Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities

Recently there has been a flurry of research on the use of reconfigurable intelligent surfaces (RIS) in wireless networks to create smart radio environments. In a smart radio environment, surfaces are capable of manipulating the propagation of incident electromagnetic waves in a programmable manner to actively alter the channel realization, which turns the wireless channel into a controllable system block that can be optimized to improve overall system performance. In this article, we provide a tutorial overview of reconfigurable intelligent surfaces (RIS) for wireless communications. We describe the working principles of reconfigurable intelligent surfaces (RIS) and elaborate on different candidate implementations using metasurfaces and reflectarrays. We discuss the channel models suitable for both implementations and examine the feasibility of obtaining accurate channel estimates. Furthermore, we discuss the aspects that differentiate RIS optimization from precoding for traditional MIMO arrays highlighting both the arising challenges and the potential opportunities associated with this emerging technology. Finally, we present numerical results to illustrate the power of an RIS in shaping the key properties of a MIMO channel.Comment: to appear in the IEEE Transactions on Cognitive Communications and Networking (TCCN

Channel Estimation for mmWave Massive MIMO Based Access and Backhaul in Ultra-Dense Network

Millimeter-wave (mmWave) massive MIMO used for access and backhaul in ultra-dense network (UDN) has been considered as the promising 5G technique. We consider such an heterogeneous network (HetNet) that ultra-dense small base stations (BSs) exploit mmWave massive MIMO for access and backhaul, while macrocell BS provides the control service with low frequency band. However, the channel estimation for mmWave massive MIMO can be challenging, since the pilot overhead to acquire the channels associated with a large number of antennas in mmWave massive MIMO can be prohibitively high. This paper proposes a structured compressive sensing (SCS)-based channel estimation scheme, where the angular sparsity of mmWave channels is exploited to reduce the required pilot overhead. Specifically, since the path loss for non-line-of-sight paths is much larger than that for line-of-sight paths, the mmWave massive channels in the angular domain appear the obvious sparsity. By exploiting such sparsity, the required pilot overhead only depends on the small number of dominated multipath. Moreover, the sparsity within the system bandwidth is almost unchanged, which can be exploited for the further improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterpart, and it can approach the performance bound.Comment: 6 pages, 5 figures. Millimeter-wave (mmWave), mmWave massive MIMO, compressive sensing (CS), hybrid precoding, channel estimation, access, backhaul, ultra-dense network (UDN), heterogeneous network (HetNet). arXiv admin note: substantial text overlap with arXiv:1604.03695, IEEE International Conference on Communications (ICC'16), May 2016, Kuala Lumpur, Malaysi

Resource allocation for transmit hybrid beamforming in decoupled millimeter wave multiuser-MIMO downlink

This paper presents a study on joint radio resource allocation and hybrid precoding in multicarrier massive multiple-input multiple-output communications for 5G cellular networks. In this paper, we present the resource allocation algorithm to maximize the proportional fairness (PF) spectral efficiency under the per subchannel power and the beamforming rank constraints. Two heuristic algorithms are designed. The proportional fairness hybrid beamforming algorithm provides the transmit precoder with a proportional fair spectral efficiency among users for the desired number of radio-frequency (RF) chains. Then, we transform the number of RF chains or rank constrained optimization problem into convex semidefinite programming (SDP) problem, which can be solved by standard techniques. Inspired by the formulated convex SDP problem, a low-complexity, two-step, PF-relaxed optimization algorithm has been provided for the formulated convex optimization problem. Simulation results show that the proposed suboptimal solution to the relaxed optimization problem is near-optimal for the signal-to-noise ratio SNR <= 10 dB and has a performance gap not greater than 2.33 b/s/Hz within the SNR range 0-25 dB. It also outperforms the maximum throughput and PF-based hybrid beamforming schemes for sum spectral efficiency, individual spectral efficiency, and fairness index