References of "Vu, Thang Xuan 50022494"
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See detailMachine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance
Vu, Thang Xuan UL; Chatzinotas, Symeon UL; Nguyen, van Dinh UL et al

in IEEE Transactions on Wireless Communications (in press)

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial ... [more ▼]

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with M RF chains and N antennas, where M < N. Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of M antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tries all valid antenna subsets. Although approaching the (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between the key system parameters and the selected antennas. The proposed L-ASPD is robust against the number of users and their locations, BS's transmit power, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves higher effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance. [less ▲]

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See detailActor‑critic learning‑based energy optimization for UAV access and backhaul networks
Yuan, Yaxiong UL; Lei, Lei UL; Vu, Thang Xuan UL et al

in EURASIP Journal on Wireless Communications and Networking (2021)

In unmanned aerial vehicle (UAV)-assisted networks, UAV acts as an aerial base station which acquires the requested data via backhaul link and then serves ground users (GUs) through an access network. In ... [more ▼]

In unmanned aerial vehicle (UAV)-assisted networks, UAV acts as an aerial base station which acquires the requested data via backhaul link and then serves ground users (GUs) through an access network. In this paper, we investigate an energy minimization problem with a limited power supply for both backhaul and access links. The difficul- ties for solving such a non-convex and combinatorial problem lie at the high compu- tational complexity/time. In solution development, we consider the approaches from both actor-critic deep reinforcement learning (AC-DRL) and optimization perspectives. First, two offline non-learning algorithms, i.e., an optimal and a heuristic algorithms, based on piecewise linear approximation and relaxation are developed as benchmarks. Second, toward real-time decision-making, we improve the conventional AC-DRL and propose two learning schemes: AC-based user group scheduling and backhaul power allocation (ACGP), and joint AC-based user group scheduling and optimization-based backhaul power allocation (ACGOP). Numerical results show that the computation time of both ACGP and ACGOP is reduced tenfold to hundredfold compared to the offline approaches, and ACGOP is better than ACGP in energy savings. The results also verify the superiority of proposed learning solutions in terms of guaranteeing the feasibility and minimizing the system energy compared to the conventional AC-DRL. [less ▲]

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See detailEfficient Federated Learning Algorithm for Resource Allocation in Wireless IoT Networks
Nguyen, van Dinh UL; Sharma, Shree Krishna UL; Vu, Thang Xuan UL et al

in IEEE Internet of Things Journal (2021), 8(5), 3394-3409

Federated learning (FL) allows multiple edge computing nodes to jointly build a shared learning model without having to transfer their raw data to a centralized server, thus reducing communication ... [more ▼]

Federated learning (FL) allows multiple edge computing nodes to jointly build a shared learning model without having to transfer their raw data to a centralized server, thus reducing communication overhead. However, FL still faces a number of challenges such as non-iid distributed data and heterogeneity of user equipments (UEs). Enabling a large number of UEs to join the training process in every round raises a potential issue of the heavy global communication burden. To address these issues, we generalize the current state-of-the-art Federated Averaging (FedAvg) by adding a weight-based proximal term to the local loss function. The proposed FL algorithm runs stochastic gradient descent in parallel on a sampled subset of the total UEs with replacement during each global round. We provide a convergence upper bound characterizing the trade-off between convergence rate and global rounds, showing that a small number of active UEs per round still guarantees convergence. Next, we employ the proposed FL algorithm in wireless Internet-of-Things (IoT) networks to minimize either total energy consumption or completion time of FL, where a simple yet efficient path-following algorithm is developed for its solutions. Finally, numerical results on unbalanced datasets are provided to demonstrate the performance improvement and robustness on the convergence rate of the proposed FL algorithm over FedAvg. They also reveal that the proposed algorithm requires much less training time and energy consumption than the FL algorithm with full user participation. These observations advocate the proposed FL algorithm for a paradigm shift in bandwidth- constrained learning wireless IoT networks. [less ▲]

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See detailFederated Learning Meets Contract Theory: Economic-Efficiency Framework for Electric Vehicle Networks
Saputra, Yuris M.; Nguyen, Diep N.; Dinh, Thai Hoang et al

in IEEE Transactions on Mobile Computing (2021)

