Sequential Processing in Cell-free Massive MIMO Uplink with Limited Memory Access Points

Cell-free massive multiple-input multiple-output (MIMO) is an emerging technology that will reshape the architecture of next-generation networks. This paper considers the sequential fronthaul, whereby the access points (APs) are connected in a daisy chain topology with multiple sequential processing stages. With this sequential processing in the uplink, each AP refines users' signal estimates received from the previous AP based on its own local received signal vector. While this processing architecture has been shown to achieve the same performance as centralized processing, the impact of the limited memory capacity at the APs on the store and forward processing architecture is yet to be analyzed. Thus, we model the received signal vector compression using rate-distortion theory to demonstrate the effect of limited memory capacity on the optimal number of APs in the daisy chain fronthaul. Without this memory constraint, more geographically distributed antennas alleviate the adverse effect of large-scale fading on the signal-to-interference-plus-noise-ratio (SINR). However, we show that in case of limited memory capacity at each AP, the memory capacity to store the received signal vectors at the final AP of this fronthaul becomes a limiting factor. In other words, we show that when deciding on the number of APs to distribute the antennas, there is an inherent trade-off between more macro-diversity and compression noise power on the stored signal vectors at the APs. Hence, the available memory capacity at the APs significantly influences the optimal number of APs in the fronthaul.

Zeroth-order Asynchronous Learning with Bounded Delays with a Use-case in Resource Allocation in Communication Networks

Distributed optimization has experienced a significant surge in interest due to its wide-ranging applications in distributed learning and adaptation. While various scenarios, such as shared-memory, local-memory, and consensus-based approaches, have been extensively studied in isolation, there remains a need for further exploration of their interconnections. This paper specifically concentrates on a scenario where agents collaborate toward a unified mission while potentially having distinct tasks. Each agent's actions can potentially impact other agents through interactions. Within this context, the objective for the agents is to optimize their local parameters based on the aggregate of local reward functions, where only local zeroth-order oracles are available. Notably, the learning process is asynchronous, meaning that agents update and query their zeroth-order oracles asynchronously while communicating with other agents subject to bounded but possibly random communication delays. This paper presents theoretical convergence analyses and establishes a convergence rate for the proposed approach. Furthermore, it addresses the relevant issue of deep learning-based resource allocation in communication networks and conducts numerical experiments in which agents, acting as transmitters, collaboratively train their individual (possibly unique) policies to maximize a common performance metric.

A Deep Learning Based Resource Allocator for Communication Systems with Dynamic User Utility Demands

Deep learning (DL) based resource allocation (RA) has recently gained a lot of attention due to its performance efficiency. However, most of the related studies assume an ideal case where the number of users and their utility demands, e.g., data rate constraints, are fixed and the designed DL based RA scheme exploits a policy trained only for these fixed parameters. A computationally complex policy retraining is required whenever these parameters change. Therefore, in this paper, a DL based resource allocator (ALCOR) is introduced, which allows users to freely adjust their utility demands based on, e.g., their application layer. ALCOR employs deep neural networks (DNNs), as the policy, in an iterative optimization algorithm. The optimization algorithm aims to optimize the on-off status of users in a time-sharing problem to satisfy their utility demands in expectation. The policy performs unconstrained RA (URA) -- RA without taking into account user utility demands -- among active users to maximize the sum utility (SU) at each time instant. Based on the chosen URA scheme, ALCOR can perform RA in a model-based or model-free manner and in a centralized or distributed scenario. Derived convergence analyses provide guarantees for the convergence of ALCOR, and numerical experiments corroborate its effectiveness.

Distributed Adaptive Norm Estimation for Blind System Identification in Wireless Sensor Networks

Distributed signal-processing algorithms in (wireless) sensor networks often aim to decentralize processing tasks to reduce communication cost and computational complexity or avoid reliance on a single device (i.e., fusion center) for processing. In this contribution, we extend a distributed adaptive algorithm for blind system identification that relies on the estimation of a stacked network-wide consensus vector at each node, the computation of which requires either broadcasting or relaying of node-specific values (i.e., local vector norms) to all other nodes. The extended algorithm employs a distributed-averaging-based scheme to estimate the network-wide consensus norm value by only using the local vector norm provided by neighboring sensor nodes. We introduce an adaptive mixing factor between instantaneous and recursive estimates of these norms for adaptivity in a time-varying system. Simulation results show that the extension provides estimation results close to the optimal fully-connected-network or broadcasting case while reducing inter-node transmission significantly.

