User Fairness Constraint Research Articles

Achieving Multi-Beam Gain in Intelligent Reflecting Surface Assisted Wireless Energy Transfer

Intelligent reflecting surface (IRS) is a promising technology to boost the efficiency of wireless energy transfer (WET) systems. However, for a multiuser WET system, simultaneous multi-beam energy transmission is generally required to achieve the maximum performance, which may not be implemented by using the IRS having only a single set of coefficients. As a result, it remains unknown how to exploit the IRS to approach such a performance upper bound. To answer this question, we aim to maximize the total harvested energy of a multiuser WET system subject to the user fairness constraints and the non-linear energy harvesting model. We first consider the static IRS beamforming scheme, which shows that the optimal IRS reflection matrix obtained by applying semidefinite relaxation is indeed of high rank in general as the number of energy receivers (ERs) increases, due to which the resulting rank-one solution by applying Gaussian Randomization may lead to significant loss. To achieve the multi-beam gain, we then propose a general time-division based novel framework by exploiting the IRS's dynamic passive beamforming. Moreover, it is able to achieve a good balance between the system performance and complexity by controlling the number of IRS shift patterns. Finally, we also propose a time-division multiple access (TDMA) based passive beamforming design for performance comparison. Simulation results demonstrate the necessity of multi-beam transmission and the superiority of the proposed dynamic IRS beamforming scheme over existing schemes.

Path Planning for the Dynamic UAV-Aided Wireless Systems Using Monte Carlo Tree Search

For UAV-aided wireless systems, online path planning attracts much attention recently. To better adapt to the real-time dynamic environment, for the first time, we propose a Monte Carlo Tree Search (MCTS)-based path planning scheme. In details, we consider a single UAV acts as a mobile server to provide computation tasks offloading services for a set of mobile users on the ground, where the movement of ground users follows a Random Way Point model. Our model aims at maximizing the average throughput under energy consumption and user fairness constraints, and the proposed time-saving MCTS algorithm can further improve the performance. Simulation results show that the proposed algorithm achieves a larger average throughput and a faster convergence performance compared with the baseline algorithms of Q-learning and Deep Q-Network.

QoS-Driven Load Balancing in Hybrid LiFi and WiFi Networks

This work studies the quality of service (QoS) performance of wireless networks that integrate light fidelity (LiFi) and wireless fidelity (WiFi). While the hybrid network is potential for improving network capacity, load balancing becomes essential and challenging due to the nature of heterogeneous access points (APs). A number of studies have been conducted to address this issue, focusing on maximising the network capacity with user fairness constraints. However, in practice, QoS metrics including packet loss ratio and latency are important to network services. In this paper, QoS-driven load balancing is studied for hybrid LiFi and WiFi networks (HLWNets) in two scenarios: single-AP association (SA) and multi-AP association (MA). In each case, an optimisation problem is formulated to minimise the packet loss ratio and latency, and a low-complexity iterative algorithm is proposed to solve the problem. Results show that the novel methods, especially MA, can effectively balance the traffic loads among the APs and improve the QoS performance. In addition, the more subflows the better performance MA provides. Targeting the same level of QoS, MA can achieve a system throughput up to 160% higher than the signal strength strategy and 130% higher than the proportional fairness load balancing.

Reputation-Based Federated Learning for Secure Wireless Networks

The dilemma between the ever-increasing demands for data processing, and the limited capabilities of mobile devices in a wireless communication system calls for the appearance of federated learning (FL). As a distributed machine learning (ML) method, FL executes in an iterative manner by distributing the global model parameters and aggregating the local model parameters, which avoids the transmission of huge raw data and preserves data privacy during the training process. However, since FL cannot control the local training and transmission process, this gives malicious users the opportunity to deteriorate the global aggregation. We adopt a reputation model based on beta distribution function to measure the credibility of local users, and propose a reputation-based scheduling policy with user fairness constraint. By taking into account the impact of wireless channel conditions and malicious attack features, we derive tractable expressions for the convergence rate of FL in a wireless setting. Moreover, we validate the superiority of the proposed reputation-based scheduling policy via numerical analysis and empirical simulations. The results show that the proposed secure wireless FL framework can not only distinguish malicious users from normal users but also effectively defend against several typical attack types featured in attack intensity and attack frequency. The analysis also reveals that the effect of average attack intensity on the convergence performance of FL is dominated by the percentage of malicious user equipments (UEs), and imposes even greater negative effect on the convergence performance of FL as the percentage of malicious UEs increases.

