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This paper proposes a novel scheme for cell-free massive multiple-input multiple-output (CFmMIMO) networks to support any federated learning (FL) framework. This scheme allows each instead of all the iterations of the FL framework to happen in a large-scale coherence time to guarantee a stable operation of an FL process. To show how to optimize the FL performance using this proposed scheme, we consider an existing FL framework as an example and target FL training time minimization for this framework. An optimization problem is then formulated to jointly optimize the local accuracy, transmit power, data rate, and users’ processing frequency. This mixed-timescale stochastic nonconvex problem captures the complex interactions among the training time, and transmission and computation of training updates of one FL process. By employing the online successive convex approximation approach, we develop a new algorithm to solve the formulated problem with proven convergence to the neighbourhood of its stationary points. Our numerical results confirm that the presented joint design reduces the training time by up to 55% over baseline approaches. They also show that CFmMIMO here requires the lowest training time for FL processes compared with cell-free time-division multiple access massive MIMO and collocated massive MIMO. [ABSTRACT FROM AUTHOR]

This paper focuses on constructing genotypic predictors for antiretroviral drug susceptibility of HIV. To this end, a method to recover the largest elements of an unknown vector in a least squares problem is developed. The proposed method introduces two novel ideas. The first idea is a novel forward stepwise selection procedure based on the magnitude of the estimates of the candidate variables. To implement this newly introduced procedure, we revise Tikhonov regularisation from a sparse representations’ perspective. This analysis leads us to the second novel idea in the paper, which is the development of a new method to recover the largest elements of the unknown vector in the least squares problem. The method implements a sequence of Tikhonov regularisation problems which aim to recover the largest of the remaining elements of the unknown vector. Additionally, we derive sufficient conditions that ensure the recovery of the largest elements of the unknown vector. We perform numerical studies using simulated data and data from the Stanford HIV resistance database. The performance of the proposed method is compared against a state-of-the-art method. [ABSTRACT FROM AUTHOR]

Automatic control using ultrareliable and low latency communication is one of the potential applications of the new fifth-generation wireless systems. A remaining challenge is then to guarantee a low end-to-end delay with low jitter over combined internet and wireless interfaces that are packet switched and capacity optimized. The main novelty of this paper is to introduce stringent delay control to meet this challenge, over simultaneous multiple data paths. The proposed multiple-input-multiple-output (MIMO) cascade control system is nonlinear since the dwell times of the transmission node queues used as actuators cannot be negative. Stability analysis based on integral quadratic constraint theory is, therefore, applied to characterize the global stability of the controller. The practical performance is evaluated with experiments using product like test bed C++ code. It is stressed that the proposed controller does not require internode time synchronization. [ABSTRACT FROM AUTHOR]

In a new radio (NR) communication system, low latency and limited jitter are critical to support new use cases exploiting haptic feedback and other closed-loop feedback control applications. Therefore, mechanisms for delay control and delay alignment are important. The simplest approaches to mitigating delay jitter, using buffering techniques, come at the price of memory and increased delay. More recently, techniques using feedback of delays experienced have shown great promise for improved performance with low overhead. The scope of this article is to discuss and motivate recognition of the need for delay alignment in general. [ABSTRACT FROM AUTHOR]

This letter describes a novel algorithm for sparse reconstruction. The method uses offline computations to reduce the computational burden of online execution. The approach relies on the recently proposed stepwise Tikhonov regularization (STIR) method to implement forward selection procedures such as orthogonal least squares (OLS), orthogonal matching pursuit (OMP), and STIR. Numerical simulations show the efficacy of the proposed approach, which is competitive against state-of-the-art implementation of OLS and OMP. [ABSTRACT FROM AUTHOR]

