This paper presents a method to characterize six anisotropic thermal moduli for lattice structures, enabling the estimation of full anisotropic thermal elastic moduli. The study focuses on a group of distorted Kelvin cells, generated by twisting the four-node faces, to explore the relationship between distortion, anisotropic thermal expansions, and dynamic responses. Through parametric studies, the anisotropic thermal moduli are characterized as functions of the twisting angles, revealing that thermal moduli related to compression decrease with increasing twisting angles, while those related to shearing, which do not exist in isotropic materials, are identified. Dynamic responses reveal complex modal shapes and coupling between longitudinal and transverse directions, enhancing vibration mitigation. The proposed lattices and methods offer a promising structure for assembling and designing dual-functional metamaterials, featuring customizable thermal elastic moduli, ease of space assembly, lightweight structure, and effective vibration mitigation capabilities.

Federated learning (FL) has emerged as an instance of distributed machine learning paradigm that avoids the transmission of data generated on the users' side. Although data are not transmitted, edge devices have to deal with limited communication bandwidths, data heterogeneity, and straggler effects due to the limited computational resources of users' devices. A prominent approach to overcome such difficulties is FedADMM, which is based on the classical two-operator consensus alternating direction method of multipliers (ADMM). The common assumption of FL algorithms, including FedADMM, is that they learn a global model using data only on the users' side and not on the edge server. However, in edge learning, the server is expected to be near the base station and have direct access to rich datasets. In this paper, we argue that leveraging the rich data on the edge server is much more beneficial than utilizing only user datasets. Specifically, we show that the mere application of FL with an additional virtual user node representing the data on the edge server is inefficient. We propose FedTOP-ADMM, which generalizes FedADMM and is based on a three-operator ADMM-type technique that exploits a smooth cost function on the edge server to learn a global model parallel to the edge devices. Our numerical experiments indicate that FedTOP-ADMM has substantial gain up to 33% in communication efficiency to reach a desired test accuracy with respect to FedADMM, including a virtual user on the edge server.

Recently, multiple works in the literature have presented encouraging results on the feasibility of in-band full-duplex (IBFD) communications in point-to-point and single cell arrangements, where the IBFD capability is provided by the base station. However, in multi-cell networks, in addition to high self-interference, full-duplex operations also face the severe problems of base station-to-base station cross-link interference (CLI), inter-sector CLI, user equipment-to-user equipment CLI, and inter-operator interference. Due to these difficulties, the research and engineering communities have recently proposed to adopt sub-band full-duplex (SBFD), as an intermediary step towards IBFD in the evolution of 5G New Radio systems. With SBFD, cellular base stations may operate the downlink and uplink on different non-overlapping frequency sub-bands within a time division duplexing carrier, which helps reduce self-interference and CLI due to the frequency isolation between the uplink and downlink sub-bands. In this paper, we focus on Frequency Range 1 operation, investigate the novel SBFD concept, and compare it to traditional time division duplexing, using realistic assumptions on multi-cell deployments, adjacent channel leakage, CLI and self-interference cancellation techniques. Our results indicate that SBFD operation may primarily be a candidate for low power deployments, with complexity and feasibility challenges in scenarios using high base station transmission power, sectorization, and in the presence of multiple operators.

Constitutive models for the vibroacoustics of porous materials have previously been defined for lattice cell, foam and fibrous materials in terms of dynamic viscous drag forces and oscillatory solid to fluid heat transfer effects. Where the microgeometries are cylindrical in nature, analytical expressions have been derived to efficiently represent the viscous and thermal effects. A logical extension of this work is towards porous materials consisting of spherical shapes, which may also be defined using analytical relations. In this work, the analytical dynamic viscous drag force and oscillatory thermal impedance expressions of a sphere undergoing rectilinear oscillations in a viscous fluid are derived. A transfer matrix model of acoustic wave propagation is then used to predict the sound absorption performance of an array of packed spheres, using only the porosity and mean diameters of the spheres, and the constitutive properties of the solid spheres and the surrounding viscous fluid as modelling inputs. The results compare very well with published measurements.

