Effective capacity of cognitive cooperative relay networks over α-μ fading channels

Kabiri, Charles; Zepernick, Hans‐Jürgen; Tran, Hung

doi:10.1109/apcc.2015.7412551

Cited by 5 publications

(12 citation statements)

References 17 publications

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“…In cellular networks, with statistical QoS provisioning, Rayleigh fading has been extensively evaluated with EC metric. In CRNs with multiple channels, prediction related to multiple interference has also been studied with Nakagami-m Networks [21], [33], [39], [57], [58], [60]- [62], [65], [77], [81], [93]- [124] Rician [42], [45], [70], [125] Log-Normal [126] Weibull [127] Generalized Fading Models [52], [128]- [131] Rayleigh Fading Channels [6], [23], [26], [28], [29], [31], [32], [35], [41], [44], [49], [54], [66]- [69], [73]- [75], [79], [80], [83], [84], [132]- [218] 2D [160] Multidimensional [219] Turbulence Model [220]- [223] Gamma Fading…”

Section: A Stochastic Fading Modelsmentioning

confidence: 99%

“…In these work, buffer-aided relaying strategy has been employed. The EC model in these underlay CRNs has also been used with varying fading conditions, such as generalized fading models, stochastic fading model, and Nakagamim fading channel [115], [128], [129]. Adaptive modulation techniques have also been analyzed using the EC concept in underlay CRNs [118], [120], [203].…”

Section: A Cognitive Radio Network (Crns)mentioning

confidence: 99%

“…Hybrid CRNs Spectrum Sensing [143], [145], [148], [234]- [236] Various Fading Conditions [115], [128], [129] Resource Allocation [102], [110], [116], [124], [137], [153], [172], [237] Modulation Techniques [118], [120], [203] Relaying In CRNs [142], [158], [195] Spectrum Sensing [157], [198] Interference Constraints [155], [238] CRNs with MISO Systems [239] Channel Access Approaches [240], [241] Resource Allocation [37], [44], [58], [60], [103], [107], [177], [181], [202], [225], [242]- [245] Spectrum Sensing [15] Interference Constraints [140] QoS Provisioning In M2M Communications [92] Voice Capacity [246], [247] Resource Allocation…”

Section: Interweave Crnsmentioning

confidence: 99%

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Effective Capacity in Wireless Networks: A Comprehensive Survey

Amjad

Musavian

Rehmani

2019

IEEE Commun. Surv. Tutorials

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Low latency applications, such as multimedia communications, autonomous vehicles, and Tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) mobile networks. Existing physicallayer channel models, however, do not explicitly consider qualityof-service (QoS) aware related parameters under specific delay constraints. To investigate the performance of low-latency applications in future networks, a new mathematical framework is needed. Effective capacity (EC), which is a link-layer channel model with QoS-awareness, can be used to investigate the performance of wireless networks under certain statistical delay constraints. In this paper, we provide a comprehensive survey on existing works, that use the EC model in various wireless networks. We summarize the work related to EC for different networks such as cognitive radio networks (CRNs), cellular networks, relay networks, adhoc networks, and mesh networks. We explore five case studies encompassing EC operation with different design and architectural requirements. We survey various delay-sensitive applications such as voice and video with their EC analysis under certain delay constraints. We finally present the future research directions with open issues covering EC maximization.

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Section: A Stochastic Fading Modelsmentioning

confidence: 99%

Section: A Cognitive Radio Network (Crns)mentioning

confidence: 99%

Section: Interweave Crnsmentioning

confidence: 99%

See 1 more Smart Citation

Effective Capacity in Wireless Networks: A Comprehensive Survey

Amjad

Musavian

Rehmani

2019

IEEE Commun. Surv. Tutorials

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“…Channel effective capacity for one secondary transmitter and two secondary receivers is stated in the work of Hammouda et al Different overlay sensing strategies are also discussed and two strategies of data sending, greedy, and lower‐rate are presented for channel capacity assessment. In the work of Kabiri and Zepernick, the effective capacity for underlay cognitive system in α‐μ fading is discussed. Effective capacity, packet loss rate, and delay are analyzed while the effective capacity maximization and time‐slot allocation in cooperative cognitive system are presented in the work of Wang and Liu .…”

Section: Related Workmentioning

confidence: 99%

Effective capacity and outage probability assessment of multiple‐relay cognitive communication systems in Nakagami‐m and Rayleigh fading channel

Zahedi

Ergen

et al. 2019

Trans Emerging Tel Tech

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The radio spectrum of communication systems is largely occupied by wideband technologies, and thus, efficient usage of spectrum is a crucial requirement from regulators' and service providers' perspectives. Cognitive communication has been proposed as a potential solution to resolve the spectrum scarcity problem.Cognitive radio schemes can be jointly applied with cooperative communications to improve the performance under both overlay and underlay strategies as the two main cognitive strategies supported by multiple relays. This combination significantly improves the reliability and capacity of data communication. In this paper, two QoS parameters, namely, the effective capacity and outage probability, have been thoroughly analyzed in a Nakagami-m fading channel that is the typical channel condition often considered in the transmission of communication systems. Closed-form expressions are obtained for effective capacity and outage probability in both underlay and overlay strategies. Simulation results verify the effectiveness of the mathematical modelling of the proposed QoS parameters.Trans Emerging Tel Tech. 2020;31:e3841.wileyonlinelibrary.com/journal/ett

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“…Various OL sensing strategies are also discussed and two different strategies of data sending, greedy and lower-rate are presented for channel capacity assessment. In [25], the EC for UL CS in − fading is discussed. EC, packet loss rate and delay are analyzed as QoS parameters of the system.…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of multiple-relay cognitive communication system in Rayleigh fading channel

Zahedi

2019

Telecommun Syst

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The spectrum scarcity in communication systems has attracted many researches in the area of cognitive communication which is known as a precious solution to overcome this problem. Overlay (OL) and underlay (UL) strategies are two main techniques in the cognitive systems (CSs). On the other side, cooperative communication improves the reliability and capacity of data communications when using the multiple-relay scheme. In this paper, assuming the multiple-relay cooperative cognitive communication system, the quality of service (QoS) parameters as effective capacity (EC) and outage probability (OP) are analyzed in details. A closed-form mathematical expression is obtained for OP and EC in Rayleigh fading channel considering different constraints on the CS such as false detection of busy/idle state of primary user (PU), or interference threshold of secondary user on PU in OL and UL strategies, respectively. Moreover, the comparison of OL and UL strategies in terms of the QoS parameters is done. As will be shown in this paper, simulation and numerical results verify each other and follow the mathematical analysis of the QoS parameters.

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Effective capacity of cognitive cooperative relay networks over α-μ fading channels

Cited by 5 publications

References 17 publications

Effective Capacity in Wireless Networks: A Comprehensive Survey

Effective Capacity in Wireless Networks: A Comprehensive Survey

Effective capacity and outage probability assessment of multiple‐relay cognitive communication systems in Nakagami‐m and Rayleigh fading channel

Performance analysis of multiple-relay cognitive communication system in Rayleigh fading channel

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