This paper proposes a novel FL method called FedFNS, which integrates feature norm regularization and statistical aggregation. Specifically, a local model correction strategy is proposed to reduce client bias through incorporating a regularization term into the loss function. To mitigate the negative effects of unreliable communications on global model performance, a statistical weighted aggregation strategy is proposed, which leverages the transmission success probability of each client. The effectiveness of FedFNS is validated through extensive experiments, demonstrating its superiority over several classic FL methods in terms of accuracy.

We leverage covert communication to enhance the security of a large-scale multi-tier Low Earth Orbit (LEO) satellite network against vigilant adversarial terrestrial Base Stations (BSs) aiming at detecting satellite transmissions. This approach involves deploying massive LEO satellites at different altitudes around Earth to form a multi-tier network serving as a backhaul for near-ground Unmanned Aerial Vehicles (UAVs) that provide network services to terrestrial mobile users. Meanwhile, terrestrial BSs attempt to detect satellite transmissions based on their own received signal powers. To evade detection, the LEO satellite network performs power control to obscure the satellite transmission within the co-channel interference among the LEO satellites. We formulate a two-stage Stackelberg game to model the conflict dynamics between the terrestrial BSs and the LEO satellite network. In this game, the terrestrial BSs act as non-cooperative followers at the lower stage aiming to minimize their detection errors. On the other hand, the LEO satellite network acts as the leader at the upper stage aiming to maximize its utility while ensuring communication covertness. In contrast to existing works that focus on a small set of network nodes, our study considers a large-scale multi-tier LEO satellite network and employs stochastic geometry to model the spatial distribution of network nodes. To achieve the Stackelberg equilibrium, we develop a bi-level algorithm based on Successive Convex Approximation (SCA) and golden-section search. Our numerical results provide practical insights, revealing a trade-off in leveraging co-channel interference (i.e., while it improves the communication covertness of satellite transmission, it simultaneously degrades the link reliability).

A vital challenge in the deployment of federated learning (FL) over wireless networks is the high energy consumption incurred for the local computation and model update upload on energy-constrained devices such as IoT sensors. Equipping with energy harvesting (EH) modules is a promising solution that allows the devices to work in a self-sustainable manner. Moreover, quantizing the model updates can further improve the energy efficiency during the upload. In this paper, we propose an EH-enabled FL system with model quantization in which EH devices act as clients and client scheduling, model quantization, and transmit energy are jointly optimized to minimize the training loss while satisfying energy causality constraints and guaranteeing fairness in client selection. We formulate a non-convex mixed-integer nonlinear programming (MINLP) problem for the optimization. Then, by recasting the product of a continuous variable and a 0-1 variable in an equivalent linear form, we transform this non-convex MINLP problem into a convex problem and solve it. We present numerical evaluations on various datasets to show that our proposed system is stable and achieves high performance regardless of whether the loss function is convex or non-convex and whether the data distributions are independent and identically distributed (i.i.d.) or non-i.i.d.

Most approaches to resource allocation in energy harvesting systems for Internet-of-Things (IoT) networks do not consider real-time periodic task allocation with Quality of Service (QoS). This article studies the offline 2-D optimization problem for allocating randomly harvested energy flowing causally between slots into different real-time periodic tasks on an IoT device. We formulate an energy allocation problem, for tasks with different energy costs and requested QoS, which aims to maximize a convex reward function subject to energy causality (EC), energy saturation (ES) and task executable (TE) constraints within a certain length of time. We decouple the optimization problem into two subproblems after analysis. First, we propose a novel method to allocate energy to slots based on the Karush–Kuhn–Tucher conditions only considering the EC & ES constraints. The proposed method outperforms the state-of-the-art “Tunnel Policy,” based on geometric programming. Next, an adaption is made to satisfy the TE constraints by allocating in the original constant power slots directly without iteratively checking the wasted energy. Finally, the energy already allocated to slots is put into tasks to complete the 2-D allocation. The effectiveness of the proposed methods and task framework are validated by extensive experiments.

With the development of decentralized consensus protocols, permissionless blockchains have been envisioned as a promising enabler for the general-purpose transaction-driven, autonomous systems. However, most of the prevalent blockchain networks are built upon the consensus protocols under the crypto-puzzle framework known as proof-of-work. Such protocols face the inherent problem of transaction-processing bottleneck, as the networks achieve the decentralized consensus for transaction confirmation at the cost of very high latency. In this paper, we study the problem of consensus formation in a system of multiple throughput-scalable blockchains with sharded consensus. Specifically, the protocol design of sharded consensus not only enables parallelizing the process of transaction validation with sub-groups of processors, but also introduces the Byzantine consensus protocols for accelerating the consensus processes. By allowing different blockchains to impose different levels of processing fees and to have different transaction-generating rate, we aim to simulate the multi-service provision eco-systems based on blockchains in real world. We focus on the dynamics of blockchain-selection in the condition of a large population of consensus processors. Hence, we model the evolution of blockchain selection by the individual processors as an evolutionary game. Both the theoretical and the numerical analysis are provided regarding the evolutionary equilibria and the stability of the processors' strategies in a general case.

