Energy-Efficient Cell-Free Network Assisted by Hybrid RISs

Abstract

This paper investigates the optimization of reconfigurable intelligent surface (RIS) in an integrated sensing and communication (ISAC) system. To meet the demand of growing number of devices, power domain non-orthogonal multiple access (NOMA) is considered. However, traditional NOMA with a large number of devices is challenging due to large decoding delay and propagation error introduced by successive interference cancellation (SIC). Thus, OMA is integrated into NOMA to support more devices. We formulate a max-min problem to optimize the sensing beampattern with constraints on communication rate, through joint power allocation, active beamforming and RIS phase shift design. To solve the non-convex problem with a non-smooth objective function, we propose a low complexity alternating optimization (AO) algorithm, where a closed form expression for the intra-cluster power allocation (intra-CPA) is derived, and penalty and successive convex approximation (SCA) methods are used to optimize the beamforming and phase shift design. Simulation results show the effectiveness of the proposed algorithm in terms of improving minimum beampattern gain (MBPG) compared with other baselines. Furthermore, the trade-off between sensing and communication is analyzed and demonstrated in the simulation results.

Holographic MIMO with Integrated Sensing and Communication for Energy-Efficient Cell-Free 6G Networks

“…Therefore, two channels are required to serve user u ∈ U: One channel is from the first HMIMO BS to user u, and another channel is from the second HMIMO BS to user u. Considering the integration of the HMIMO BSs in the CF network, the received signal for user u is given by [10], [29]:…”

“…Reconfigurable intelligent surfaces (RISs) have been integrated into the CF networks to substitute some of the BSs so as to improve the system capacity [6], [8] and energy efficiency [5]- [7], [9]- [11]. However, the multiplicative fading effect caused by an RIS results in an extremely high path loss in the cascaded channels, therefore, it is realistically challenging to attain the ideal capacity improvements promised by RISs [10]. Consequently, it is advantageous to consider HMIMO BS as a prospective candidate for designing energy-efficient wireless systems that can provide flexibility and adaptability in dynamic environments.…”

“…Several recent works [2], [7], [8], [10], [15], [20], [21], [26]- [28] investigated the problems of leveraging RISs in CF networks, deploying RHS in the HMIMO-based systems, and employing ISAC-based approaches in both the CF networks and HMIMO-based systems. For example, the work in [2] developed an artificial intelligence (AI) framework for HMIMOenabled wireless networks that utilizes joint sensing, localiza-tion, and communication to allocate power for beamforming to serve the users based on the exact location of the users.…”

“…The work in [7] developed an RIS-aided CFMM system that solves the max-min fairness problem by maximizing the achievable rate for all users by optimizing both the beamforming at the APs and the phase shifts at the RISs. The authors of [10] proposed a hybrid RIS-aided CF network that maximizes energy efficiency by formulating an optimization problem consisting of joint beamforming and coefficients of RIS. The authors in [15] suggested a holographic ISAC-based system that employs amplitude-controlled metamaterial antennas to resolve spectrum congestion problems.…”

“…Therefore, most of the works either integrate RIS in the CF networks to achieve energy efficiency or incorporate RHS in the HMIMO-enabled networks for beamforming. However, none of the prior works [2], [7], [8], [10], [15], [20], [21], [26]- [28] have considered the integration of HMIMO BS in CF networks in which each BS is replaced with an HMIMO BS which ensures an energy-efficient CF network for serving the users. To the best of our knowledge, no prior work has empowered the HMIMO BSs in the CF networks to ensure an energy-efficient system employing ISAC.…”