Demand response technology offers the exciting potential to reduce peak energy demand, electricity infrastructure expenditure, and household electricity bills. In this paper, a pricing mechanism that relies on non-cooperative heterogeneous loads knowledgeable of future energy consumption—such as electric vehicles—transferring minimal amounts of information to achieve peak demand response in a distributed fashion, whilst maintaining the privacy of the players. The existence of a Nash equilibrium is proven, as well as convergence conditions proving uniqueness of a Nash equilibrium and the stability of an “Iterated Synchronous Best Response Algorithm.” The price of anarchy (PoA) is proven to approach 1 as the number of homogeneous players approaches infinity, indicating there is no advantage to cooperation for a large number of similar players. Finally, simulation results are presented which suggest that the PoA for a system with heterogeneous players is likely to be proportional to the spread of energy consumption constraints. [ABSTRACT FROM AUTHOR]

This paper aims to maximize the spectral and energy efficiencies of a content-centric cloud radio access network (C-RAN), where users requesting the same contents are grouped together. Data are transferred from a central baseband unit to multiple remote radio heads (RRHs) equipped with local caches. The RRHs then send the received data to each group’s user. Both multicast and unicast schemes are considered for data transmission. We formulate mixed-integer nonlinear problems in which user association, RRH activation, data rate allocation, and signal precoding are jointly designed. These challenging problems are subject to minimum data rate requirements, limited fronthaul capacity, and maximum RRH transmit power. Employing successive convex quadratic programming, we propose iterative algorithms with guaranteed convergence to Fritz John solutions. Numerical results confirm that the proposed joint designs markedly improve the spectral and energy efficiencies of the considered content-centric C-RAN compared to benchmark schemes. Importantly, they show that unicasting outperforms multicasting in terms of spectral efficiency in both cache and cache-less scenarios. In terms of energy efficiency, multicasting is the best choice for the system without cache whereas unicasting is best for the system with cache. Finally, edge caching is shown to improve both spectral and energy efficiencies. [ABSTRACT FROM AUTHOR]

This paper aims to maximize the energy efficiency of a downlink cloud radio access network (C-RAN). Here, data is transferred from a baseband unit in the core network to several remote radio heads via a set of edge routers over capacity-limited fronthaul links. The remote radio heads then send the received signals to their users via radio access links. Both data sharing and compression-based strategies are considered for fronthaul data transfer. New mixed-integer nonlinear problems are formulated, in which the ratio of network throughput and total power consumption is maximized. These challenging problem formulations include practical constraints on routing, predefined minimum data rates, fronthaul capacity, and maximum remote radio head transmit power. By employing the successive convex quadratic programming, iterative algorithms are proposed with guaranteed convergence to the Fritz John solutions of the formulated problems. Significantly, each iteration of the proposed algorithms solves only one simple convex program. Numerical examples with practical parameters confirm that the proposed joint optimization designs markedly improve the C-RAN’s energy efficiency compared to benchmark schemes. They also show that the fronthaul data-sharing strategy outperforms its compression-based counterpart in terms of energy efficiency, in both single-hop and multi-hop network scenarios. [ABSTRACT FROM AUTHOR]

The paper presents a new data flow controller for use in 4G and 5G wireless applications where the incoming internet data flow is split in support of multipoint downlink wireless transmission. The different paths of the data from the node of the split to the wireless transmission nodes and on to the mobile may then result in different travel times. The mobile can cope with small values of this delay skew by means of buffers and reordering protocols. The control objective is therefore to keep the delay skew within a prespecified interval of time, when the packets arrive in the mobile. The proposed algorithm solves the problem by cascade control. The packet dwell time of each wireless transmission node data queue is controlled by an inner loop, with the reference dwell times being determined by an outer delay skew control loop. The delay skew control loop exploits a deadzone to achieve interval control. In a further contribution the $\mathcal{L}_2$ -stability of the controller is analyzed for the dual connectivity functionality of the coming 5G cellular network deployment. A combined use of the input–output Nyquist- and Popov-criteria is required for the analysis. The performance is also evaluated by a laboratory testbed implementation. [ABSTRACT FROM AUTHOR]