Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces
Abstract : This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs. This problem is posed as a joint optimization problem of transmit beamforming and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, two iterative algorithms are proposed for the single-user case and multi-user case. For the single-user case, the phase optimization problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the optimal RIS on-off status is obtained by using the dual method. For the multi-user case, a low-complexity greedy searching method is proposed to solve the RIS on-off optimization problem. Simulation results show that the proposed scheme achieves up to 33% and 68% gains in terms of the energy efficiency in both single-user and multi-user cases compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.
? The authors demonstrated that spatial microwave modulators can efficiently shape, in a passive way, complex existing microwave fields in reverberating environments with a non-coherent energy feedback.
? In many urban and densely populated cities worldwide, in fact, there exist localized dead zones where the signal quality is not sufficiently good.
? There exist no experimentally-validated channel models that provide wireless researchers with accurate and realistic information on the path-loss, shadowing, and fast-fading statistics for RISs.
? These devices, however, are expected to communicate the sensed data to fusion centers, which are then in charge of the subsequent processing and analysis.
? Due to high data rate demand and massive numbers of users, energy consumption has become a challenging problem in the design of future wireless networks.
? To maximize the energy efficiency for a single user, a suboptimal solution is obtained by using a low-complexity algorithm that iteratively solves two, joint subproblems.
? The energy efficiency optimization problem, an iterative algorithm with low complexity is proposed via alternatingly optimizing the phase vector, beamforming vector, and RIS on-off vector.
? The nonlinear integer optimization problem is NP-hard in general, it is hard to obtain the globally optimal solution with polynomial complexity.
• The concept of coding meta-materials is proposed in for manipulating EM waves by changing the phase response of the surface elements.
• Liquid-crystal reconfigurable meta-surface-based reflectors are proposed in by exploiting electronically tunable liquid crystals to enable the real-time reconfigurability of the meta-surfaces for beam steering.
• The concept of software-controlled HyperSurfaces is proposed in with the aim of enabling full EM manipulation of the radio waves.
• The same authors generalized their programmable wireless environment concept considering a general multi-user scenario and proposed solutions for interference minimization, eavesdropping, and multipath mitigation.
? In consequence, energy efficiency, defined as the ratio of spectral efficiency over power consumption, has emerged as an important performance index for deploying green and sustainable wireless networks.
? However, the effective deployment of energy-efficient RIS systems faces several challenges ranging from performance characterization to network optimization.
? Moreover, the proposed DRIS scheme achieves almost the same performance as the EXH-DRIS scheme, which indicates that the proposed DRIS can achieve the near optimum solution.
? It is also found that DRIS achieves better performance than CRIS, which indicates the benefit of distributed deployment of RISs.
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