MISO Wireless Communication Systems via Intelligent Reflecting Surfaces : (Invited Paper)

Abstract

Intelligent reflecting surfaces (IRSs) have received considerable attention from the wireless communications research community recently. In particular, as low-cost passive devices, IRSs enable the control of the wireless propagation environment, which is not possible in conventional wireless networks. To take full advantage of such IRS-assisted communication systems, both the beamformer at the access point (AP) and the phase shifts at the IRS need to be optimally designed. However, thus far, the optimal design is not well understood. In this paper, a point-to-point IRS-assisted multiple-input single-output (MISO) communication system is investigated. The beamformer at the AP and the IRS phase shifts are jointly optimized to maximize the spectral efficiency. Two efficient algorithms exploiting fixed point iteration and manifold optimization techniques, respectively, are developed for solving the resulting non-convex optimization problem. The proposed algorithms not only achieve a higher spectral efficiency but also lead to a lower computational complexity than the state-of-the-art approach. Simulation results reveal that deploying large-scale IRSs in wireless systems is more efficient than increasing the antenna array size at the AP for enhancing both the spectral and the energy efficiency.

Enabling Secure Wireless Communications via Intelligent Reflecting Surfaces

“…As the proposed BnB algorithm is guaranteed to converge to the optimal solution of P 1 , it can be regarded as a performance benchmark for existing suboptimal algorithms. As discussed in Remark 3, the manifold optimization-based algorithm proposed in [8] achieves the highest spectral efficiency among the existing approaches for beamforming in IRS-assisted MISO communication systems. As can be observed in Fig.…”

“…Remark 3: The search space defined by the UMCs in P 1 was identified as a complex circle manifold [8]. By translating the classical conjugate gradient descent methods in the Euclidean space to the Riemannian manifold, a locally optimal solution was obtained for P 1 .…”

“…By translating the classical conjugate gradient descent methods in the Euclidean space to the Riemannian manifold, a locally optimal solution was obtained for P 1 . To the best of the authors' knowledge, the manifold optimization-based algorithm achieves the highest spectral efficiency among all the existing approaches [8]. In this paper, we propose a BnB algorithm that yields the globally optimal solution of P 1 and we study the degree of optimality of the manifold optimization.…”

“…Promisingly, our simulation results confirm that the deployment of IRSs significantly improves the spectral efficiency of the considered system. More importantly, by taking the proposed BnB algorithm as the performance benchmark, the existing low-complexity manifold optimization-based algorithm in [8] is shown to be near-optimal.…”

IRS-Enhanced OFDMA: Joint Resource Allocation and Passive Beamforming Optimization

“…Fixing {α k,q,n } and {p q,n }, update {φ q } via SCA. 5 until R converges to a prescribed accuracy. 6 Record the optimal solution as {α…”