Motivated by the idea of “transmitting energy and information simultaneously,” we investigate, in this paper, the impact of constraints on the amount of energy that individual symbols carry, i.e., minimum amplitude constraints. We consider a Gaussian mixture noise channel with both minimum and peak amplitude constraints. First, we prove that the capacity-achieving input has a discrete distribution with a finite number of probability mass points. Then, we further investigate the number and positions of the probability mass points for the capacity-achieving input. Specifically, when the interference is constant and known at both the transmitter and the receiver, it can be totally eliminated so that the channel operates like an AWGN channel. In this case, we give a theorem to determine whether the optimal input is binary. For more general cases, such as non-binary inputs and non-constant interference, we investigate optimal inputs and capacities via numerical computations.

In the real-world, it is virtually impossible to have non-causal knowledge of future events. Research in energy harvesting (EH) systems that assumes knowledge of future energy arrivals falls short in terms of practical utility, pointing to the need for online strategies. In addition, the modeling and analysis for EH transmitter and receiver are inherently different. Compared with EH transmitter, EH receiver has received less attention. In this paper, we formulate Markov decision process problems and perform online optimization to maximize the number of bits decoded for an EH receiver with a time-switching architecture, which harvests energy from both a dedicated transmitter and other sources. We consider both infinite and finite horizon scenarios. For the infinite horizon, we provide an upper bound on the average expected reward. Then, we find an optimal policy which can achieve performance arbitrarily close to this bound. For the finite horizon, we first provide a policy obtained from standard backward induction with space quantization. Its performance can be close to optimal online performance as the number of quantization intervals increases, at the cost of relatively high computational complexity. Then, by carefully restricting the state space, we present a computationally efficient policy, which achieves comparatively good performance.

Internet of Things (IoT) systems will increasingly rely on energy harvested from their environment, and energy harvesting (EH) techniques necessitate a rethinking of how we design and optimize IoT systems. The performance of EH IoT systems is affected by the battery nonidealities and the energy consumption for transmitting and receiving. While EH considerations at the transmitter have been widely studied, the EH receiver has received relatively much less attention. Motivated by this, we investigate, in this paper, the performance of EH receivers in IoT systems with batteries having non-negligible internal resistance. We first consider a receiver with a dual-path architecture and give an optimal battery management scheme. Then, based on this management scheme, we maximize the amount of information decoded at the receiver for single and multiple block scenarios. In the multiple block scenario, we transform the nonconvex problem into a series of convex problems by exploiting certain structural properties of the nonconvex constraints. Additionally, we present extensive numerical results to validate our analysis and to study the impact of the internal resistance. This paper suggests that the internal resistance significantly impacts the design and performance of EH IoT systems. However, the dual-path architecture can be an efficient way to reduce the performance loss caused by internal resistance. Finally, we discuss implementation considerations.

The difficulty of modeling energy consumption in communication systems leads to challenges in energy harvesting (EH) systems, in which nodes scavenge energy from their environment. An EH receiver must harvest enough energy for demodulating and decoding. The energy required depends upon factors, such as code rate and signal-to-noise ratio, which can be adjusted dynamically. We consider a receiver which harvests energy from the transmitter and other ambient sources, meaning the received signal is used for both EH and information decoding. Assuming a generalized function for energy consumption, we maximize the total number of information bits decoded, under both average and peak power constraints at the transmitter, by carefully optimizing the power used for EH, power used for information transmission, fraction of time for EH, and code rate. For transmission over a single block, we find there exist problem parameters for which either maximizing power for information transmission or maximizing power for EH is optimal. In the general case, the optimal solution is a tradeoff of the two. For transmission over multiple blocks, we give an upper bound on performance and give sufficient and necessary conditions to achieve this bound. Finally, we give some numerical results to illustrate our results and analysis.

