Angle diversity to increase coverage and position accuracy in 3D visible light positioning

The most common approach to light-based indoor positioning relies on multilateration of received signals to the mobile device. Any deficiencies in the fidelity of these light signals can significantly distort position estimates. In this paper, we propose a method to dynamically control the light distribution from the overhead luminaires to mitigate fading effects that would otherwise occur under static lighting. By manipulating the direction of the luminaire, effectively the dispersion pattern, we introduce signal diversity in the form of multiple pointing angles and light distributions. In addition to providing angle diversity, steering and then tracking sustains the maximal line-of-sight path between a source and receiver, which reduces angle-dependent attenuation and optimizes the signal-to-noise ratio for any coordinate without needing to change the physical properties of the source or receiver. This gain in signal strength combats the limited field-of-view of luminaires and photodiodes to provide better overall coverage, which translates directly to increase positioning accuracy, particularly in a 3D space. In the results, we show field-of-view gains of 43% and improvements in MSE of 20cm.Accepted manuscrip

Robust federated learning with noisy communication

Federated learning is a communication-efficient training process that alternate between local training at the edge devices and averaging of the updated local model at the center server. Nevertheless, it is impractical to achieve perfect acquisition of the local models in wireless communication due to the noise, which also brings serious effect on federated learning. To tackle this challenge in this paper, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and worst-case model. Due to the non-convexity of the problem, regularizer approximation method is proposed to make it tractable. Regarding the worst-case model, we utilize the sampling-based successive convex approximation algorithm to develop a feasible training scheme to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function value are demonstrated via simulation for the proposed designs

Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive View

The next-generation wireless technologies, commonly referred to as the sixth generation (6G), are envisioned to support extreme communications capacity and in particular disruption in the network sensing capabilities. The terahertz (THz) band is one potential enabler for those due to the enormous unused frequency bands and the high spatial resolution enabled by both short wavelengths and bandwidths. Different from earlier surveys, this paper presents a comprehensive treatment and technology survey on THz communications and sensing in terms of the advantages, applications, propagation characterization, channel modeling, measurement campaigns, antennas, transceiver devices, beamforming, networking, the integration of communications and sensing, and experimental testbeds. Starting from the motivation and use cases, we survey the development and historical perspective of THz communications and sensing with the anticipated 6G requirements. We explore the radio propagation, channel modeling, and measurements for THz band. The transceiver requirements, architectures, technological challenges, and approaches together with means to compensate for the high propagation losses by appropriate antenna and beamforming solutions. We survey also several system technologies required by or beneficial for THz systems. The synergistic design of sensing and communications is explored with depth. Practical trials, demonstrations, and experiments are also summarized. The paper gives a holistic view of the current state of the art and highlights the issues and challenges that are open for further research towards 6G.Comment: 55 pages, 10 figures, 8 tables, submitted to IEEE Communications Surveys & Tutorial

Client Selection in Federated Learning: Principles, Challenges, and Opportunities

As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) has received tremendous attention from both industry and academia. In a typical FL scenario, clients exhibit significant heterogeneity in terms of data distribution and hardware configurations. Thus, randomly sampling clients in each training round may not fully exploit the local updates from heterogeneous clients, resulting in lower model accuracy, slower convergence rate, degraded fairness, etc. To tackle the FL client heterogeneity problem, various client selection algorithms have been developed, showing promising performance improvement. In this paper, we systematically present recent advances in the emerging field of FL client selection and its challenges and research opportunities. We hope to facilitate practitioners in choosing the most suitable client selection mechanisms for their applications, as well as inspire researchers and newcomers to better understand this exciting research topic

Online Service Provisioning in NFV-enabled Networks Using Deep Reinforcement Learning

In this paper, we study a Deep Reinforcement Learning (DRL) based framework for an online end-user service provisioning in a Network Function Virtualization (NFV)-enabled network. We formulate an optimization problem aiming to minimize the cost of network resource utilization. The main challenge is provisioning the online service requests by fulfilling their Quality of Service (QoS) under limited resource availability. Moreover, fulfilling the stochastic service requests in a large network is another challenge that is evaluated in this paper. To solve the formulated optimization problem in an efficient and intelligent manner, we propose a Deep Q-Network for Adaptive Resource allocation (DQN-AR) in NFV-enable network for function placement and dynamic routing which considers the available network resources as DQN states. Moreover, the service's characteristics, including the service life time and number of the arrival requests, are modeled by the Uniform and Exponential distribution, respectively. In addition, we evaluate the computational complexity of the proposed method. Numerical results carried out for different ranges of parameters reveal the effectiveness of our framework. In specific, the obtained results show that the average number of admitted requests of the network increases by 7 up to 14% and the network utilization cost decreases by 5 and 20 %

To Talk or to Work: Energy Efficient Federated Learning over Mobile Devices via the Weight Quantization and 5G Transmission Co-Design

Federated learning (FL) is a new paradigm for large-scale learning tasks across mobile devices. However, practical FL deployment over resource constrained mobile devices confronts multiple challenges. For example, it is not clear how to establish an effective wireless network architecture to support FL over mobile devices. Besides, as modern machine learning models are more and more complex, the local on-device training/intermediate model update in FL is becoming too power hungry/radio resource intensive for mobile devices to afford. To address those challenges, in this paper, we try to bridge another recent surging technology, 5G, with FL, and develop a wireless transmission and weight quantization co-design for energy efficient FL over heterogeneous 5G mobile devices. Briefly, the 5G featured high data rate helps to relieve the severe communication concern, and the multi-access edge computing (MEC) in 5G provides a perfect network architecture to support FL. Under MEC architecture, we develop flexible weight quantization schemes to facilitate the on-device local training over heterogeneous 5G mobile devices. Observed the fact that the energy consumption of local computing is comparable to that of the model updates via 5G transmissions, we formulate the energy efficient FL problem into a mixed-integer programming problem to elaborately determine the quantization strategies and allocate the wireless bandwidth for heterogeneous 5G mobile devices. The goal is to minimize the overall FL energy consumption (computing + 5G transmissions) over 5G mobile devices while guaranteeing learning performance and training latency. Generalized Benders' Decomposition is applied to develop feasible solutions and extensive simulations are conducted to verify the effectiveness of the proposed scheme.Comment: submitted to MOBIHO

Physical Layer Anonymous Precoding Design: From the Perspective of Anonymity Entropy

In the era of e-Health, privacy protection has become imperative in applications that carry personal and sensitive data. Departing from the data-perturbation based privacy-preserving techniques that reduce the fidelity of the disclosed data, in this paper we investigate anonymous communications, which mask the identity of the data sender while providing high data reliability. Focusing on the physical (PHY) layer, we first explore the break of privacy through a statistical attribute based sender detection (SD) from the receiver. Compared to the existing literature, this enables a much enhanced SD performance, especially when the users are equipped with different numbers of antennas. To counteract the advanced SD approach above, we formulate explicit anonymity constraints for the design of the anonymous precoder, which mask the sender’s PHY attributes that can be exploited by SD, while at the same time preserving the reliability of the data. Then, anonymity entropy-oriented precoders are proposed for different antenna configurations at the users, which adaptively construct a maximum number of aliases while obeying users’ signal-to-noise-ratio requirements for data accuracy. Simulation results demonstrate that the proposed anonymous precoders provide the highest level of anonymity entropy over the benchmarks, while achieving reasonable symbol error rate for the communication signal