Energy harvesting based two-way full-duplex relaying network over a Rician fading environment: performance analysis

Full-duplex transmission is a promising technique to enhance the capacity of communication systems. In this paper, we propose and investigate the system performance of an energy harvesting based two-way full-duplex relaying network over a Rician fading environment. Firstly, we analyse and demonstrate the analytical expressions of the achievable throughput, outage probability, optimal time switching factor, and symbol error ratio of the proposed system. In the second step, the effect of various parameters of the system on its performance is presented and investigated. In the final step, the analytical results are also demonstrated by Monte Carlo simulation. The numerical results proved that the analytical results and the simulation results agreed with each other.Web of Science68112311

Multisource power splitting energy harvesting relaying network in half-duplex system over block Rayleigh fading channel: System performance analysis

Energy harvesting and information transferring simultaneously by radio frequency (RF) is considered as the novel solution for green-energy wireless communications. From that point of view, the system performance (SP) analysis of multisource power splitting (PS) energy harvesting (EH) relaying network (RN) over block Rayleigh-fading channels is presented and investigated. We investigate the system in both delay-tolerant transmission (DTT), and delay-limited transmission (DLT) modes and devices work in the half-duplex (HD) system. In this model system, the closed-form (CF) expressions for the outage probability (OP), system throughput (ST) in DLT mode and for ergodic capacity (EC) for DTT mode are analyzed and derived, respectively. Furthermore, CF expression for the symbol errors ratio (SER) is demonstrated. Then, the optimal PS factor is investigated. Finally, a Monte Carlo simulation is used for validating the analytical expressions concerning with all system parameters (SP).Web of Science81art. no. 6

Automatic limit and shakedown analysis of 3-D steel frames

This paper presents an efficient algorithm for both limit and shakedown analysis of 3-D steel frames by kinematical method using linear programming technique. Several features in the application of linear programming for rigid-plastic analysis of three-dimensional steel frames are discussed, as: change of the variables, automatic choice of the initial basic matrix for the simplex algorithm, direct calculation of the dual variables by primal-dual technique. Some numerical examples are presented to demonstrate the robustness, efficiency of the proposed technique and computer program

Power beacon-assisted energy harvesting in a half-duplex communication network under co-channel interference over a Rayleigh fading environment: Energy efficiency and outage probability analysis

In this time, energy efficiency (EE), measured in bits per Watt, has been considered as an important emerging metric in energy-constrained wireless communication networks because of their energy shortage. In this paper, we investigate power beacon assisted (PB) energy harvesting (EH) in half-duplex (HD) communication network under co-channel Interferer over Rayleigh fading environment. In this work, we investigate the model system with the time switching (TS) protocol. Firstly, the exact and asymptotic form expressions of the outage probability (OP) are analyzed and derived. Then the system EE is investigated and the influence of the primary system parameters on the system performance. Finally, we verify the correctness of the analytical expressions using Monte Carlo simulation. Finally, we can state that the simulation and analytical results are the same.Web of Science1213art. no. 257

Decentralized Collaborative Learning Framework for Next POI Recommendation

Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) due to its effectiveness in helping people decide the next POI to visit. However, accurate recommendation requires a vast amount of historical check-in data, thus threatening user privacy as the location-sensitive data needs to be handled by cloud servers. Although there have been several on-device frameworks for privacy-preserving POI recommendations, they are still resource-intensive when it comes to storage and computation, and show limited robustness to the high sparsity of user-POI interactions. On this basis, we propose a novel decentralized collaborative learning framework for POI recommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner. DCLR significantly reduces the local models' dependence on the cloud for training, and can be used to expand arbitrary centralized recommendation models. To counteract the sparsity of on-device user data when learning each local model, we design two self-supervision signals to pretrain the POI representations on the server with geographical and categorical correlations of POIs. To facilitate collaborative learning, we innovatively propose to incorporate knowledge from either geographically or semantically similar users into each local model with attentive aggregation and mutual information maximization. The collaborative learning process makes use of communications between devices while requiring only minor engagement from the central server for identifying user groups, and is compatible with common privacy preservation mechanisms like differential privacy. We evaluate DCLR with two real-world datasets, where the results show that DCLR outperforms state-of-the-art on-device frameworks and yields competitive results compared with centralized counterparts.Comment: 21 Pages, 3 figures, 4 table

FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning

The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions attempted to achieve more fairness by weighted aggregating deep learning models across clients. This work introduces a novel non-IID type encountered in real-world datasets, namely cluster-skew, in which groups of clients have local data with similar distributions, causing the global model to converge to an over-fitted solution. To deal with non-IID data, particularly the cluster-skewed data, we propose FedDRL, a novel FL model that employs deep reinforcement learning to adaptively determine each client's impact factor (which will be used as the weights in the aggregation process). Extensive experiments on a suite of federated datasets confirm that the proposed FedDRL improves favorably against FedAvg and FedProx methods, e.g., up to 4.05% and 2.17% on average for the CIFAR-100 dataset, respectively.Comment: Accepted for presentation at the 51st International Conference on Parallel Processin

Contraction and Robustness of Continuous Time Primal-Dual Dynamics

The Primal-Dual (PD) algorithm is widely used in convex optimization to determine saddle points. While the stability of the PD algorithm can be easily guaranteed, strict contraction is nontrivial to establish in most cases. This work focuses on continuous, possibly non-autonomous PD dynamics arising in a network context, in distributed optimization, or in systems with multiple time-scales. We show that the PD algorithm is indeed strictly contracting in specific metrics and analyze its robustness establishing stability and performance guarantees for different approximate PD systems. We derive estimates for the performance of multiple time-scale multi-layer optimization systems, and illustrate our results on a primal-dual representation of the Automatic Generation Control of power systems.Comment: 6 pages, 1 figures, published on LCSS and CDC 201