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.

Cell-free massive multiple-input multiple-output (CF mMIMO) network has gained significant attention due to its ability to provide a ubiquitous connectivity over a wide geographical area without having any cell boundary. It serves the user equipments (UEs) by employing multiple access points (APs) connected to a central processing unit (CPU). In this paper, we focus on designing beam combiners for the APs under limited availability of the pilots. We argue that the received signal-to-interference plus noise (SINR) can be maximized if received signals at each AP are beam combined in a way that it aligns with the desired signal and nullifies the interference arising due to the pilot reuse. We use this strategy to improve three popular beam combiners for the CF mMIMO network based on the maximum ratio (MR), generalized MR (GMR), and reduced-complexity minimum mean square error (RCMMSE) criteria, respectively. The proposed distributed combiners handle the issue of pilot contamination and reduce the amount of signal exchanges between APs and CPU. We have analytically shown that the proposed combiners improve the spectral efficiency of their respective counterparts without increasing their overall complexities. We have corroborated this using simulations for both uncorrelated and correlated channels.

With the rapid evolution of vehicular communication technologies, reconfigurable intelligent surfaces (RISs) are being proposed to create a favorable propagation environment between the mobile vehicles and the communication infrastructure to achieve a seamless and reliable communication. In this work, we consider an RIS-aided multi-vehicular communication network and formulate a sum rate maximization problem. We propose to solve the maximization problem in two steps: i) by optimizing the RIS phase shifts for each vehicle using the successive refinement algorithm and ii) by proposing a novel cost metric for selecting the RIS elements dedicated to each vehicle. Further, to ensure fairness in utilizing the RIS elements for each vehicle, we propose two new algorithms, computing the number of RIS elements to be dedicated for each vehicle. Using simulations, we corroborate the effectiveness of the proposed solutions in providing a uniform achievable sum rate over a large distance under consideration.

Massive multiple-input and multiple-output (MIMO) systems are an integral part of 5G cellular networks. They employ a large antenna array at the base station (BS) to serve several users concurrently, which is expected to grow for the 5G and beyond networks. The performance of such systems relies on the quality of channel estimates, and therefore, an accurate channel state information (CSI) is critical for the deployments. However, with the increasing number of users, and antennas at BS, the pilot contamination increases, thus, inducing an extra noise to the received signal. This extra noise leads to an inaccurate and inefficient channel estimate. In this work, we propose a channel estimation technique based upon a specially designed untrained deep neural network (DNN), namely, deep image prior (DIP). We leverage the performance of the untrained DNN model for denoising the received signal and obtaining a superior channel estimation performance. We use normalized mean square error (NMSE) as a metric to corroborate the robustness of the proposed channel estimator against pilot contamination compared to the existing channel estimators.

Massive MIMO is an integral part of cellular communication systems in 5G and beyond. However, with the increase in the number of base station antennas, the need for radio-frequency chains to support parallel data streams also increases, leading to higher cost and lower energy efficiency. To address this issue, hybrid beam combining techniques have been devised, where constant phase shifters (CPSs) are used at the receiver to combine the signals coherently. Coherent combining requires a large number of CPSs and, as such, even a small amount of hardware impairment can lead to severe degradation in the combining performance. The use of these components needs to be done with practical consideration of non-linearities. In this paper, we consider a behavioral model for characterizing the impairments, and propose a CPS-based hybrid combining algorithm. Comparisons show that the proposed algorithm improves the throughput by utilizing a considerably lower number of CPSs. Considering both amplitude-to-amplitude modulation and amplitude-to-phase modulation distortions, it is shown that the proposed algorithm provides higher throughput than existing techniques. Error performance analysis also reveals that the proposed algorithm is robust to hardware impairments.

Owing to improved coverage and flexibility, the radio access network (RAN) functionalities are being virtualized in a sense that the base station will merely act as a radio unit, and all the baseband processing will occur in the cloud. Therefore, the baseband signal-processing algorithms need to be designed in a way that it can match the latency requirements. In this paper, we address one of the inherent but complex issues in baseband signal processing, namely, the log log-likelihood ratio (LLR) computation. In general, soft-decision rules are used for calculating the LLRs, which is computationally expensive. Thus, we attempt to exploit the benefits of hard-decision based LLRs for proposing a framework that uses soft decision only when the received symbols are closed to the decision boundary; otherwise, the framework uses hard decision. This helps us to keep the complexity low while meeting the desirable error performance. These schemes are suitable for incorporation in virtual RAN systems while considering appropriate QoS requirements.

