Using Joint Particle Swarm Optimization and Genetic Algorithm for Resource Allocation in TD-LTE Systems

Du, Jianbo; Zhao, Liqiang; Xin, Jie; Wu, Jen-Ming; Zeng, Jie

doi:10.4108/eai.19-8-2015.2260168

Cited by 5 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results indicate a significant performance difference of the ABC algorithm among others algorithms. It can notice from Figure 5 that PSO shows faster transient convergence relative to ABC and GSA, especially during the first 50 iterations at v r = [0, 8], v r = [8,16], v r = [16,25], and v r = [25, 33]. However, as illustrated in Figure 5, ABC shows faster convergence rate to the minimum solution at various speeds range.…”

Section: Total Signaling Costmentioning

confidence: 92%

“…On the other side, the best global objective function is defined through the beginning of the process up to the current generation. The velocity and position of each particle are defined by (7) and (8), respectively. The remaining steps can be easily followed from the flow chart in Figure 2, in addition to Eberhart and Kennedy.…”

Section: Implementation Of the Optimization Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Optimal location management in LTE networks using evolutionary techniques

Aqeeli

Hashim

Haque

et al. 2019

Int J Communication

View full text Add to dashboard Cite

Summary Wireless network is considered a vital enabler in the world of information technology, specifically, LTE and LTE advanced networks, which are the latest technologies owing to their fast speed, robustness, and large bandwidth. However, in spite of the aforementioned advancements, signaling overhead poses critical challenges in terms of network availability, especially those caused by location management messages which are related to users’ mobility behavior. This paper seeks to address the problem of signaling overhead caused by the location management messages specifically, tracking area update (TAU) and paging by deploying three evolutionary algorithms, namely particle swarm optimization (PSO), artificial bee colony (ABC), and gravitational search algorithm (GSA). The deployed algorithms guarantee yielding the minimum values of the signaling overhead for TAU, paging, and the battery power consumption of the user. It is shown that ABC‐based algorithm has faster convergence and better signaling overhead when compared with other implemented algorithms. Moreover, the measured relative standard deviation (RSD) value of all algorithms shows low uncertainty of around 1% for the objective function and 3% for the paging, TAU, and power. Hence, the three applied optimization algorithms have proven to be efficient and reliable for solving the problem in a large‐scale environment.

show abstract

Section: Total Signaling Costmentioning

confidence: 92%

Section: Implementation Of the Optimization Algorithmsmentioning

confidence: 99%

Optimal location management in LTE networks using evolutionary techniques

Aqeeli

Hashim

Haque

et al. 2019

Int J Communication

View full text Add to dashboard Cite

show abstract

“…The candidate solution X i is initialized between the minimum and maximum boundaries of the space, termed x min and x max , respectively, such that x i,j (t) ∈ x j min , x j max . On the other side, the velocity 21) with N intv being the number of intervals.…”

Section: Multi-objective Particle Swarm Optimization a Particle mentioning

confidence: 99%

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

Hashim

Abido

2019

Computer Networks

View full text Add to dashboard Cite

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.

show abstract

“…The authors in [15], [16] have proposed solutions to the MaxRate-MinReq problem based on meta heuristics while [17] presents an exact solution. In [15] the authors define the MCS allocation based on Tabu Search.…”

Section: Literature Review and Main Contributionsmentioning

confidence: 99%

“…However, the proposed solution is not compared to the optimal solution and no outage results are provided. In [16], Particle Swarm Optimization is used in order to perform subcarrier and power allocation. However, the problem is simplified by assuming continuous mapping between SNR and transmit data rate.…”

Section: Literature Review and Main Contributionsmentioning

confidence: 99%

Joint Resource Block Assignment and Power Allocation Problem for Rate Maximization With QoS Guarantees in Multiservice Wireless

Maurício

Lima

Sousa³

et al. 2016

JCIS

View full text Add to dashboard Cite

Abstract-We formulate the resource and power assignment problem of maximizing the spectral efficiency of a wireless system subject to user satisfaction constraints in the multiservice scenario. We show that although this optimization problem is nonlinear, it can be converted to an integer linear program. In this way, standard techniques can be used to obtain the optimal solution. Motivated by the high computational complexity of the optimal solution, we propose a fast suboptimal algorithm. Simulation results show that our proposal achieves near-optimal performance in low and medium loads with a much lower computational complexity compared with the algorithm used to obtain the optimal solution. Therefore, our proposed algorithm achieves a good tradeoff between performance and computational complexity. We also show that the addition of adaptive power allocation renders significant performance gains in the considered scenario.Index Terms-Multiservice, quality of service (QoS), rate maximization, resource and power assignment.

show abstract

Using Joint Particle Swarm Optimization and Genetic Algorithm for Resource Allocation in TD-LTE Systems

Cited by 5 publications

References 10 publications

Optimal location management in LTE networks using evolutionary techniques

Optimal location management in LTE networks using evolutionary techniques

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

Joint Resource Block Assignment and Power Allocation Problem for Rate Maximization With QoS Guarantees in Multiservice Wireless

Contact Info

Product

Resources

About