In this paper, we propose a novel energy-efficient framework for an electric vehicle (EV) network using a contract theoretic-based economic model to maximize the profits of charging stations (CSs) and ... [more ▼]

In this paper, we propose a novel energy-efficient framework for an electric vehicle (EV) network using a contract theoretic-based economic model to maximize the profits of charging stations (CSs) and improve the social welfare of the network. Specifically, we first introduce CS-based and CS clustering-based decentralized federated energy learning (DFEL) approaches which enable the CSs to train their own energy transactions locally to predict energy demands. In this way, each CS can exchange its learned model with other CSs to improve prediction accuracy without revealing actual datasets and reduce communication overhead among the CSs. Based on the energy demand prediction, we then design a multi-principal one-agent (MPOA) contract-based method. In particular, we formulate the CSs' utility maximization as a non-collaborative energy contract problem in which each CS maximizes its utility under common constraints from the smart grid provider (SGP) and other CSs' contracts. Then, we prove the existence of an equilibrium contract solution for all the CSs and develop an iterative algorithm at the SGP to find the equilibrium. Through simulation results using the dataset of CSs' transactions in Dundee city, the United Kingdom between 2017 and 2018, we demonstrate that our proposed method can achieve the energy demand prediction accuracy improvement up to 24.63% and lessen communication overhead by 96.3% compared with other machine learning algorithms. Furthermore, our proposed method can outperform non-contract-based economic models by 35% and 36% in terms of the CSs' utilities and social welfare of the network, respectively. [less ▲]

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See detailWireless Edge Caching: Modeling, Analysis, and Optimization
Vu, Thang Xuan UL; Bastug, Ejder; Chatzinotas, Symeon UL et al

Book published by Cambridge University Press (2020)

Understand both uncoded and coded caching techniques in future wireless network design. Expert authors present new techniques that will help you to improve backhaul, load minimization, deployment cost ... [more ▼]

Understand both uncoded and coded caching techniques in future wireless network design. Expert authors present new techniques that will help you to improve backhaul, load minimization, deployment cost reduction, security, energy efficiency and the quality of the user experience. Covering topics from high-level architectures to specific requirement-oriented caching design and analysis, including big-data enabled caching, caching in cloud-assisted 5G networks, and security, this is an essential resource for academic researchers, postgraduate students and engineers working in wireless communications. [less ▲]

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See detailState Aggregation for Multiagent Communication over Rate-Limited Channels
Mostaani, Arsham UL; Vu, Thang Xuan UL; Chatzinotas, Symeon UL et al

in State Aggregation for Multiagent Communication over Rate-Limited Channels (2020, December)

A collaborative task is assigned to a multiagent system (MAS) in which agents are allowed to communicate. The MAS runs over an underlying Markov decision process and its task is to maximize the averaged ... [more ▼]

A collaborative task is assigned to a multiagent system (MAS) in which agents are allowed to communicate. The MAS runs over an underlying Markov decision process and its task is to maximize the averaged sum of discounted one-stage rewards. Although knowing the global state of the environment is necessary for the optimal action selection of the MAS, agents are limited to individual observations. The inter-agent communication can tackle the issue of local observability, however, the limited rate of the inter-agent communication prevents the agent from acquiring the precise global state information. To overcome this challenge, agents need to communicate their observations in a compact way such that the MAS compromises the minimum possible sum of rewards. We show that this problem is equivalent to a form of rate-distortion problem which we call the task-based information compression. State Aggregation for Information Compression (SAIC) is introduced here to perform the task-based information compression. The SAIC is shown, conditionally, to be capable of achieving the optimal performance in terms of the attained sum of discounted rewards. The proposed algorithm is applied to a rendezvous problem and its performance is compared with two benchmarks; (i) conventional source coding algorithms and the (ii) centralized multiagent control using reinforcement learning. Numerical experiments confirm the superiority and fast convergence of the proposed SAIC. [less ▲]

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See detailActive Popularity Learning with Cache Hit Ratio Guarantees using a Matrix Completion Committee
Bommaraveni, Srikanth UL; Vu, Thang Xuan UL; Chatzinotas, Symeon UL et al

Scientific Conference (2020, October 08)