Neighborhood search algorithms have been proposed for low complexity detection in large/massive multipleinput multiple-output systems. They iteratively search for the vector, which minimizes the maximum likelihood (ML) cost in a fixed neighborhood. However, the ML solution may not lie in the searched space and also the search may go through a large number of intermediate vectors. Motivated by this, we first propose to cut down the size of the neighborhood so that the complexity of such algorithms can be reduced. Second, we also look for an update which is not restricted to be in a fixed neighborhood. This helps in improving the error performance. For the first purpose, we propose a metric and a few selection rules to decide whether or not to include a vector in the neighborhood. We use the indices of, say K, largest components of the metric for generating a reduced neighborhood set, which is used to reduce the complexity of the existing algorithms while maintaining their error performance. Furthermore, this reduced set facilitates the proposal of two new search algorithms. Simulation results show that the proposed algorithms have a much better error performance and also lower complexity compared with the existing algorithms.

Breadth first tree search (BFTS) algorithms are known to provide a close to maximum likelihood (quasi-ML) solution at a low-complexity if the received sequence is detected in the right sequence order. However, finding the right sequence order has an exponential overhead. In view of this, we propose to repeatedly apply a BFTS algorithm to all sequence orders. Since it will test all the orders, it is expected to achieve quasi-ML performance. However, this will increase the complexity because of redundant iterations. The complexity can be reduced if we can stop the iterations as soon as a quasi-ML solution is achieved. For this, we propose two stopping rules, one relies on a constellation based heuristic and the other one uses the distribution of ML cost. It is found that their complexity curves have a cross-over point. Thus, a combination of the two rules provides a quasi-ML error performance at a low-complexity for uncoded as well as coded systems. We further show that the proposed stopping rule can reduce the complexity of depth first tree search algorithms also. Last, for large MIMO systems, compared with existing algorithms, it is found to be exceptionally better in terms of both error performance and complexity.

Neighborhood search algorithms have been proposed for detection in large multiple-input multiple-output systems. They iteratively search for the best vector in a fixed neighborhood. A better way could be to look for an update which is not restricted to a fixed neighborhood. Motivated by this, we formulate a problem to maximize the reduction in maximum likelihood (ML) cost and use it to derive an expression for updating the current solution. Using this update and a likelihood function regarding the locations of errors, we propose an unconstrained likelihood ascent search (ULAS) algorithm. ULAS seeks to provide the maximum reduction in ML cost by finding an update which is not restricted to be in a fixed neighborhood. Using simulations, the proposed algorithm has been shown to provide better error performance for uncoded systems than existing algorithms, at lower complexity. We also show that ULAS is amenable to lattice reduction, which helps in obtaining two variants leading to further improvements in performance.

Neighborhood search algorithms have been proposed for detection in large/massive multiple-input multiple-output (MIMO) systems. They iteratively search for the best vector in a fixed neighborhood. However, the ML solution may not lie in the searched space or the search may take a large number of intermediate vectors to converge. Instead of searching in a fixed neighborhood, a better way will be to look for an update which is not restricted to be in a fixed neighborhood. Motivated by this, we formulate an optimization problem to maximize the reduction in ML cost and use it to derive an expression for updating the solution. We use a metric based on the channel matrix and the error vector to determine the likelihood of a symbol being in error. Using this likelihood and the update, we propose a likelihood ascent search (LAS) algorithm to find an update which is not restricted to be in a fixed neighborhood and seeks to provide maximum reduction in ML cost. This process continues till there is a reduction in the ML cost. Compared to existing LAS based algorithms, it is found to provide better error performance, that too at a lower complexity.

Neighborhood search algorithms such as likelihood ascent search (LAS) and reactive tabu search (RTS) have been proposed for low complexity detection in multiple-input multiple-output (MIMO) systems having a large number of antennas. Both these algorithms are iterative and search for the vector which minimizes the maximum likelihood (ML) cost in the neighborhood. In this paper we propose a way to reduce the size of the neighborhood. For this, we propose a metric and a selection rule to decide whether or not to include a vector in the neighborhood. We use the indices of, sayK, largest components of the metric for generating a reduced neighborhood set. This reduced set is used to evaluate the performance of the resulting LAS and RTS algorithms. Simulation results show that this reduces the complexity significantly while maintaining the error performance. We also show that the proposed reduced neighborhood algorithms can make MIMO systems with several hundred antenna pairs feasible.