Integrated sensing and communication (ISAC) has emerged as a highly promising technology for future sixth-generation networks owing to its potential to optimize spectrum resources at minimal hardware costs. One promising realization of ISAC is the multi-static design, where several distributed nodes jointly enhance sensing and communication performance. However, the current multi-static designs fall short of maintaining accurate sensing performance while fulfilling the simultaneous uplink/downlink communication requirements. To solve this efficiently, we consider a reconfigurable intelligent surface (RIS) aided cell-free massive multiple-input multiple-output (CF-mMIMO) operating in network-assisted full-duplex (NAFD) mode and enhance the weighted sensing and communication rates of the network by efficiently designing transmit/receive beamformers at the APs and optimizing the RIS phase-shifts. Since the formulated problem is highly non-convex, we leverage tools such as quadratic-transform and succesive-convex approximation to convert the objective into a tractable form and then utilize Lagrange multipliers method to derive inter-AP-interference suppression enabled closed-form expressions for transmit and receive beamformers at the APs. Then, we propose a variance-reduced projected-gradient method, that relies on closed-form gradient updates to update the reflect beamformers at the RIS based on statistical-CSI. Simulation results demonstrate the effectiveness of the proposed design in achieving excellent ISAC performance at low computational complexity.

Owing to the inherent abilities of active simultaneously transmitting and reflecting reconfigurable intelligent surfaces (aSTAR-RIS) to simultaneously reflect, refract, and amplify the incident signals, it is being used to enhance the user experience in a wireless network. In this work, we consider aSTAR-RIS to enhance the energy efficiency (EE) of cell-free massive multiple-input multiple-output (CF-mMIMO) network in the presence of hardware impairments (HWI) and electromagnetic interference (EMI). We aim to jointly optimize the respective transmit and reflect beamformers at the access points (APs) and aSTAR-RIS and formulate an EE maximization problem. The maximization problem is solved by leveraging fractional programming tools to decouple the transmit and reflect beamformers and, subsequently, solve it using an alternating optimization (AO) approach. To reduce the computational complexity of AO, we derive closed-form updates for the HWI-aware transmit beamformers at the APs and resort to stochastic projected gradient descent method with closed-form step size updates to optimize the reflect beamformers at aSTAR-RIS. We also provide an effective AP selection scheme to reduce the fronthaul signaling and further enhance the EE of the network. Simulation results corroborate the effectiveness of the proposed approach in maximizing the EE of the considered network.

The alliance of reconfigurable intelligent surfaces (RISs) and cell-free massive multiple-input multiple-output (CF-mMIMO) is a promising architecture for the next-generation wireless networks. The idea is to deploy a large number of distributed access points (APs) assisted by a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) that provides 360° coverage to user equipments (UEs) located on the front and back side of the RIS, over the same time-frequency resources. In this paper, we focus on the joint active and passive beamforming design in a downlink STAR-RIS-assisted CF-mMIMO network so that the energy efficiency of the network can be maximized. For this, we employ fractional programming and Lagrangian dual transform to decouple the active and passive beamformers and solve it via an alternating optimization (AO). To keep the computational complexity in check, we have derived a closed-form update for the active beamformers at the APs and also obtained a computationally inexpensive line-search-based general inertial projected gradient (GIPG) algorithm to optimize the passive beamformer at the STAR-RIS. Using simulations, we corroborate the effectiveness of the proposed beamforming algorithm in maximizing the EE of the network in the presence of hardware imperfections at APs, UEs, and STAR-RIS.