We propose a novel resource-efficient over-the-air (OTA) computation framework to address the huge fronthaul computational and control overhead requirements in cell-free massive multiple-input multiple-output (MIMO) networks. We show that the global sufficient statistics to decode the data symbols can be computed OTA using the locally available information at the access points (APs). We provide the essential signal processing aspects at the APs and the central processing unit (CPU) to facilitate the OTA computation of sufficient statistics. The proposed framework scales effectively with an increase in the number of APs. We also make a comprehensive study of the benefits of an OTA framework compared to a conventional digital fronthaul in terms of the overhead associated in transferring the sufficient statistics from the APs to the CPU. To evaluate the performance of the OTA framework, we give closed-form expressions for the mean-square error (MSE) of the estimators of sufficient statistics and the overall data estimator. Furthermore, we assess the symbol error rate (SER) and bit error rate (BER) of the user equipment (UEs) data to demonstrate the efficacy of our method, and benchmark them against the state-of-the-art wired fronthaul networks.

We propose a novel resource efficient analog over-the-air (OTA) computation framework to address the demanding requirements of the uplink (UL) fronthaul between the access points (APs) and the central processing unit (CPU) in cell-free massive multiple-input multiple-output (MIMO) systems. We discuss the drawbacks of the wired and wireless fronthaul solutions, and show that our proposed mechanism is efficient and scalable as the number of APs increases. We present the transmit precoding and two-phase power assignment strategies at the APs to coherently combine the signals OTA in a spectrally efficient manner. We derive the statistics of the APs' locally available signals which enable us to to obtain the analytical expressions for the Bayesian and classical estimators of the OTA combined signals. We empirically evaluate the normalized mean square error (NMSE), symbol error rate (SER), and the coded bit error rate (BER) of our developed solution and benchmark against the state-of-the-art wired fronthaul based system.

Cell-free massive multiple-input-multiple-output (CF-mMIMO) is a next-generation wireless access technology that offers superior coverage and spectral efficiency compared to conventional MIMO. With many future applications in unlicensed spectrum bands, networks will likely experience and may even be limited by out-of-system (OoS) interference. The OoS interference differs from the in-system interference from other serving users in that for OoS interference, the associated pilot signals are unknown or non-existent, which makes estimation of the OoS interferer channel difficult.In this paper, we propose a novel sequential algorithm for the suppression of OoS interference for uplink CF-mMIMO with a stripe (daisy-chain) topology. The proposed method has comparable performance to that of a fully centralized interference rejection combining algorithm but has substantially less fronthaul load requirements.

RadioWeaves network operates a large number of distributed antennas using cell-free architecture to provide high data rates and support a large number of users. Operating this network in an energy-efficient manner in the limited available spectrum is crucial. Therefore, we consider energy efficiency (EE) maximization of a RadioWeaves network that shares spectrum with a collocated primary network in underlay mode. To simplify the problem, we lower bound the non-convex EE objective function to form a convex problem. We then propose a downlink power allocation policy that maximizes the EE of the secondary RadioWeaves network subject to power constraint at each access point and interference constraint at each primary user. Our numerical results investigate the secondary system’s performance in interference, power, and EE constrained regimes with correlated fading channels. Furthermore, they show that the proposed power allocation scheme performs significantly better than the simpler equal power allocation scheme.

RadioWeaves, in which distributed antennas with integrated radio and compute resources serve a large number of users, is envisioned to provide high data rates in next-generation wireless systems. In this paper, we develop a physical layer abstraction model to evaluate the performance of different RadioWeaves deployment scenarios. This model helps speed up system-level simulators of the RadioWeaves and is made up of two blocks. The first block generates a vector of signal-to-interference-plus-noise ratios (SINRs) corresponding to each coherence block, and the second block predicts the packet error rate corresponding to the SINRs generated. The vector of SINRs generated depends on different parameters such as the number of users, user locations, antenna configurations, and precoders. We have also considered different antenna gain patterns, such as omni-directional and directional microstrip patch antennas. Our model exploits the benefits of exponential effective SINR mapping (EESM). We study the robustness and accuracy of the EESM for RadioWeaves.

Cell-free massive multiple-input-multiple-output (mMIMO) is an emerging technology for beyond 5G with its promising features such as higher spectral efficiency and superior spatial diversity as compared to conventional multiple-input-multiple-output (MIMO) technology. The main working principle of cell-free mMIMO is that many distributed access points (APs) cooperate simultaneously to serve all the users within the network without creating cell boundaries. This paper considers the uplink of a cell-free mMIMO system utilizing the radio stripe network architecture with a sequential fronthaul between the APs. A novel uplink sequential processing algorithm is developed, which is proved to be optimal in both the maximum spectral efficiency (SE) and the minimum mean square error (MSE) sense. A detailed quantitative analysis of the fronthaul requirement or signaling of the proposed algorithm and its comparison with competing sub-optimal algorithms is provided. Key conclusions and implications are summarized in the form of corollaries. Based on the analytical and numerical simulation results, we conclude that the proposed scheme can significantly reduce the fronthaul signaling, without compromising the communication performance.

In this paper, we consider the weighted sum-rate (WSR) maximization problem in a cognitive relaying network under imperfect channel state information (CSI). The secondary network employs cooperative nonorthogonal multiple access (NOMA). We investigate the optimal power allocation and relay precoder design for the secondary network that shares spectrum with the primary network while keeping interference to the primary users below a threshold. We exploit the relation between the WSR and weighted mean-square error (WMSE) to solve the equivalent WSR maximization problem. We consider a practical scenario, where the available CSI of all the links are imperfect. The performance of the proposed solution is robust to the errors in the CSI. The optimization problem is intractable in its original form; however, we reformulate it as a tractable, equivalent optimization problem via certain transformations and approximations. We illustrate the robust performance of the proposed design via numerical simulation results.

In this paper, we investigate the sum-rate maximization problem in a cooperative relay network, which employs non-orthogonal multiple access (NOMA). Specifically, we address the problem of optimal power allocation and relay precoder design. The corresponding optimization problem turns out to be non-convex. In order to get around the difficulty to deal with non-convexity, we propose a minorization-maximization technique based algorithm to jointly optimize source power allocation and relay precoder design. We then solve the problem to optimally allocate power at source and design the precoder at relay. For the case of two destination nodes, we propose a grid search based approach. Simulation results are provided to illustrate the performance of the proposed algorithm.

In this paper, we present the optimal power allocation and relay precoder design for a cognitive relay network employing non-orthogonal multiple access (NOMA) technique. The secondary user (SU) nodes share the spectrum with the licensed primary user (PU) while keeping the interference at PU nodes below a permissible threshold. The cognitive nodes communicate with each other with the assistance of a cognitive amplify and forward (AF) relay equipped with multiple antennas. The proposed source power allocation and relay precoder design aim at maximizing the sum-rate of the cognitive destination nodes while maintaining the interference to the PU node below the specified threshold. Given that the SU transmit node has to restrict its transmit power in order to reduce the interference to the PU, use of NOMA can significantly improve the SU performance. The resulting optimization problem is non-convex in its original form. In order to circumvent the difficulties associated with the non-convexity, we employ minorization-maximization technique to obtain convex approximations to some of the non- convex functions appearing in the problem. We then solve this problem to obtain the optimal resource allocation policy at source and precoder design at the relay. Numerical results are provided to illustrate the performance of the proposed design for various operating conditions and parameters.