Multiple-input Single-output Communication System Research Articles

Cooperative beamforming design for double-IRS-assisted MISO communication system

Intelligent reflecting surface (IRS) has arisen as a promising technology to reconfigure the wireless propagation environment cost-effectively. Most of the existing works on IRS focused on the passive beamforming (PB) optimization and performance enhancement without considering the multiple inter-IRS links cooperation that did not reveal the full preponderance of the multi-IRS-assisted reconfigurable communication system. In this work, we investigate a double-IRS-assisted multiple input single output (MISO) downlink communication system with the active beamforming (AB) and the cooperative PB design in the absence of direct link. Taking both the double-reflection links and the single-reflection links into account, the AB at the base station (BS) and the cooperative PB at two IRSs are jointly optimized to maximize the weight sum rate (WSR) under the constraint of the transmit power. To tackle the problem, we propose the double-IRS-assisted fractional programming block coordinate descent (D-FPBCD) method to find the sub-optimal solution with low complexity. We first reconstruct the original issue as a tractable one by the closed-form fractional programming (FP) approach, then, the prox-linear block coordinate descent (BCD) and successive convex approximation (SCA) techniques are used to find the sub-optimal solution. Finally, simulation results demonstrate the effectiveness of the proposed double-IRS-assisted wireless communication scheme.

Intelligent Reflecting Surface for Spectral Efficiency Maximization in the Multi-User MISO Communication Systems

This paper proposes an intelligent reflective surface (IRS) design scheme to improve the spectral efficiency (SE) for downlink multi-user (MU) multiple-input-single-output (MISO) system. IRS composed of low-cost reflecting elements is expected to be used in various scenarios in future wireless communication systems. It can be used even when the direct path is blocked by obstacles. Since the reflecting elements can only adjust the phase, the design of IRS matrix causes a non-convex problem. In this paper, the proposed schemes perform optimization by transforming a non-convex problem into a solvable convex function. Typically, base station-IRS (BS-IRS) channel can be assumed as the line-of-sight (LOS) channel environment. In this case, multi-user system suffers from performance degradation due to the LOS channel rank problem. To alleviate this problem, this paper considers deterministic scattering and sufficient spacing between the reflecting elements of the IRS. The simulation results show that the proposed schemes achieve better SE performance than the randomly generated IRS scheme. In addition, proposed minimum mean square error (MMSE)-based scheme can achieve high performance compared to other schemes even in a low-rank channel environment.

Beamforming Optimization for Intelligent Reflecting Surface-Aided MISO Communication Systems

This article considers an intelligent reflecting surface (IRS)-assisted point-to-point multiple-input single-output communication system. An IRS implemented by configurable phase shifters is used to assist the transmission from an access point (AP) equipped with multiple antennas to a user having a single antenna. We aim to jointly optimize the transmit beamforming at the AP and the reflect beamforming at the IRS to maximize the spectral efficiency of the system. The considered joint optimization problem can be decoupled into the transmit and reflect beamforming design problems by applying the maximum-ratio transmission strategy. The former has a closed-form expression, whereas the latter requires solving a nonconvex optimization problem. A known solution based on manifold optimization (MO) is proposed to solve the reflect beamforming design problem. Although the MO-based algorithm achieves higher spectral efficiency than the conventional semidefinite relaxation approach, it incurs high time complexity. On this basis, we address this issue by proposing a computationally efficient gradient projection (GP)-based algorithm for the reflect beamforming design problem. When low-resolution (e.g., 1–2 bits) phase shifters are adopted, we leverage an innovative probability learning technique on the basis of the cross-entropy (CE) framework to alleviate the performance loss caused by the use of low-resolution phase shifters. Simulation results demonstrate that the proposed GP-based algorithm nearly obtains the same spectral efficiency as the state-of-the-art MO-based algorithm at a low complexity. However, the running time is significantly reduced. When low-resolution phase shifters are employed, the proposed CE-based algorithm outperforms the test algorithms in terms of spectral and energy efficiency in various system configurations.

Co-Design for Overlaid MIMO Radar and Downlink MISO Communication Systems via Cramér–Rao Bound Minimization

This paper considers the problem of co-existence of collocated multiple-input multiple-output (MIMO) radar and downlink multiple-input single-output (MISO) communication systems, which share the same frequency band. As the interference caused by the communication systems would degrade the accuracy of radar target localization, we formulate the co-design of radar waveform and communication transmit weights by minimizing the Cramér-Rao Bound (CRB) of direction-of-arrival (DOA) estimation, subject to a set of constraints accounting for the worst-case signal-to-noise-plus-interference ratio (SINR), similarity and energy. However, the objective is nonconvex and all variables are coupled together in the SINR constraints, the formulated problem is thus NP-hard. Towards that end, a decentralized block successive upper-bound minimization (D-BSUM) method based on the decomposition theory is developed. More specifically, at each iteration of this algorithm, the radar waveform is obtained with the aid of the alternating direction method of multipliers (ADMM) algorithm, and communication weights are obtained by exploiting the semidefinite programs (SDP). It is also proved that the proposed SDP-based method gives a rank-one solution. Numerical simulations are conducted to evaluate the effectiveness of the proposed algorithm.

Quantized Feedback-Based Differential Signaling for Free-Space Optical Communication System

We propose a quantized feedback-based differential signaling scheme for $2 \times 1$ free space optical (FSO) multiple-input single-output communication systems. The analytical bit error rate (BER) for the proposed differential signaling scheme is derived and compared with the existing differential signaling scheme for FSO systems. The asymptotic BER of the proposed scheme is also determined and it is shown by using the asymptotic BER expression that the proposed scheme achieves full spatial diversity of two. Moreover, the effect of the feedback error on the performance of the proposed scheme is studied and it is demonstrated by simulation-based and analytical results that the proposed scheme significantly outperforms a previously proposed differential signaling scheme in terms of turbulence fades reduction for tolerable feedback errors.

Optimization of Energy Harvesting MISO Communication System With Feedback

Optimization of a point-to-point (p2p) multiple-input single-output (MISO) communication system is considered when both the transmitter (TX) and the receiver (RX) have energy harvesting (EH) capabilities. The RX is interested in feeding back the channel state information (CSI) to the TX to help improve the transmission rate. The objective is to maximize the throughput by a deadline, subject to the EH constraints at the TX and the RX. The throughput metric considered is an upper bound on the ergodic rate of the MISO channel with beamforming and limited feedback. Feedback bit allocation and transmission policies that maximize the upper bound on the ergodic rate are obtained. Tools from majorization theory are used to simplify the formulated optimization problems. Optimal policies obtained for the modified problem outperform the naive scheme in which no intelligent management of energy is performed.

Transmit antenna selection based strategies in MISO communication systems with low-rate channel state feedback

The performance of multiple-antenna communication systems is known to critically depend on the amount of channel state information (CSI) available at the transmitter. In the low-rate CSI feedback case, an important problem is what kind of information should be submitted to the transmitter in each feedback cycle and what is the optimal transmission strategy in this case. In this paper, we address this problem in the multiple-input single-output (MISO) case by analytically comparing the bit error rate (BER) performance of different low-rate feedback based transmitter strategies involving various combinations of transmit antenna selection, Alamouti's spacetime coding, and adaptive power allocation.