Although signal distortion-based peak-to-average power ratio (PAPR) reduction is a feasible candidate for orthogonal frequency division multiplexing (OFDM) to meet standard/regulatory requirements, the error vector magnitude (EVM) stemming from the PAPR reduction has a deleterious impact on the performance of high data-rate achieving multiple-input multiple-output (MIMO) systems. Moreover, these systems must constrain the adjacent channel leakage ratio (ACLR) to comply with regulatory requirements. Several recent works have investigated the mitigation of the EVM seen at the receivers by capitalizing on the excess spatial dimensions inherent in the large-scale MIMO that assume the availability of perfect channel state information (CSI) with spatially uncorrelated wireless channels. Unfortunately, practical systems operate with erroneous CSI and spatially correlated channels. Additionally, most standards support user-specific/CSI-aware beamformed and cell-specific/non-CSI-aware broadcasting channels. Hence, we formulate a robust EVM mitigation problem under channel uncertainty with nonconvex PAPR and ACLR constraints catering to beamforming/broadcasting. To solve this formidable problem, we develop an efficient scheme using our recently proposed three-operator alternating direction method of multipliers (TOP-ADMM) algorithm and benchmark it against two three-operator algorithms previously presented for machine learning purposes. Numerical results show the efficacy of the proposed algorithm under imperfect CSI and spatially correlated channels.

Although spectral precoding is a propitious technique to suppress out-of-band emissions, it has a detrimental impact on the system-wide throughput performance, notably, in high data-rate multiple-input multiple-output (MIMO) systems with orthogonal frequency division multiplexing (OFDM), because of (spatially-coloured) transmit error vector magnitude (TxEVM) emanating from spectral precoding. The first contribution of this paper is to propose two mask-compliant spectral precoding schemes, which mitigate the resulting TxEVM seen at the receiver by capitalizing on the immanent degrees-of-freedom in (massive) MIMO systems and consequently improve the system-wide throughput. Our second contribution is an introduction to a new and simple three-operator consensus alternating direction method of multipliers (ADMM) algorithm, referred to as TOP-ADMM, which decomposes a large-scale problem into easy-to-solve subproblems. We employ the proposed TOP-ADMM-based algorithm to solve the spectral precoding problems, which offer computational efficiency. Our third contribution presents substantial numerical results by using an NR release 15 compliant simulator. In case of perfect channel knowledge at the transmitter, the proposed methods render similar block error rate and throughput performance as without spectral precoding yet meeting out-of-band emission (OOBE) requirements at the transmitter. Further, no loss on the OOBE performance with a graceful degradation on the throughput is observed under channel uncertainty.

Spectral precoding is a promising technique to suppress out-of-band emissions and comply with leakage constraints over adjacent frequency channels and with mask requirements on the unwanted emissions. However, spectral precoding may distort the original data vector, which is formally expressed as the error vector magnitude (EVM) between the precoded and original data vectors. Notably, EVM has a deleterious impact on the performance of multiple-input multiple-output orthogonal frequency division multiplexing-based systems. In this paper we propose a novel spectral precoding approach which constrains the EVM while complying with the mask requirements. We first formulate and solve the EVM-unconstrained mask-compliant spectral precoding problem, which serves as a springboard to the design of two EVM-constrained spectral precoding schemes. The first scheme takes into account a wideband EVM-constraint which limits the average in-band distortion. The second scheme takes into account frequency-selective EVM-constraints, and consequently, limits the signal distortion at the subcarrier level. Numerical examples illustrate that both proposed schemes outperform previously developed schemes in terms of important performance indicators such as block error rate and system-wide throughput while complying with spectral mask and EVM constraints.

This paper deals with a distortion-based non-convex peak-to-average power ratio (PAPR) problem for large-scale multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Our work is motivated by the observation that the distortion stemming from the PAPR reduction schemes has a deleterious impact on the data rates of MIMO-OFDM systems. Recently, some approaches have been proposed to either null or mitigate such distortion seen at the receiver(s) side by exploiting the extra degrees of freedom when the downlink channel is perfectly known at the transmitter. Unfortunately, most of these proposed methods are not robust against channel uncertainty, since perfect channel knowledge is practically infeasible at the transmitter. Although some recent works utilize semidefinite programming to cope with channel uncertainty and non-convex PAPR problem, they have formidable computational complexity. Additionally, some prior-art techniques tackle the non-convex PAPR problem by minimizing the peak power, which renders a suboptimal solution. In this work, we showcase the application of powerful first-order optimization schemes, namely the three-operator alternating direction method of multipliers (ADMM)-type techniques, notably 1) three-operator ADMM, 2) Bregman ADMM, and 3) Davis-Yin splitting, to solve the non-convex and robust PAPR problem, yielding a near-optimal solution in a computationally efficient manner.