Power Control for Cognitive Radio Networks Under Channel Uncertainty

Dall’Anese, Emiliano; Kim, Seung Jun; Giannakis, Georgios B.; Pupolin, S.

doi:10.1109/twc.2011.081711.110323

Cited by 86 publications

(82 citation statements)

References 31 publications

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“…The investigation of suitable methods in order to provide robustness against channel/interference uncertainty has recently received a lot of attention, especially in the following two contexts: (i) robust power control for Single Input Single Output (SISO) CR networks [149], [153], [156], [157], and (ii) robust beamforming for MIMO or Multiple Input Single Output (MISO) CR networks [152], [154], [155], [162]. We describe important design issues for these scenarios below.…”

Section: ) Theoretical Model Based Estimation: Some Existingmentioning

confidence: 99%

“…This problem has been widely studied in the literature in various settings [149], [153], [156], [157]. This can be basically formulated as the maximization of the SUs' social utility subject to three constraints, namely, a given PU interference threshold, the SUs' minimum requested SINR, and the SUs' upper bound on their transmit power levels.…”

Section: ) Robust Power Control For Siso Cr Networkmentioning

confidence: 99%

See 1 more Smart Citation

Cognitive Radio Techniques Under Practical Imperfections: A Survey

Sharma

Bogale

Chatzinotas

et al. 2015

IEEE Commun. Surv. Tutorials

196

111

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Abstract-Cognitive Radio (CR) has been considered as a potential candidate for addressing the spectrum scarcity problem of future wireless networks. Since its conception, several researchers, academic institutions, industries, regulatory and standardization bodies have put their significant efforts towards the realization of CR technology. However, as this technology adapts its transmission based on the surrounding radio environment, several practical issues may need to be considered. In practice, several imperfections such as noise uncertainty, channel/interference uncertainty, transceiver hardware imperfections, signal uncertainty, synchronization issues, etc. may severely deteriorate the performance of a CR system. To this end, the investigation of realistic solutions towards combating various practical imperfections is very important for successful implementation of the cognitive technology. In this direction, first, this survey paper provides an overview of the enabling techniques for CR communications. Subsequently, it discusses the main imperfections that may occur in the most widely used CR paradigms and then reviews the existing approaches towards addressing these imperfections. Finally, it provides some interesting open research issues.

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Section: ) Theoretical Model Based Estimation: Some Existingmentioning

confidence: 99%

Section: ) Robust Power Control For Siso Cr Networkmentioning

confidence: 99%

Cognitive Radio Techniques Under Practical Imperfections: A Survey

Sharma

Bogale

Chatzinotas

et al. 2015

IEEE Commun. Surv. Tutorials

196

111

View full text Add to dashboard Cite

show abstract

“…The optimal or suboptimal power allocation policies in the underlay mode have been already investigated widely [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. A heuristic algorithm in CR systems under OFDMA has been reported in [4].…”

Section: Introductionmentioning

confidence: 99%

Robust power allocation for massive connections underlaying cognitive radio networks with channel uncertainty

Ren

Chen

et al. 2016

J Wireless Com Network

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This paper investigates a robust power allocation scheme for a cognitive radio network (CRN) with channel uncertainty, where a large number of secondary connections (SCs) share the same frequency spectrum with a primary user (PU). Specifically, considering the fact that the channel gain estimates from the secondary transmitters (S-Tx) to the primary receiver (P-Rx) are typically uncertain and cannot be perfectly known in practice, this paper advocates a robust interference constraint. Meanwhile, we take the S-Tx's transmit power limitation and interference between SCs into account. The optimization problem is a non-convex and non-linear program (NCNLP) with an outage probability constraint. Both the original objective function and the outage probability constraint are converted into manageable forms via the mathematical transformation. Then, we introduce the convex optimization theory to solve the intractable problem. Additionally, we develop an efficient algorithm to obtain the near-optimal solution based on the interior point method, where we take advantage of the Newton algorithm to make the search of the feasible solution simple and effective. Finally, we conduct the in-depth simulations under various parameter configurations, which demonstrate that our proposed robust power allocation scheme can achieve higher performance in comparison to the existing scheme in the literature.

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“…One critical modification is the knowledge of the interference channels from the CR transmitters to the PU receivers. Previous work has considered perfect CR-to-PU channel knowledge [15], [16], limited-rate feedback from the PUs on CR-to-PU channel gains [17], imperfect CR-to-PU channel knowledge [18] and CR-to-PU channel uncertainty knowledge attained through SS or channel gain cartography [19].…”

Section: Introductionmentioning

confidence: 99%

Power Control in Cognitive Radio Networks Using Cooperative Modulation and Coding Classification

Tsakmalis

Chatzinotas

Ottersten

2015

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract-In this paper, a centralized Power Control (PC) scheme aided by interference channel gain learning is proposed to allow a Cognitive Radio (CR) network to access the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The main idea is the CR network to constantly probe the band of the PU with intelligently designed aggregated interference and sense whether the Modulation and Coding scheme (MCS) of the PU changes in order to learn the interference channel gains. The coordinated probing is engineered by the Cognitive Base Station (CBS), which assigns appropriate CR power levels in a binary search way. Subsequently, each CR applies a Modulation and Coding Classification (MCC) technique and sends the sensing information through a control channel to the CBS, where all the MCC information is combined using a fusion rule to acquire an MCS estimate of higher accuracy and monitor the probing impact to the PU MCS. After learning the normalized interference channel gains towards the PU, the CBS selects the CR power levels to maximize total CR network throughput while preserving the PU MCS and thus its QoS. The effectiveness of the proposed technique is demonstrated through numerical simulations.

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Power Control for Cognitive Radio Networks Under Channel Uncertainty

Cited by 86 publications

References 31 publications

Cognitive Radio Techniques Under Practical Imperfections: A Survey

Cognitive Radio Techniques Under Practical Imperfections: A Survey

Robust power allocation for massive connections underlaying cognitive radio networks with channel uncertainty

Power Control in Cognitive Radio Networks Using Cooperative Modulation and Coding Classification

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