∈ C N0 and n DL j ∈ C Nj denote the additive white Gaussian noise (AWGN) vector with zero mean and covariance matrix R 0 = σ 2 0 I N0 and R DL j = σ 2 j I Nj at the BS and the j-th DL user, respectively. 1 Furthermore, c U L k (c 0 ) in (2)-(3), is the distortion at the transmitter at the k-th UL user (BS), which closely approximates the effects of phase noise, non-linearities in the DAC and additive power-amplifier noise. The covariance matrix of c U L k is given by κ (κ 1) times the energy of the intended signal at each transmit antenna [7].…”

Design and Analysis of FD MIMO Cellular Systems in Coexistence With MIMO Radar

“…With regards to FD, it can potentially double the spectrum efficiency of communication systems [4]- [7] by transmitting and receiving at the same time and frequency resources. However, this results in signal leakage from the transmitting antennas to its receiving antennas, also known as self-interference (SI), which dominates the performance of FD systems.…”