Channel Time Allocation PSO for Gigabit Multimedia Wireless Networks

Scott-Hayward, Sandra; Garcia-Palacios, Emiliano

doi:10.1109/tmm.2014.2298211

Cited by 23 publications

(13 citation statements)

References 25 publications

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“…The PSO algorithm optimized all the variables, including network weights and network parameters. Scott-Hayward and Garcia-Palacios [260] introduced a resource allocation solution capable of handling mixed media applications within the constraints of a 60 GHz wireless network. A new channel time allocation PSO (CTA-PSO) was proposed to solve the network utility maximization resource allocation problem.…”

Section: Communicationmentioning

confidence: 99%

“…Ganguly et al [221], Komsiyah [222], Feng et al [223], Pekşen et al [224], Yang et al [225], de Mendonça et al [226], Liu et al [227], Aich and Banerjee [228], Chou et al [229], Lee et al [230], Thakral and Bakhshi [231], Fister et al [232], Aghaei et al [233], Selakov et al [234], Shirvany et al [235], and Tungadio et al [236] Automatic control Cai and Yang [237], Kolomvatsos and Hadjieftymiades [238], Pandey et al [239],Štimac et al [240], Nedic et al [241], Chang and Chen [242], Xiang et al [243], Danapalasingam [244], Mahmoodabadi et al [245], Zhong et al [246], Perng et al [247], Huang and Li [248], and Nisha and Pillai [249] Communication Yousefi et al [250], Sun et al [251], Yongqiang et al [252], Chiu et al [253], Zubair and Moinuddin [255], Kim and Lee [256], Yazgan and Hakki Cavdar [257], Rabady and Ababneh [258], Das et al [259], Scott-Hayward and Garcia-Palacios [260], Omidvar and Mohammadi [261], and Kuila and Jana [262] Operations Liu and Wang…”

Section: Area Publication Electrical and Electronic Engineeringmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

683

380

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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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Section: Communicationmentioning

confidence: 99%

Section: Area Publication Electrical and Electronic Engineeringmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

683

380

View full text Add to dashboard Cite

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“…The nature of the utility function for multimedia applications determines the type of optimization problem [4]. Classically, resource management solutions handle video as an isolated application in the network, thus solving a convex optimization problem.…”

Section: Optimization Techniquesmentioning

confidence: 99%

“…With the focus of achieving a near global optimal solution in a time suitable for implementation in a real network, CTA-PSO has been developed [4]. CTA-PSO overcomes premature convergence by controlling exploration and …”

Section: Channel Time Allocation Particle Swarm Optimizationmentioning

confidence: 99%

Multimedia resource allocation in mmwave 5G networks

Scott-Hayward

Garcia-Palacios

2015

IEEE Commun. Mag.

Self Cite

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INTRODUCTIONThe volume of mobile traffic is exploding, driven by a proliferation of connected devices. The high bandwidth required by multimedia applications is severely stretching the available wireless spectrum. Consumers influenced by fixed access broadband services expect almost instant file uploads/downloads and transfers between terminals (laptops, tablets, and smart TVs), IPTV, and HD video. The challenge for service providers is therefore to extend the capability of wireless access to maintain performance characteristics in the transition to predominantly wireless service provision. The entertainment industry will be just the first adopter to drive and inspire other futuristic scenarios in connected health, social and community interaction, and education.The 60 GHz band, with up to four 2.16 GHz channels, supports high-data-rate short-range line-of-sight (LOS) directional transmissions. Millimeter-wave (mmWave) communication technologies such as IEEE 802.15.3c-2009 [1] and IEEE 802.11ad [2] are uniquely positioned due to their ability to deliver the gigabit-per-second throughput envisaged for the fifth generation (5G).Despite the adverse propagation characteristics of mmWave technologies (a result of high propagation loss due to oxygen absorption and atmospheric attenuation), the fact that waves are confined within walls makes it ideal for personal communications: promoting security and privacy. This also promotes more efficient frequency reuse, therefore creating very high bandwidth hubs that are ideal for the delivery of gigabitper-second applications. A major question, however, is how to combat the impact of blockage of the LOS link by human shadowing or obstacles, which severely impacts the transmission.In order to support the high volume of highquality services, optimized resource allocation is required. Optimizing resource allocation in a multi-user multimedia gigabit scenario poses interesting challenges. Most traditional optimization techniques have been used to solve convex optimization problems. However, real-time multimedia applications introduce non-convex utility functions in which the quality perceived by the user does not increase gracefully with an increase in throughput. A re-think of resource allocation optimization techniques is necessary in this context. Relatively new approaches, such as particle swarm optimization (PSO), are ideal candidates to solve non-convex problems. Our proposal compares PSO to other techniques and presents channel-time allocation PSO (CTA-PSO) as a solution to optimize channel time allocation (resource) even when blockage occurs in the mmWave network. IEEE 802.15.3c [1] and IEEE 802.11ad [2] provide mechanisms at the medium access control (MAC) layer to allocate resource (in the form of channel access time) to multiple users. A period of contention allows for different terminals to request access time while allocated channel time slots provide dedicated data transmission time. The advantage of scheduled access is to provide a dedicated time slot for communic...

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“…Conventional works had proposed several approaches to operate mmWave meshed backhaul (This paper mainly focuses on the mmWave backhaul part. Readers who are interested about mmWave technologies for improving throughput and delay performance with varying traffic demand at the access layer might refer to related works in [17][18][19]). For example, anchored BSs with wired backhaul working as gateways for other BSs were introduced in [20].…”

Section: Introductionmentioning

confidence: 99%

Context-Based Dynamic Meshed Backhaul Construction for 5G Heterogeneous Networks

Tran

Santos

Ogawa

et al. 2018

JSAN

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Five-G heterogeneous network overlaid by millimeter-wave (mmWave) access employs mmWave meshed backhauling as a promising cost-efficient backhaul architecture. Due to the nature of mobile traffic distribution in practice which is both time-variant and spatially non-uniform, dynamic construction of mmWave meshed backhaul is a prerequisite to support the varying traffic distribution. Focusing on such scenario of outdoor dynamic crowd (ODC), this paper proposes a novel method to control mmWave meshed backhaul for efficient operation of mmWave overlay 5G HetNet through Software-Defined Network (SDN) technology. Our algorithm is featured by two functionalities, i.e., backhauling route multiplexing for overloaded mmWave small cell base stations (SC-BSs) and mmWave SC-BSs’ ON/OFF status switching for underloaded spot. In this paper, the effectiveness of the proposed meshed network is confirmed by both numerical analyses and experimental results. Simulations are conducted over a practical user distribution modeled from measured data in realistic environments. Numerical results show that the proposed algorithm can cope with the locally intensive traffic and reduce energy consumption. Furthermore, a WiGig (Wireless Gigabit Alliance certified) device based testbed is developed for Proof-of-Concept (PoC) and preliminary measurement results confirm the proposed dynamic formation of the meshed network’s efficiency.

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Channel Time Allocation PSO for Gigabit Multimedia Wireless Networks

Cited by 23 publications

References 25 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Multimedia resource allocation in mmwave 5G networks

Context-Based Dynamic Meshed Backhaul Construction for 5G Heterogeneous Networks

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