In this paper, we consider a scalar Gaussian Channel with minimum amplitude constraint, and investigate when the capacity-achieving input is binary. First, we study the case that the input satisfies both minimum and peak amplitude constraints and find that the optimal input is discrete. Then, for a given minimum amplitude, we find sufficient conditions that the peak amplitude constraint must satisfy such that the optimal input is binary and when it is not binary. Similarly, for a given peak amplitude, we find sufficient conditions that the minimum amplitude constraint must satisfy such that the optimal input is binary and when it is not binary. Finally, we find that when the input satisfies minimum amplitude and average power constraints, the optimal input is not binary, regardless of whether there is also a peak amplitude constraint.

The analysis and optimization of energy-harvesting transmitters and receivers are different. This paper considers an end-to-end communication with an energy-harvesting receiver. The receiver has a time-switching architecture and can harvest energy from both a dedicated transmitter and other ambient radio-frequency (RF) sources. We first argue that the energy consumed for decoding (per channel use) can be expressed in terms of the gap to channel capacity and utilize this model to optimize two schemes for receiver operation. The two schemes are harvest-then-receive and harvest-when-receive, and they differ primarily in how and when they use the harvested energy for decoding. For transmission over a single block, we compare their performance from various aspects. Then we consider transmission over multiple blocks. When the energy harvested is all from the transmitter, we provide the solution for choosing the optimal code rate and fraction of channels used for energy harvesting for each block. When the energy harvested can also be from other RF sources, we provide a table-search algorithm to find a solution. Finally, we present some numerical examples to validate the accuracy of our analysis.

Compared with energy-harvesting transmitters, the performance of energy-harvesting receivers has not been fully investigated. The main consumption of energy at transmitters is for transmission, while that at receivers is for information decoding. Hence, the analysis and optimization of energy-harvesting transmitters and receivers are inherently different. Motivated by the above, in this paper, we analyze the performance of a time-switching energy-harvesting receiver, which switches between harvesting and decoding. We assume that the receiver can only harvest energy from the electromagnetic signal radiated by the transmitter and the energy consumption of other processing is negligible compared with decoding. We first argue that the energy consumed for decoding (per channel use) can be expressed in terms of the gap to channel capacity and utilize this model to optimize two schemes for receiver operation. The two schemes are Harvest-then-Receive and Harvest-when-Receive, and they differ primarily in how and when they use the harvested energy for decoding. We address the problem of maximizing the amount of information decoded over both single and multiple blocks, and use the binary symmetric channel as an example to validate the accuracy of our analysis.

Compared with energy-harvesting transmitters, the performance of energy-harvesting receivers has not been fully investigated. The main consumption of energy at transmitters is for transmission, while that at receivers is for information decoding. Hence, the analysis and optimization of energy-harvesting transmitters and receivers are inherently different. This paper considers optimization of a communication system using an energy-harvesting receiver. We assume that the receiver antenna operates over a relatively wide range of frequencies; hence the receiver can harvest energy from both the in-band signal sent by the transmitter and other possibly out-of-band sources. The receiver adopts a time-switching architecture, i.e., in each block, the receiver first harvests energy then decodes information. We assume the energy consumption for decoding is a non-decreasing convex function of the normalized code rate and dominates the energy used for other processing tasks. In this context, we formulate a non-convex optimization problem to maximize the amount of information decoded over multiple blocks. We solve this non-convex problem by converting it into an equivalent convex problem. We also provide numerical examples to validate the accuracy of our analysis and compare our scheme with two suboptimal schemes requiring less overhead.

In this letter, we investigate the outage performance of a dual-hop fixed-gain amplify-and-forward relaying systems in presence of interference at the relay.We assume the desired signal has Nakagami-m fading and the interfering signals experience independent and identically distributed (i.i.d) κ - μ and η - μ fading. We present exact and closed-form expressions for the outage probability. Then we derive closed-form expression for the outage probability when the interfering signals experience i.i.d Nakagami-m fading as the special case of κ-μ and η-μ fading,respectively. Furthermore, the asymptotic outage performance is investigated in high signal-to-interference-noise ratio (SINR). Finally, Monte-Carlo simulation results are given to verify the analytical results.

Performance metrics such as outage probability, average error rate and capacity have been studied in cooperative diversity systems by a large number of papers. However, the second-order statistics properties in cooperative diversity systems have not been completely investigated. In this letter, we investigate the second-order statistics properties of an adaptive selection decode-and-forward relaying (SR) cooperative diversity system. We focus on two performance metrics: average outage rate (AOR) and average outage duration (AOD). The accurate closed-form approximations for the AOR and AOD performances are derived using Gauss-Legendre quadrature. The accuracy of our approximations is validated by the Monte-Carlo simulations. Furthermore, we compare conventional SR protocol and adaptive SR protocol in AOR and AOD. It is shown that adaptive SR protocol has a better performance in AOR but two protocols have similar performance in AOD.