Centralized power control in cellular radio systems

Grandhi, S.A.; Vijayan, Ramachandran; Goodman, D.J.; Zander, Jens

doi:10.1109/25.260766

Cited by 320 publications

(163 citation statements)

References 4 publications

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“…It is easy to show that matrix C is a non-negative irreducible matrix using the same methodology of [17]. Therefore, matrix C has interesting properties that can be used in the ASM methodology.…”

Section: Coupling Matrixmentioning

confidence: 99%

Simulated Annealing-Based Advanced Spectrum Management Methodology for WCDMA Systems

Nasreddine

Perez-Romero

Salient

et al. 2008

2008 IEEE International Conference on Communications

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Abstract-In this paper, we propose a new Advanced Spectrum Management (ASM) methodology for WCDMA systems using simulated annealing and the concept of coupling matrix, which is able to capture inter-cell interactions. The proposed methodology takes into account the fact that each cell can be associated to more than one carrier. Simulation results show that the proposed methodology increases spectrum efficiency while guaranteeing the requested QoS levels. Moreover, it allows releasing carriers in some cells, so that they could be eventually used by e.g. secondary cognitive radio users exploiting the flexible spectrum allocation and opportunistic spectrum access.

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Section: Coupling Matrixmentioning

confidence: 99%

Simulated Annealing-Based Advanced Spectrum Management Methodology for WCDMA Systems

Nasreddine

Perez-Romero

Salient

et al. 2008

2008 IEEE International Conference on Communications

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“…Therefore, and again, postulating one global unidimensional criteria (e.g. as is done in traditional algorithms (Grandhi et al, 1993) ) to derive a power allocation method would not allow assessment of the potential benefits of different trade-offs. Indeed, the results of the proposed design will show how the traditional approach to power control (which consists in maximizing the minimum SINR for all mobiles) in spite of offering interesting capabilities in some situations, also prevents most mobiles from achieving their QoS potential.…”

Section: Radio Communications 498mentioning

confidence: 99%

“…Grandhi's centralized power control (CPC) algorithm (Grandhi et al, 1993) is applied in the DBS architecture by considering the M master DBS for the M mobiles on a given channel with g ij (1 ≤ i ≤ M, 1 ≤ j ≤ M) denoting the gain of the link from mobile i to DBS j, with DBS i being mobile i's master connection. Matrix A is defined as…”

Section: Centralized Power Control (Cpc)mentioning

confidence: 99%

Multi-Agent Design for the Physical Layer of a Distributed Base Station Network

Leroux¹,

Roy²

2010

Radio Communications

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“…Based on this, a central decision maker, the Base Station, gathers local information from the users through a control channel, elaborates an intelligent selection of their operational parameters, such as their transmit power, channel or time schedule, and communicates it to them. In this general context, the research community has formulated and tackled PC problems [9][10][11][12][13][14] to achieve common or different signal to interference plus noise ratio (SIN R) requirements, maximum total system throughput, maximum weighted throughput, maximum worst user throughput or minimum transmit power, subject to QoS constraints from individual users, like SIN R, data rate or outage probability. In the CR regime, the centralized PC problem retains its basic form but with some small alterations.…”

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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Centralized power control in cellular radio systems

Cited by 320 publications

References 4 publications

Simulated Annealing-Based Advanced Spectrum Management Methodology for WCDMA Systems

Simulated Annealing-Based Advanced Spectrum Management Methodology for WCDMA Systems

Multi-Agent Design for the Physical Layer of a Distributed Base Station Network

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

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