Edge caching is a promising technology to facethe stringent latency requirements and back-haul trafficoverloading in 5G wireless networks. However, acquiringthe contents and modeling the optimal cache ... [more ▼]

Edge caching is a promising technology to facethe stringent latency requirements and back-haul trafficoverloading in 5G wireless networks. However, acquiringthe contents and modeling the optimal cache strategy is achallenging task. In this work, we use an active learningapproach to learn the content popularities since it allowsthe system to leverage the trade-off between explorationand exploitation. Exploration refers to caching new fileswhereas exploitation use known files to cache, to achievea good cache hit ratio. In this paper, we mainly focus tolearn popularities as fast as possible while guaranteeing anoperational cache hit ratio constraint. The effectiveness ofproposed learning and caching policies are demonstratedvia simulation results as a function of variance, cache hitratio and used storage. [less ▲]

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See detailResource Allocation for UAV Relay-Assisted IoT Communication Networks
Tran Dinh, Hieu UL; Nguyen, van Dinh UL; Gautam, Sumit UL et al

Scientific Conference (2020, October 06)

This work studies unmanned aerial vehicle (UAV) relay-assisted Internet of Things (IoT) communication networks in which a UAV is deployed as an aerial base station (BS) to collect time-constrained data ... [more ▼]

This work studies unmanned aerial vehicle (UAV) relay-assisted Internet of Things (IoT) communication networks in which a UAV is deployed as an aerial base station (BS) to collect time-constrained data from IoT devices and transfer information to a ground gateway (GW). In this context, we jointly optimize the allocated bandwidth, transmission power, as well as the UAV trajectory to maximize the total system throughput while satisfying the user’s latency requirement and the UAV’s limited storage capacity. The formulated problem is strongly nonconvex which is very challenging to solve optimally. Towards an appealing solution, we first introduce new variables to convert the original problem into a computationally tractable form, and then develop an iterative algorithm for its solution by leveraging the inner approximation method. Numerical results are given to show [less ▲]

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See detailActor-Critic Deep Reinforcement Learning for Energy Minimization in UAV-Aided Networks
Yuan, Yaxiong UL; Lei, Lei UL; Vu, Thang Xuan UL et al

in 2020 European Conference on Networks and Communications (EuCNC) (2020, September 21)

In this paper, we investigate a user-timeslot scheduling problem for downlink unmanned aerial vehicle (UAV)-aided networks, where the UAV serves as an aerial base station. We formulate an optimization ... [more ▼]

In this paper, we investigate a user-timeslot scheduling problem for downlink unmanned aerial vehicle (UAV)-aided networks, where the UAV serves as an aerial base station. We formulate an optimization problem by jointly determining user scheduling and hovering time to minimize UAV’s transmission and hovering energy. An offline algorithm is proposed to solve the problem based on the branch and bound method and the golden section search. However, executing the offline algorithm suffers from the exponential growth of computational time. Therefore, we apply a deep reinforcement learning (DRL) method to design an online algorithm with less computational time. To this end, we first reformulate the original user scheduling problem to a Markov decision process (MDP). Then, an actor-critic-based RL algorithm is developed to determine the scheduling policy under the guidance of two deep neural networks. Numerical results show the proposed online algorithm obtains a good tradeoff between performance gain and computational time. [less ▲]

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See detailActive Content Popularity Learning and Caching Optimization with Hit Ratio Guarantees
Bommaraveni, Srikanth UL; Vu, Thang Xuan UL; Chatzinotas, Symeon UL et al

in Active Content Popularity Learning and Caching Optimization with Hit Ratio Guarantees (2020), 8

Edge caching is an effective solution to reduce delivery latency and network congestion by bringing contents close to end-users. A deep understanding of content popularity and the principles underlying ... [more ▼]

Edge caching is an effective solution to reduce delivery latency and network congestion by bringing contents close to end-users. A deep understanding of content popularity and the principles underlying the content request sequence are required to effectively utilize the cache. Most existing works design caching policies based on global content requests with very limited consideration of individual content requests which reflect personal preferences. To enable the optimal caching strategy, in this paper, we propose an Active learning (AL) approach to learn the content popularities and design an accurate content request prediction model. We model the content requests from user terminals as a demand matrix and then employ AL-based query-by-committee (QBC) matrix completion to predict future missing requests. The main principle of QBC is to query the most informative missing entries of the demand matrix. Based on the prediction provided by the QBC, we propose an adaptive optimization caching framework to learn popularities as fast as possible while guaranteeing an operational cache hit ratio requirement. The proposed framework is model-free, thus does not require any statistical knowledge about the underlying traffic demands. We consider both the fixed and time-varying nature of content popularities. The effectiveness of the proposed learning caching policies over the existing methods is demonstrated in terms of root mean square error, cache hit ratio, and cache size on a simulated dataset. [less ▲]

