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Register a new userOn the Effective Capacity of IRS-assisted wireless communication
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We consider futuristic, intelligent reflecting surfaces (IRS)-aided communication between a base station (BS) and a user equipment (UE) for two distinct scenarios: a single-input, single-output (SISO) system whereby the BS has a single antenna, and a multi-input, single-output (MISO) system whereby the BS has multiple antennas. For the considered IRS-assisted downlink, we compute the effective capacity (EC), which is a quantitative measure of the statistical quality-of-service (QoS) offered by a communication system experiencing random fading. For our analysis, we consider the two widely-known assumptions on channel state information (CSI) -- i.e., perfect CSI and no CSI, at the BS. Thereafter, we first derive the distribution of the signal-to-noise ratio (SNR) for both SISO and MISO scenarios, and subsequently derive closed-form expressions for the EC under perfect CSI and no CSI cases, for both SISO and MISO scenarios. Furthermore, for the SISO and MISO systems with no CSI, it turns out that the EC could be maximized further by searching for an optimal transmission rate $r^*$, which is computed by exploiting the iterative gradient-descent method. We provide extensive simulation results which investigate the impact of the various system parameters, e.g., QoS exponent, power budget, number of transmit antennas at the BS, number of reflective elements at the IRS etc., on the EC of the system.
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In this work, we propose a beam training codebook for Reconfigurable Intelligent Surface (RIS) assisted mmWave uplink communication. Beam training procedure is important to establish a reliable link between user node and Access point (AP). A codebook based training procedure reduces the search time to obtain best possible phase shift by RIS controller to align incident beam at RIS in the direction of receiving node. We consider a semi passive RIS to assist RIS controller with a feedback of minimum overhead. It is shown that the procedure detects a mobile node with high probability in a short interval of time. Further we use the same codebook at user node to know the desired direction of communication via RIS.
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This paper presents the analytic framework for evaluating the ergodic capacity (EC) of the reconfigurable intelligent surface (RIS) assisted systems. Moreover, high-signal-to-noise-ratio and high-number of reflection units (RUs) approximations for the EC are provided. Finally, the special case in which the RIS is equipped with a single RU is investigated. Our analysis is verified through respective Monte Carlo simulations, which highlight the accuracy of the proposed framework.
Combining intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) is an effective solution to enhance communication coverage and energy efficiency. In this paper, we focus on an IRS-assisted NOMA network and propose an energy-efficient algorithm to yield a good tradeoff between the sum-rate maximization and total power consumption minimization. We aim to maximize the system energy efficiency by jointly optimizing the transmit beamforming at the BS and the reflecting beamforming at the IRS. Specifically, the transmit beamforming and the phases of the low-cost passive elements on the IRS are alternatively optimized until the convergence. Simulation results demonstrate that the proposed algorithm in IRS-NOMA can yield superior performance compared with the conventional OMA-IRS and NOMA with a random phase IRS.
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