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See detailEnergy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization
Yuan, Yaxiong UL; Lei, Lei UL; Vu, Thang Xuan UL et al

in IEEE Transactions on Vehicular Technology (2021)

In unmanned aerial vehicle (UAV) applications, the UAV's limited energy supply and storage have triggered the development of intelligent energy-conserving scheduling solutions. In this paper, we ... [more ▼]

In unmanned aerial vehicle (UAV) applications, the UAV's limited energy supply and storage have triggered the development of intelligent energy-conserving scheduling solutions. In this paper, we investigate energy minimization for UAV-aided communication networks by jointly optimizing data-transmission scheduling and UAV hovering time. The formulated problem is combinatorial and non-convex with bilinear constraints. To tackle the problem, firstly, we provide an optimal relax-and-approximate solution and develop a near-optimal algorithm. Both the proposed solutions are served as offline performance benchmarks but might not be suitable for online operation. To this end, we develop a solution from a deep reinforcement learning (DRL) aspect. The conventional RL/DRL, e.g., deep Q-learning, however, is limited in dealing with two main issues in constrained combinatorial optimization, i.e., exponentially increasing action space and infeasible actions. The novelty of solution development lies in handling these two issues. To address the former, we propose an actor-critic-based deep stochastic online scheduling (AC-DSOS) algorithm and develop a set of approaches to confine the action space. For the latter, we design a tailored reward function to guarantee the solution feasibility. Numerical results show that, by consuming equal magnitude of time, AC-DSOS is able to provide feasible solutions and saves 29.94% energy compared with a conventional deep actor-critic method. Compared to the developed near-optimal algorithm, AC-DSOS consumes around 10% higher energy but reduces the computational time from minute-level to millisecond-level. [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 detailResource Optimization With Load Coupling in Multi-Cell NOMA
You, Lei; Yuan, Di; Lei, Lei UL et al

in IEEE Transactions on Wireless Communications (2018)

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See detailA Framework for Optimizing Multi-cell NOMA: Delivering Demand with Less Resource
You, Lei; Lei, Lei UL; Yuan, Di et al

in 2017 IEEE Global Communications Conference (GLOBECOM) (2017, December)

Non-orthogonal multiple access (NOMA) allows multiple users to simultaneously access the same time-frequency resource by using superposition coding and successive interference cancellation (SIC). Thus far ... [more ▼]

Non-orthogonal multiple access (NOMA) allows multiple users to simultaneously access the same time-frequency resource by using superposition coding and successive interference cancellation (SIC). Thus far, most papers on NOMA have focused on performance gain for one or sometimes two base stations. In this paper, we study multi-cell NOMA and provide a general framework for user clustering and power allocation, taking into account inter-cell interference, for optimizing resource allocation of NOMA in multi-cell networks of arbitrary topology. We provide a series of theoretical analysis, to algorithmically enable optimization approaches. The resulting algorithmic notion is very general. Namely, we prove that for any performance metric that monotonically increases in the cells’ resource consumption, we have convergence guarantee for global optimum. We apply the framework with its algorithmic concept to a multi-cell scenario to demonstrate the gain of NOMA in achieving significantly higher efficiency. [less ▲]

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See detailAdaptive Cloud Radio Access Networks: Compression and Optimization
Vu, Thang Xuan UL; Nguyen, Hieu Duy; Quek, Tony Q.S. et al

in IEEE Transactions on Signal Processing (2017), 65(1), 228-241

Future mobile networks are facing with exponential data growth due to the proliferation of diverse mobile equipment and data-hungry applications. Among promising technology candidates to overcome this ... [more ▼]

Future mobile networks are facing with exponential data growth due to the proliferation of diverse mobile equipment and data-hungry applications. Among promising technology candidates to overcome this problem, cloud radio access network (CRAN) has received much attention. In this work, we investigate the design of fronthaul in C-RAN uplink by focusing on the compression and optimization in fronthaul uplinks based on the statistics of wireless fading channels. First, we derive the system block error rate (BLER) under Rayleigh fading channels. In particular, upper and lower bounds of the BLER union bound are obtained in closed-form. From these bounds, we gain insight in terms of diversity order and limits of the BLER. Next, we propose adaptive compression schemes to minimize the fronthaul transmission rate subject to a BLER constraint. Furthermore, a fronthaul rate allocation is proposed to minimize the system BLER. It is shown that the uniform rate allocation approaches the optimal scheme as the total fronthauls’ bandwidth increases. Lastly, numerical results are presented to demonstrate the effectiveness of our proposed optimizations. [less ▲]

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See detailFronthaul compression and optimization for cloud radio access networks
Vu, Thang Xuan UL; Nguyen, Hieu Duy; Quek, Tony Q. S. et al

in 2016 IEEE International Conference on Communications (ICC) (2016)

Detailed reference viewed: 98 (1 UL)