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See detailUAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization
Tran Dinh, Hieu UL; Nguyen, van Dinh UL; Gautam, Sumit UL et al

E-print/Working paper (2020)

Unmanned aerial vehicle (UAV) communication has emerged as a prominent technology for emergency communications (e.g., natural disaster) in Internet of Things (IoT) networks to enhance the ability of ... [more ▼]

Unmanned aerial vehicle (UAV) communication has emerged as a prominent technology for emergency communications (e.g., natural disaster) in Internet of Things (IoT) networks to enhance the ability of disaster prediction, damage assessment, and rescue operations promptly. In this paper, a UAV is deployed as a flying base station (BS) to collect data from time-constrained IoT devices and then transfer the data to a ground gateway (GW). In general, the latency constraint at IoT users and the limited storage capacity of UAV highly hinder practical applications of UAV-assisted IoT networks. In this paper, full-duplex (FD) technique is adopted at the UAV to overcome these challenges. In addition, half-duplex (HD) scheme for UAV-based relaying is also considered to provide a comparative study between two modes (viz., FD and HD). Herein, a device is successfully served iff its data is collected by UAV and conveyed to GW within the flight time. In this context, we aim at maximizing the number of served IoT devices by jointly optimizing bandwidth and power allocation, as well as the UAV trajectory, while satisfying the requested timeout (RT) requirement of each device and the UAV’s limited storage capacity. The formulated optimization problem is troublesome to solve due to its non-convexity and combinatorial nature. Toward appealing applications, we first relax binary variables into continuous values and transform the original problem into a more computationally tractable form. By leveraging inner approximation framework, we derive newly approximated functions for non-convex parts and then develop a simple yet efficient iterative algorithm for its solutions. Next, we attempt to maximize the total throughput subject to the number of served IoT devices. Finally, numerical results show that the proposed algorithms significantly outperform benchmark approaches in terms of the number of served IoT devices and the amount of collected data. [less ▲]

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See detailA Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies
Nguyen, Cong T.; Saputra, Yuris M.; Nguyen, Huynh Van et al

in IEEE Access (2020), 8

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching ... [more ▼]

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice [less ▲]

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See detailEnergy-efficient deployment in wireless edge caching
Vu, Thang Xuan UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in Wireless Edge Caching: Modeling, Analysis, and Optimization (2020)

In this chapter, we investigate the performance of edge-caching wireless networks by taking into account the caching capability when designing the signal transmission. We consider hierarchical caching ... [more ▼]

In this chapter, we investigate the performance of edge-caching wireless networks by taking into account the caching capability when designing the signal transmission. We consider hierarchical caching systems in which the contents can be prefetched at both user terminals or the base station and investigate the energy performance under two notable uncoded and coded caching strategies. The backhaul and access throughputs are derived for both caching policies for arbitrary values of base station and user cache sizes from which closed-form expressions for the corresponding system energy efficiency (EE) are obtained. Furthermore, we propose two optimization problems to maximize the system EE and minimize the content delivery time subject to some given quality of service requirements. [less ▲]

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See detailA Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues
Nguyen, Cong T.; Saputra, Yuris M.; Nguyen, Huynh Van et al

in IEEE Access (2020)

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social ... [more ▼]

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs. [less ▲]

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See detailCoarse Trajectory Design for Energy Minimization in UAV-enabled Wireless Communications with Latency Constraints
Tran Dinh, Hieu UL; Vu, Thang Xuan UL; Chatzinotas, Symeon UL et al

in IEEE Transactions on Vehicular Technology (2020)

In this paper, we design the UAV trajectory to minimize the total energy consumption while satisfying the requested timeout (RT) requirement and energy budget, which is accomplished via jointly optimizing ... [more ▼]

