Computationally efficient optimal power allocation algorithm for multicarrier communication systems

We propose a continuous rate and power allocation algorithm for multiuser downlink multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with coordinated multipoint (CoMP) transmission that guarantees to satisfy individual rate target across all users. The optimization problem is formulated as a total transmit power minimization problem subject to per-user rate targets and per-antenna power constraints across multiple cooperating base stations. While the per-antenna power constraint leads to a more complex optimization problem, it is a practical consideration that limits the average transmit antenna power and helps to control the resulting high peak powers in OFDM. Our proposed algorithm uses successive convex approximation (SCA) to transform the non-convex power minimization problem and dynamically allocate power to co-channel user terminals. We prove that the transformed power minimization problem is convex and that our proposed SCA algorithm converges to a solution. The proposed algorithm is compared with two alternative approaches: (1) iterative waterfilling (IWF) and (2) zero-forcing beamforming (ZFB) with semi-orthogonal user selection. Simulation results highlight that the SCA algorithm outperforms IWF and ZFB in both medium-and low-interference environments.

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“…Slow fading margin (dB)channel-to-noise ratio (CNR) on the (n, l)th spatial subchannel for the kth UT, which is given by [10] …”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The resource allocation problem for downlink MIMO-OFDM systems has been studied extensively for the single-user case [9,10]. However, the optimization problem for multiuser MIMO-OFDM systems becomes mathematically challenging as the problem becomes nonconvex in the presence of interference.…”

Section: Introductionmentioning

confidence: 99%

Power minimization for cooperative MIMO-OFDM systems with individual user rate constraints

Hsu

Yeoh

Krongold

2016

J Wireless Com Network

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“…al. [55] [56] give another optimal algorithm using the Lagrangean techniques. Both these algorithms are optimal and have computational complexity of order N log 2 N .…”

Section: Water-filling Solution and Bit-loadingmentioning

confidence: 99%

A Video Transmission System for Severely Degraded Channels

Pearlman¹,

Saulnier²

2006

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“…Many papers show that adaptive modulation [4] and dynamic resource allocation [5] significantly increase throughput and allow more users to transmit simultaneously. A practical and optimal bit loading algorithm to maximize throughput and/or minimize power in a single-user OFDM system is studied in [6].…”

Section: Introductionmentioning

confidence: 99%

An Iterative Resource Allocation Algorithm for Multiuser OFDM with Fairness and QoS Constraints

Chen

Krongold

2007

2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring

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Abstract-This paper looks at the problem of total power minimization in multiuser OFDM systems while maintaining individual fairness (rate) and QoS (BER) requirements. A practical and efficient subchannel, power and bit allocation algorithms is described, where an average-SNR approximation is used to substantially reduce complexity. The proposed algorithm guarantees improvement through each iteration and converges quickly to a stable suboptimal solution. Numerical results and complexity analysis show that the proposed algorithm offers beneficial performance improvement compared to existing approaches.

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Computationally efficient optimal power allocation algorithm for multicarrier communication systems

Cited by 83 publications

References 6 publications

Power minimization for cooperative MIMO-OFDM systems with individual user rate constraints

Power minimization for cooperative MIMO-OFDM systems with individual user rate constraints

A Video Transmission System for Severely Degraded Channels

An Iterative Resource Allocation Algorithm for Multiuser OFDM with Fairness and QoS Constraints

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