Base station (BS) sleeping technology has become one of the significant technologies in fifth‐generation green communications. However, enormous communication overhead and coverage holes produced by existing sleeping strategies will decline the robustness of the network. To tackle this problem, this paper proposes a distance‐sensitive distributed repulsive sleeping strategy (DSDRSS), based on hard‐core point process (HCPP). First, through information exchanges, presleeping BSs in the same region form a sleeping cluster (SC) whose size is limited by sleeping distance. Second, BSs in the SC perform BS sleeping with a mark method where BSs will be randomly assigned a mark, and BSs with the lowest mark will remain on to ensure the coverage. Third, to characterize the performance of DSDRSS, the analytical expressions of sleeping probability, coverage probability, and average achievable rate for user equipment (UE) under DSDRSS are derived. Finally, the coverage characteristics of UE under DSDRSS are analyzed and compared with those under different sleeping operations. DSDRSS realizes sleeping operations through the cooperation between BSs in an SC, not relying on the feedback links between a small BS and the control center. As a result, DSDRSS can not only enable flexible perception of traffic changes in sleeping area but also complete sleeping with less overhead. The simulation results show that DSDRSS supports more dependable coverage compared with random sleeping strategy and general repulsive sleeping strategy.
Large scale deployment of Internet of Things (IoT) devices poses challenges in resource allocation. In this paper, alternating direction method of multipliers (ADMM) is adopted to solve such large scale resource allocation problems. Based on this, three optimization problems are investigated in a hierarchical IoT network. Considering ADMM could not solve a non-convex optimization problem directly, a non-convex fractional programming problem i.e., energy efficiency maximization problem for IoT region server, is formulated. Faced with this problem, we introduce the Dinkelbach algorithm to transfer the energy efficiency maximization problem into an equivalent convex optimization problem. Then the classic ADMM with two blocks is employed to solve the equivalent convex optimization problem. On the other hand, the classic ADMM with two blocks could not satisfy the convergence speed demands of the high-dimensional convex optimization problems any more. Thus, the network latency minimization problem for controller is designed and then solved by the Jacobian-ADMM algorithm in parallel. It is hard to satisfy controller and IoT region servers' objectives at the same time. Given this, an incentive mechanism on the basis of Stackelberg game is designed. Thus a game-based resource allocation problem is proposed to deal with the contradiction between the centralized objective of the controller and the individual objectives from the IoT region servers. Based on the Dinkelbach algorithm and Jacobian-ADMM algorithm, a two-layer iterative resource allocation algorithm is posed to solve the game-based resource allocation problem. Last but not least, the convergence of the proposed algorithms are analyzed with numerous simulation results.
Emerging 5G applications impose stringent requirements on network latency and reliability. In this work, we propose a low-latency reliable device-to-device (D2D) relay network framework to improve the cell coverage and user satisfaction. Particularly, we develop a cross-layer low-complexity resource allocation algorithm, which jointly optimizes the rate control and power allocation from a long-term perspective. The long-term optimization problem is transformed into a series of short-term subproblems by using Lyapunov optimization, and the objective function is separated into two independent subproblems related to rate control in network layer and power allocation in physical layer. Next, the Karush-Kuhn-Tucher (KKT) conditions and alternating direction method of multipliers (ADMM) algorithm are employed to solve the rate control subproblem and power allocation subproblem, respectively. Finally, simulation results demonstrate that the proposed algorithm can reach 99.9% of the optimal satisfaction of D2D pairs with lower average network delay compared to the baseline algorithm. Furthermore, the convergence time of the ADMM-based power allocation algorithm is only about 1.7% of that by using the CVX toolbox.
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