Energy beamforming for full-duplex wireless-powered communication networks

In this paper, we consider a full-duplex (FD) wireless-powered communication network (WPCN), where one FD hybrid access point (HAP) equipped with multiple antennas simultaneously transmits energy to and receives information from multiple users. Firstly, we propose a space division wireless energy allocation scheme and calculate the harvested energy in downlink wireless energy transfer (WET) for each user. Secondly, we derive an approximate closed-form expression of user’s achievable ergodic rate in uplink wireless information transfer (WIT). Thirdly, the energy allocation for different users is optimized under the max–min user fairness constraint, and a closed-form solution is obtained. Numerical results show that the simulation and the approximation of achievable rates are well matched, and energy beamforming can effectively suppress self-interference (SI), and improve rates as well as fairness among users. Moreover, FD-WPCNs are shown to outperform half-duplex (HD) WPCNs in rates with same number of antennas.

Collaboration- and Fairness-Aware Big Data Management in Distributed Clouds

With the advancement of information and communication technology, data are being generated at an exponential rate via various instruments and collected at an unprecedented scale. Such large volume of data generated is referred to as big data, which now are revolutionizing all aspects of our life ranging from enterprises to individuals, from science communities to governments, as they exhibit great potentials to improve efficiency of enterprises and the quality of life. To obtain nontrivial patterns and derive valuable information from big data, a fundamental problem is how to properly place the collected data by different users to distributed clouds and to efficiently analyze the collected data to save user costs in data storage and processing, particularly the cost savings of users who share data. By doing so, it needs the close collaborations among the users, by sharing and utilizing the big data in distributed clouds due to the complexity and volume of big data. Since computing, storage and bandwidth resources in a distributed cloud usually are limited, and such resource provisioning typically is expensive, the collaborative users require to make use of the resources fairly. In this paper, we study a novel collaboration- and fairness-aware big data management problem in distributed cloud environments that aims to maximize the system throughout, while minimizing the operational cost of service providers to achieve the system throughput, subject to resource capacity and user fairness constraints. We first propose a novel optimization framework for the problem. We then devise a fast yet scalable approximation algorithm based on the built optimization framework. We also analyze the time complexity and approximation ratio of the proposed algorithm. We finally conduct experiments by simulations to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm is promising, and outperforms other heuristics.

Full Duplex-Assisted Intercell Interference Cancellation in Heterogeneous Networks

This paper studies the suppression of cross-tier intercell interference (ICI) generated by a macro base station (MBS) to pico user equipments (PUEs) in heterogeneous networks (HetNets). Different from existing ICI avoidance schemes such as enhanced ICI cancellation (eICIC) and coordinated beamforming, which generally operate at the MBS, we propose a full duplex (FD)-assisted ICI cancellation (fICIC) scheme, which can operate at each pico BS (PBS) individually and is transparent to the MBS. The basic idea of the fICIC is to apply FD technique at the PBS such that the PBS can send the desired signals and forward the listened cross-tier ICI simultaneously to PUEs. We first consider the narrowband single-user case, where the MBS serves a single macro UE and each PBS serves a single PUE. We obtain the closed-form solution of the optimal fICIC scheme, and analyze its asymptotical performance in ICI-dominated scenario. We then investigate the general narrowband multiuser case, where both MBS and PBSs serve multiple UEs. We devise a low-complexity algorithm to optimize the fICIC aimed at maximizing the downlink sum rate of the PUEs subject to user fairness constraint. Finally, the generalization of the fICIC to wideband systems is investigated. Simulations validate the analytical results and demonstrate the advantages of the fICIC on mitigating cross-tier ICI.