Power allocation in cognitive spectrum sharing area using effective capacity in imperfect fading channel

Pirmoradian, Mahdi; Politis, Christos

doi:10.1109/cnds.2011.5764555

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…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%

“…However, other spectrum sensing approaches, such as QoS-aware, cooperative, and cyclostationary-based spectrum sensing have not been explored while taking into consideration the EC model. This gray-space utilization pattern of CRNs with the EC concept has also been used to develop various optimal resource allocation schemes with stringent-delay requirements [102], [110], [116], [124], [137], [153], [172], [237]. Among resource allocation strategy, problem of power allocation has been formulated and then solved by applying different optimization scenarios.…”

Section: A Cognitive Radio Network (Crns)mentioning

confidence: 99%

“…Underlay CRNs [143] Channel Errors Rayleigh Spectrum Sharing with Imperfect CSI [145] QoS with Interference Limitations Rayleigh Multiple Channel Spectrum Sharing [148] Interference Limitations Rayleigh Spectrum Sensing In CRNs [235] Channel Gain Rayleigh [236] Analysis of Outdated CSI Not Defined [128] General Fading α − µ CRNs with Various Fading Conditions [129] General Fading α − µ [115] Different QoS Applications Nakagami-m [234] Optimal Power Allocation Rayleigh Resource Allocation [137] Trade-off Between SUs and PUs performance Rayleigh [237] Interference Power Constraint Gamma Fading [153] Power Allocation with Imperfect CSI Rayleigh [102] Power Allocation with Imperfect CSI Rayleigh [110] Securing the CRNs Nakagami-m [172] Optimal Power Allocation Rayleigh [116] Optimal Power Allocation Nakagami-m [124] Optimal Power Allocation Nakagami-M CR-Based MAC [118] Rate and Power Estimation with MQAM Nakagami-m Modulation In CRNs [120] Power Estimation with MQAM Nakagami-m [203] SUs Throughput Rayleigh FDMA-Based CRNs [142] SUs arrival rate Rayleigh CR-Relay Networks [158] Interference and Delay Constraints Rayleigh [195] SUs Capacity Rayleigh Overlay CRNs [157] Power Level based on PUs Rayleigh Spectrum Sensing In CRNs [198] SUs Arrival Rate Rayleigh [155] Performance Limitations of SUs Rayleigh Interference Limitations In CRNs [238] SUs Capacity Analysis Asymmetric Fading [239] Channel Estimation Error Nakagami-m CRNs with MISO Systems [240] Two phase channel Access Method Not Defined Channel Access In CRNs [241] Hopping Based Channel Access Not Defined [225] Optimal Power Allocation Gamma Fading Resource Allocation [103] SUs Arrival Rate Nakagami-m…”

Section: Areamentioning

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: Interweave Crnsmentioning

confidence: 99%

Section: A Cognitive Radio Network (Crns)mentioning

confidence: 99%

Section: Areamentioning

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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“…The effective capacity maximization problem was also investigated in [7] under the Nakagami-m block-fading channel assumption and the interference power constraint. The scenario had been further extended to an imperfect fading channel in [8], in which the impact of the channel estimation error on the effective capacity was studied. In [9] the interference of primary users was considered.…”

Section: Introductionmentioning

confidence: 99%

Optimal power allocation for delay-constrained multiple-channel cognitive radio systems

Zhang

2014

2014 International Wireless Communications and Mobile Computing Conference (IWCMC)

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Power allocation for the multiple-channel cognitive systems is studied in this paper. Our objective is to maximize the throughput of the cognitive link while providing delay guarantee for the secondary user. By using the effective capacity theory, the problem is modeled as effective capacity maximization problem with average power constraint, which turns out to be a constrained convex optimization problem. Through Lagrangian dual theory, the problem is divided into a group of sub-problems, where each sub-problem aims at maximizing the effective capacity under a certain channel sensing result. Then a gradient descent method based algorithm is proposed which solves the problem effectively. Simulation results show that the proposed power allocation scheme can effectively improve the effective capacity for the cognitive link and provide specific delay guarantee in terms of delay violation probability.

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