Cell-free mMIMO Support in the O-RAN Architecture: A PHY Layer Perspective for 5G and Beyond Networks

To keep supporting next-generation requirements, the radio access infrastructure will increasingly densify. Cell-free (CF) network architectures are emerging, combining dense deployments with extreme flexibility in allocating resources to users. In parallel, the Open Radio Access Networks (O-RAN) paradigm is transforming RAN towards an open, intelligent, virtualized, and fully interoperable architecture. This paradigm brings the needed flexibility and intelligent control opportunities for CF networking. In this paper, we document the current O-RAN terminology and contrast it with some common CF processing approaches. We then discuss the main O-RAN innovations and research challenges that remain to be solved.

Sampling Rate Offset Estimation and Compensation for Distributed Adaptive Node-Specific Signal Estimation in Wireless Acoustic Sensor Networks

Sampling rate offsets (SROs) between devices in a heterogeneous wireless acoustic sensor network (WASN) can hinder the ability of distributed adaptive algorithms to perform as intended when they rely on coherent signal processing. In this paper, we present an SRO estimation and compensation method to allow the deployment of the distributed adaptive node-specific signal estimation (DANSE) algorithm in WASNs composed of asynchronous devices. The signals available at each node are first utilised in a coherence-drift-based method to blindly estimate SROs which are then compensated for via phase shifts in the frequency domain. A modification of the weighted overlap-add (WOLA) implementation of DANSE is introduced to account for SRO-induced full-sample drifts, permitting per-sample signal transmission via an approximation of the WOLA process as a time-domain convolution. The performance of the proposed algorithm is evaluated in the context of distributed noise reduction for the estimation of a target speech signal in an asynchronous WASN.

SPIRAL: A Superlinearly Convergent Incremental Proximal Algorithm for Nonconvex Finite Sum Minimization

We introduce SPIRAL, a SuPerlinearly convergent Incremental pRoximal ALgorithm, for solving nonconvex regularized finite sum problems under a relative smoothness assumption. In the spirit of SVRG and SARAH, each iteration of SPIRAL consists of an inner and an outer loop. It combines incremental and full (proximal) gradient updates with a linesearch. It is shown that when using quasi-Newton directions, superlinear convergence is attained under mild assumptions at the limit points. More importantly, thanks to said linesearch, global convergence is ensured while it is shown that unit stepsize will be eventually always accepted. Simulation results on different convex, nonconvex, and non-Lipschitz differentiable problems show that our algorithm as well as its adaptive variant are competitive to the state of the art.

Transfer functions of FXLMS-based Multi-channel Multi-tone Active Noise Equalizers

Multi-channel Multi-tone Active Noise Equalizers can achieve different user-selected noise spectrum profiles even at different space positions. They can apply a different equalization factor at each noise frequency component and each control point. Theoretically, the value of the transfer function at the frequencies where the noise signal has energy is determined by the equalizer configuration. In this work, we show how to calculate these transfer functions with a double aim: to verify that at the frequencies of interest the values imposed by the equalizer settings are obtained, and to characterize the behavior of these transfer functions in the rest of the spectrum, as well as to get clues to predict the convergence behaviour of the algorithm. The information provided thanks to these transfer functions serves as a practical alternative to the cumbersome statistical analysis of convergence, whose results are often of no practical use.

GEVD-based Low-Rank Channel Covariance Matrix Estimation and MMSE Channel Estimation for Uplink Cellular Massive MIMO Systems

Uplink channel estimation is a crucial component for the performance of cellular massive MIMO systems. However, when the number of user equipments (UEs) grows, the sharing of the available resources causes interference between UEs in neighboring cells. Minimum mean squared error (MMSE) channel estimators have been proposed to mitigate this interference, but these require estimates of the channel covariance matrices. Therefore, a new channel covariance matrix estimator for low-rank channel covariance matrices is presented in this paper, using a generalized eigenvalue decomposition (GEVD) of two covariance matrices that can be estimated from the available uplink data. The requirements for the systems are minimal and, except for synchronization, there is no need for communication between the different cells and no prior knowledge on the background noise is required. Approximate MMSE estimators are also derived based on the newly proposed channel covariance matrix estimator. The effectiveness of the proposed methods is demonstrated in numerical simulations.