Location management in LTE networks using multi-objective particle swarm optimization

Abstract

Long-term evolution (LTE) and LTE-advance (LTE-A) are widely used efficient network technologies serving billions of users, since they are featured with high spectrum efficiency, less latency, and higher bandwidth. Despite remarkable advantages offered by these technologies, signaling overhead remains a major issue in accessing the network. In particular, the load of signaling is mainly attributed to location management. This paper proposes an efficient approach for minimizing the total signaling overhead of location management in LTE networks using multi-objective particle swarm optimization (MOPSO). Tracking area update (TAU) and paging are considered to be the main elements of the signaling overhead of optimal location management in LTE. In addition, the total inter-list handover contributes significantly to the total signaling overhead. However, the total signaling cost of TAU and paging is adversely related to the total inter-list handover. Hence, two cost functions should be minimized where the first function is the total signaling cost of TAU and paging and the second cost function is the total signaling overhead. The trade-off between these two objectives can be circumvented by MOPSO, which alleviates the total signaling overhead. A set of non-dominated solutions on the Pareto-optimal front is defined and the best compromise solution is presented. The proposed algorithm results in a feasible compromise solution between the two objectives, minimizing the signaling overhead, and in turn, the consumption of the power battery of the user. The efficacy and the robustness of the proposed algorithm have been proven through a large scale environment problem illustrative example. The location management in LTE networks using MOPSO best compromise solution has been compared to the results obtained by a mixed integer non-linear programming (MINLP) algorithm.

A Novel Multi-Objective Five-Elements Cycle Optimization Algorithm

“…In the comparison experiments, one parameter is varied at a time and the other parameters are fixed to the best parameters which are set as the same as Equations (11), (14) and (15). The common parameter settings in the experiment are shown in Table 2 and performed 30 independent runs for each parameter comparison set.…”

“…We designed 3 groups of experiments where P s either varies nonlinearly with the number of iterations, or varies linearly with the number of iterations, or is simply fixed. The nonlinear variation rule is shown in Equation (14) and Figure 4, and the linear variation rule is shown in Equation (26) and Figure 5:…”

“…In the optimization of the microgrid energy dispatch, considering the economic costs, energy utilization rate and environmental benefits as multi-objective, the literature [9] established the microgrid energy allocation model and used the PSEA to solve the MOO problem. Corne et al proposed the Pareto Envelope-based Selection Algorithm (PESA) [10] and its successor Pareto Envelope-based Selection Algorithm II (PESA-II) [11]; Coello et al proposed the Multi-Objective Particle Swarm Optimization algorithm (MOPSO) [12], which was effectively applied to the design of water distribution systems in Reference [13] and minimizing the total signaling overhead of location management in Long-term Evolution (LTE) networks to solve the signaling overhead in accessing the network in the literature [14]. In recent years, there has also been a considerable amount of newly presented multi-objective optimization algorithms showing their competitiveness in the field of multi-objective optimization.…”

Automated performance‐based design technique for an efficient LTE PDSCH implementation using SDSoC tool

“…The column permutation of the sub-block interleaver is given as follows: [0, 16,8,24,4,20,12,28,2,18,10,26,6,22,14,30,1,17,9,25,5,21,13,29,3,19,11,27,7,23,15,31]. The bit_collection block accepts input from three sub-block_interleaver and generates output depending on coding type.…”

“…4 The target of the LTE standard is to increase efficiency spectrum utilization, improve system capacity, and increase data rate. 5 In this paper, hardwaresoftware co-design of the LTE physical downlink shared channel (PDSCH) transmitter and the receiver synthesized by SDSoC for heterogeneous FPGA-CPU platform is proposed. The rest of this paper is organized as follows.…”