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Over-the-air computation (AirComp) is a known technique in which wireless devices transmit values by analog amplitude modulation so that a function of these values is computed over the communication channel at a common receiver. The physical reason is the superposition properties of the electromagnetic waves, which naturally return sums of analog values. Consequently, the applications of AirComp are almost entirely restricted to analog communication systems. However, the use of digital communications for over-the-air computations would have several benefits, such as error correction, synchronization, acquisition of channel state information, and easier adoption by current digital communication systems. Nevertheless, a common belief is that digital modulations are generally unfeasible for computation tasks because the overlapping of digitally modulated signals returns signals that seem to be meaningless for these tasks. This paper breaks through such a belief and proposes a fundamentally new computing method, named ChannelComp, for performing over-the-air computations by any digital modulation. In particular, we propose digital modulation formats that allow us to compute a wider class of functions than AirComp can compute, and we propose a feasibility optimization problem that ascertains the optimal digital modulation for computing functions over-the-air. The simulation results verify the superior performance of ChannelComp in comparison to AirComp, particularly for the product functions, with around 10 dB improvement of the computation error., Part of ISBN 9781538674628QC 20231214

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., Part of ISBN 9798350325744QC 20240711

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.

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., QC 20230426

This work proposes a reliable leakage detection methodology for water distribution networks (WDNs) using machine-learning strategies. Our solution aims at detecting leakage in WDNs using efficient machine-learning strategies. We analyze pressure measurements from pumps in district metered areas (DMAs) in Stockholm, Sweden, where we consider a residential DMA of the water distribution network. Our proposed methodology uses learning strategies from unsupervised learning (K-means and cluster validation techniques), and supervised learning (learning vector quantization algorithms). The learning strategies we propose have low complexity, and the numerical experiments show the potential of using machine-learning strategies in leakage detection for monitored WDNs. Specifically, our experiments show that the proposed learning strategies are able to obtain correct classification rates up to 93.98%., QC 20230705

Optical control of functional properties in ferroic materials is now a highly appealing topic because it may entail different paradigms for future technologies. In ferroelectrics, in particular, controlling the properties with light implies noncontact external control of the material’s functionality, thereby opening a pathway for developing the next generation of photocontrolled devices. Recently, experimental observations have demonstrated that the dielectric permittivity of charged-domain-wall ferroelectrics can be easily modulated by visible light. However, because of the wide band gap of ferroelectric materials, the physical origin of this phenomenon is still controversial. Here, the photoinduced electronic reconstruction mechanism is proposed as the primary light-absorption mechanism in charged domain walls, allowing an understanding of the origin of the visible-light control of dielectric permittivity in ferroelectric materials., Postprint (author's final draft)