In this paper, we design the UAV trajectory to minimize the total energy consumption while satisfying the requested timeout (RT) requirement and energy budget, which is accomplished via jointly optimizing the path and UAV’s velocities along subsequent hops. The corresponding optimization problem is difficult to solve due to its non-convexity and combinatorial nature. To overcome this difficulty, we solve the original problem via two consecutive steps. Firstly, we propose two algorithms, namely heuristic search, and dynamic programming (DP) to obtain a feasible set of paths without violating the GU’s RT requirements based on the traveling salesman problem with time window (TSPTW). Then, they are compared with exhaustive search and traveling salesman problem (TSP) used as reference methods. While the exhaustive algorithm achieves the best performance at a high computation cost, the heuristic algorithm exhibits poorer performance with low complexity. As a result, the DP is proposed as a practical trade-off between the exhaustive and heuristic algorithms. Specifically, the DP algorithm results in near-optimal performance at a much lower complexity. Secondly, for given feasible paths, we propose an energy minimization problem via a joint optimization of the UAV’s velocities along subsequent hops. Finally, numerical results are presented to demonstrate the effectiveness of our proposed algorithms. The results show that the DP-based algorithm approaches the exhaustive search’s performance with a significantly reduced complexity. It is also shown that the proposed solutions outperform the state-of-theart benchmarks in terms of both energy consumption and outage performance. [less ▲]

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See detailCache-aided full-duplex: Delivery time analysis and optimization
Vu, Thang Xuan UL; Trinh, Anh Vu; Chatzinotas, Symeon UL et al

in Wireless Networks (2020)

Edge caching has received much attention as a promising technique to overcome the stringent latency and data-hungry challenges in the future generation wireless networks. Meanwhile, full-duplex (FD ... [more ▼]

Edge caching has received much attention as a promising technique to overcome the stringent latency and data-hungry challenges in the future generation wireless networks. Meanwhile, full-duplex (FD) transmission can improve the spectral efficiency by allowing a node to receive and transmit on the same frequency band simultaneously. In this paper, we investigate the delivery time performance of a cache-aided FD system, in which an edge node, operates in FD mode, serves users via wireless channels and is equipped with a cache memory. Firstly, we derive a closed-form expression for the average delivery time by taking into account the uncertainties of both backhaul and access wireless channels. The derived analysis allows the examination of the impact of the key parameters, e.g., cache size and transmit power. Secondly, a power optimization problem is formulated to minimize the average delivery time. To deal with the non-convexity of the formulated problem, we propose an iterative optimization algorithm based on the bisection method. Finally, numerical results are presented to demonstrate the effectiveness of the proposed algorithm, which can significantly reduce the delivery time compared to the FD reference and the half-duplex counterpart. [less ▲]

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See detailJoint Optimization for PS-based SWIPT Multiuser Systems with Non-linear Energy Harvesting
Vu, Thang Xuan UL; Chatzinotas, Symeon UL; Gautam, Sumit UL et al

in IEEE Wireless Communications and Networking Conference (WCNC), Seoul, 25-38 May 2020 (2020, May)

In this paper, we investigate the performance of simultaneous wireless information and power transfer (SWIPT) multiuser systems, in which a base station serves a set of users with both information and ... [more ▼]

In this paper, we investigate the performance of simultaneous wireless information and power transfer (SWIPT) multiuser systems, in which a base station serves a set of users with both information and energy simultaneously via a power splitting (PS) mechanism. To capture realistic scenarios, a nonlinear energy harvesting (EH) model is considered. In particular, we jointly design the PS factors and the beamforming vectors in order to maximize the total harvested energy, subjected to rate requirements and a total transmit power budget. To deal with the inherent non-convexity of the formulated problem, an iterative optimization algorithm is proposed based on the inner approximation method and semidefinite relaxation (SDR), whose convergence is theoretically guaranteed. Numerical results show that the proposed scheme significantly outperforms the baseline max-min based SWIPT multicast and fixed-power PS designs. [less ▲]

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See detailJoint Power Allocation and Access Point Selection for Cell-free Massive MIMO
Vu, Thang Xuan UL; Chatzinotas, Symeon UL; ShahbazPanahi, Shahram et al

in IEEE International Conference on Communications (2020)

