Geometry-based optimal power control of fading multiple access channels for maximum sum-rate in cognitive radio networks

Wang, Wei; Wang, Wenbo; Lu, Qianxi; Shin, Kang G.; Peng, Tao

doi:10.1109/twc.2010.06.090868

Cited by 20 publications

(22 citation statements)

References 15 publications

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“…In [6], the optimal power allocation to achieve the outage capacity region boundary for the fading CMAC was derived. In [7], the power allocation problem for the fading CMAC to maximize the ergodic capacity was reformulated as a geometrical problem and the optimal power allocation was then derived. In [8], the optimal power allocation for an interference-limited CMAC to minimize the outage probability of the SUs was proposed.…”

Section: Introductionmentioning

confidence: 99%

Power Allocation for Energy-Harvesting-based Fading Cognitive Multiple Access Channels: with or without Successive Interference Cancellation

2017

International Journal of Electronics and Telecommunications

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Abstract-This paper considers a fading cognitive multiple access channel (CMAC), where multiple secondary users (SUs), who share the spectrum with a primary user (PU), transmit to a cognitive base station (CBS). A power station is assumed to harvest energy from the nature and then provide power to the SUs. We investigate the power allocation problems for such a CMAC to maximize the SU sum rate under the interference power constraint, the sum transmit power constraint and the peak transmit power constraint of each individual SU. In particular, two scenarios are considered: with successive interference cancellation (SIC) and without SIC. For the first scenario, the optimal power allocation algorithm is derived. For the second scenario, a heuristic algorithm is proposed. We show that the proposed algorithm with SIC outperforms the algorithm without SIC in terms of the SU sum rate, while the algorithm without SIC outperforms the algorithm with SIC in terms of the number of admitted SUs for a high sum transmit power limit and a low peak transmit power limit of each individual SU.

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Section: Introductionmentioning

confidence: 99%

Power Allocation for Energy-Harvesting-based Fading Cognitive Multiple Access Channels: with or without Successive Interference Cancellation

2017

International Journal of Electronics and Telecommunications

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“…In our earlier work [14], a geometrical model was constructed to transform a power-control problem to a new geometrical problem on the position relationship between a line and multiple points. This geometrical model is used to obtain an optimal solution by comparing possible optimal solutions, which belong to the second type of optimization mentioned above.…”

Section: Introductionmentioning

confidence: 99%

“…The geometrical properties are utilized to reduce the number of possible solutions, thus lowering the computational complexity. Note that the algorithm in [14] was designed only for the special optimization problem. This motivates us to study how to deploy the geometry-based scheme for more general power-control problems, and the type of problems for which the geometry-based scheme is suitable.…”

Section: Introductionmentioning

confidence: 99%

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Generalized geometry-based optimal power control in wireless networks

Wang

Shin

Zhang

et al. 2012

2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON)

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Abstract-Geometry-based optimal power control was proposed in [14] to transform the power-control problem to a new geometrical problem on the position relationship between a line and some points. This scheme provides a novel visual perspective and lowers the complexity of optimization. We generalize this scheme to a larger class of power-control optimization problems so as to maximize the network utility with multiple average and peak power constraints in wireless networks. To facilitate the handling of the geometrical model, we define a subset of geometrical models with specified characteristics, called a regular geometrical model, and derive the type of power-control problems eligible for the regular geometrical model. For such a type of problems, two strategies are proposed for the construction of the regular geometrical model. Utilizing geometrical properties, we propose a novel geometry-based optimization scheme for the general power-control problem. Its computational complexity is significantly lower than the conventional algorithms. We also provide a further discussion on irregular geometrical model cases. Finally, we provide two examples of deploying the proposed geometry-based power-control scheme.

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“…Sum-rate maximization in Gaussian cognitive MAC is studied and efficient power allocation with convex relaxation is then derived in [13]. These results were extended to the case of fading channels in [14], [15]. Utilizing the dual decomposition technique, the authors of [14] investigated the optimal power allocation and user selection in MAC and BC to maximize the ergodic sum capacity of the SS in a fading environment subject to different combinations of transmission power and interference threshold constraints.…”

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confidence: 99%

Ergodic Sum Capacity of Spectrum-Sharing Multiple Access with Collision Metric

Khoshkholgh

Mokari

Shin

2013

IEEE J. Select. Areas Commun.

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Abstract-This paper investigates the ergodic sum capacity of a spectrum-shared Multiple Access Channel (MAC). We assume that the secondary service (SS) only knows the channel distribution information (CDI) between its transmitters and the primary receivers. Availability of CDI results in collision incidences at the primary receivers because of conflicting levels of intolerable interference. We introduce the concept of collision probability constraint to manage the unexpected QoS degradation of primary service in the secondary resource allocation (RA). This RA problem is inherently difficult to solve and its objective function is not necessarily convex. Two well-known approaches, called Iterative Approach (IA) and Analytical Approach (AA), each with several cases/categories, are then used to find solutions. IA solves the problem iteratively by reconstructing convex optimization problems from the original (non-convex) one in a number of iterative loops until the collision probability constraints are satisfied. IA is shown to converge quickly to a suitable solution. Furthermore, by using a control parameter, the system designer can make a tradeoff between the speed of convergence and the ergodic sum capacity. AA, on the other hand, solves the RA problem by suggesting tractable versions of collision probability constraints. Unlike IA, AA does not require extra signaling between transmitters and the base station to tune parameters, thus facilitating the implementation of SS. Our indepth simulations have shown the proposed approach to yield lower spectral efficiency than IA.Index Terms-Collision probability, convex optimization, ergodic sum capacity, multiple access channel (MAC), spectrum sharing.

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Geometry-based optimal power control of fading multiple access channels for maximum sum-rate in cognitive radio networks

Cited by 20 publications

References 15 publications

Power Allocation for Energy-Harvesting-based Fading Cognitive Multiple Access Channels: with or without Successive Interference Cancellation

Power Allocation for Energy-Harvesting-based Fading Cognitive Multiple Access Channels: with or without Successive Interference Cancellation

Generalized geometry-based optimal power control in wireless networks

Ergodic Sum Capacity of Spectrum-Sharing Multiple Access with Collision Metric

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