Abstract
In this paper, we investigate the optimal beamforming design to achieve joint congestion control and energy-efficient resource allocation in cache-enabled sensor networks. The network of interest works in the time-slotted mode. The dynamic buffering queue for each node is introduced to reflect the degree of network congestion and service delay. Then, a time-averaged sum rate maximization problem is proposed under the constraints of queue stability, instantaneous power consumption, average power consumption, and the minimum quality of service requirements. By introducing the method of Lyapunov optimization, the importance of buffering queue backlogs and sum rate maximization can be traded off, then the original queue-aware and time-averaged optimization problem is transformed into a weighted sum rate maximization problem at each time slot. It can be further converted into a second-order cone-programming problem by successive convex approximation, which is convex and can be efficiently solved by off-the-shelf solvers. Numerical results validate that wireless caching can greatly relieve the network congestion by reducing the buffering backlogs, and show that the proposed scheme can trade off the average queue length and time-averaged sum rate by selecting different control parameters.
Highlights
In recent years, the data traffic generated by mobile users has experienced explosive growth [1].Along with the development of mobile networks, the Internet of Things (IoT) and other wireless techniques are expected to bring us a wide variety of mobile applications and even more mobile traffic will be generated [2,3]
We consider a small-scale sensor network, where one cluster head (CH) serves six nodes. This is because serving too many nodes for a CH will lead to unbearable computational complexity [20], and the quality of service (QoS) may not be guaranteed
The beamforming design for each cluster can be realized by the same methods presented in this paper
Summary
The data traffic generated by mobile users has experienced explosive growth [1].Along with the development of mobile networks, the Internet of Things (IoT) and other wireless techniques are expected to bring us a wide variety of mobile applications and even more mobile traffic will be generated [2,3]. It is predicted that most of the traffic will result from the multimedia video services, which require higher network throughput and stricter network latency To meet these unprecedented traffic demands and challenges, the standardization process of the fifth-generation (5G) network is accelerated in aspects of the network capacity and latency [4,5]. Much data traffic is produced minute by minute, and there are many repeated contents among it, which will be transmitted more than once during the traffic-peak time periods Confronting such a severe situation, proactive caching is regarded as one of the most promising techniques in 5G communication system to effectively alleviate the severe backhaul burden and improve the service delay, which has drawn tremendous attention from the industries and academia [6,7,8,9]
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