Cell-free massive multiple-input multiple-output (CF-MIMO) is a promising technological enabler for fifth generation (5G) networks in which a large number of access points (APs) jointly serve the users ... [more ▼]

Cell-free massive multiple-input multiple-output (CF-MIMO) is a promising technological enabler for fifth generation (5G) networks in which a large number of access points (APs) jointly serve the users. Each AP applies conjugate beamforming to precode data, which is based only on the AP's local channel state information. However, by having the nature of a (very) large number of APs, the operation of CF-MIMO can be energy-inefficient. In this paper, we investigate the energy efficiency performance of CF-MIMO by considering a practical energy consumption model which includes both the signal transmit energy as well as the static energy consumed by hardware components. In particular, a joint power allocation and AP selection design is proposed to minimize the total energy consumption subject to given quality of service (QoS) constraints. In order to deal with the combinatorial complexity of the formulated problem, we employ norm $l_{2,1}$-based block-sparsity and successive convex optimization to leverage the AP selection process. Numerical results show significant energy savings obtained by the proposed design, compared to all-active APs scheme and the large-scale based AP selection. [less ▲]

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See detailFull-Duplex Enabled Mobile Edge Caching: From Distributed to Cooperative Caching
Vu, Thang Xuan UL; Chatzinotas, Symeon UL; Ottersten, Björn UL et al

in IEEE Transactions on Wireless Communications (2020)

Mobile edge caching (MEC) has received much attention as a promising technique to overcome the stringent latency and data hungry requirements in future generation wireless networks. Meanwhile, full-duplex ... [more ▼]

Mobile edge caching (MEC) has received much attention as a promising technique to overcome the stringent latency and data hungry requirements in future generation wireless networks. Meanwhile, full-duplex (FD) transmission can potentially double the spectral efficiency by allowing a node to receive and transmit in the same time/frequency block simultaneously. In this paper, we investigate the delivery time performance of full-duplex enabled MEC (FD-MEC) systems, in which the users are served by distributed edge nodes (ENs), which operate in FD mode and are equipped with a limited storage memory. Firstly, we analyse the FD-MEC with different levels of cooperation among the ENs and take into account a realistic model of self-interference cancellation. Secondly, we propose a framework to minimize the system delivery time of FD-MEC under both linear and optimal precoding designs. Thirdly, to deal with the non-convexity of the formulated problems, two iterative optimization algorithms are proposed based on the inner approximation method, whose convergence is analytically guaranteed. Finally, the effectiveness of the proposed designs are demonstrated via extensive numerical results. It is shown that the cooperative scheme mitigates inter-user and self interference significantly better than the distributed scheme at an expense of inter-EN cooperation. In addition, we show that minimum mean square error (MMSE)-based precoding design achieves the best performance-complexity trade-off, compared with the zero-forcing and optimal designs. [less ▲]

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See detailModeling and Implementation of 5G Edge Caching over Satellite
Vu, Thang Xuan UL; Poirier, Yannick; Chatzinotas, Symeon UL et al

in International Journal of Satellite Communications and Networking (2020)

The fifth generation (5G) wireless networks have to deal with the high data rate and stringent latency requirements due to the massive invasion of connected devices and data-hungry applications. Edge ... [more ▼]

The fifth generation (5G) wireless networks have to deal with the high data rate and stringent latency requirements due to the massive invasion of connected devices and data-hungry applications. Edge caching is a promising technique to overcome these challenges by prefetching the content closer to the end users at the edge node’s local storage. In this paper, we analyze the performance of edge caching 5G networks with the aid of satellite communication systems. Firstly, we investigate the satellite-aided edge caching systems in two promising use cases: a) in dense urban areas, and b) in sparsely populated regions, e.g., rural areas. Secondly, we study the effectiveness of satellite systems via the proposed satellite-aided caching algorithm, which can be used in three configurations: i) mono-beam satellite, ii) multi-beam satellite, and iii) hybrid mode. Thirdly, the proposed caching algorithm is evaluated by using both empirical Zipf-distribution data and the more realistic Movielens dataset. Last but not least, the proposed caching scheme is implemented and tested by our developed demonstrators which allow real-time analysis of the cache hit ratio and cost analysis. [less ▲]

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