Statistical Delay QoS Driven Energy Efficiency and Effective Capacity Tradeoff for Uplink Multi-User Multi-Carrier Systems

In this paper, the total system effective capacity (EC) maximization problem for the uplink transmission, in a multi-user multi-carrier orthogonal frequency division multiple access system, is formulated as a combinatorial integer programming problem, subject to each user's link-layer energy ef...

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Veröffentlicht in:IEEE transactions on communications 2017-08, Vol.65 (8), p.3494-3508
Hauptverfasser: Wenjuan Yu, Musavian, Leila, Qiang Ni
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Musavian, Leila
Qiang Ni
description In this paper, the total system effective capacity (EC) maximization problem for the uplink transmission, in a multi-user multi-carrier orthogonal frequency division multiple access system, is formulated as a combinatorial integer programming problem, subject to each user's link-layer energy efficiency (EE) requirement as well as the individual's average transmission power limit. To solve this challenging problem, we first decouple it into a frequency provisioning problem and an independent multi-carrier link-layer EE-EC tradeoff problem for each user. In order to obtain the subcarrier assignment solution, a low-complexity heuristic algorithm is proposed, which not only offers close-to-optimal solutions, while serving as many users as possible, but also has a complexity linearly relating to the size of the problem. After obtaining the subcarrier assignment matrix, the multi-carrier link-layer EE-EC tradeoff problem for each user is formulated and solved by using Karush-Kuhn-Tucker conditions. The per-user optimal power allocation strategy, which is across both frequency and time domains, is then derived. Further, we theoretically investigate the impact of the circuit power and the EE requirement factor on each user's EE level and optimal average power value. The low-complexity heuristic algorithm is then simulated to compare with the traditional exhaustive algorithm and a fair-exhaustive algorithm. Simulation results confirm our proofs and design intentions, and further show the effects of delay quality-of-service exponent, the total number of users, and the number of subcarriers on the system tradeoff performance.
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To solve this challenging problem, we first decouple it into a frequency provisioning problem and an independent multi-carrier link-layer EE-EC tradeoff problem for each user. In order to obtain the subcarrier assignment solution, a low-complexity heuristic algorithm is proposed, which not only offers close-to-optimal solutions, while serving as many users as possible, but also has a complexity linearly relating to the size of the problem. After obtaining the subcarrier assignment matrix, the multi-carrier link-layer EE-EC tradeoff problem for each user is formulated and solved by using Karush-Kuhn-Tucker conditions. The per-user optimal power allocation strategy, which is across both frequency and time domains, is then derived. Further, we theoretically investigate the impact of the circuit power and the EE requirement factor on each user's EE level and optimal average power value. The low-complexity heuristic algorithm is then simulated to compare with the traditional exhaustive algorithm and a fair-exhaustive algorithm. 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To solve this challenging problem, we first decouple it into a frequency provisioning problem and an independent multi-carrier link-layer EE-EC tradeoff problem for each user. In order to obtain the subcarrier assignment solution, a low-complexity heuristic algorithm is proposed, which not only offers close-to-optimal solutions, while serving as many users as possible, but also has a complexity linearly relating to the size of the problem. After obtaining the subcarrier assignment matrix, the multi-carrier link-layer EE-EC tradeoff problem for each user is formulated and solved by using Karush-Kuhn-Tucker conditions. The per-user optimal power allocation strategy, which is across both frequency and time domains, is then derived. Further, we theoretically investigate the impact of the circuit power and the EE requirement factor on each user's EE level and optimal average power value. The low-complexity heuristic algorithm is then simulated to compare with the traditional exhaustive algorithm and a fair-exhaustive algorithm. 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subjects Algorithms
Combinatorial analysis
Complexity
Computer simulation
Delay
delay-outage probability
Delays
effective capacity
Energy efficiency
Energy management
Energy transmission
Frequency division multiple access
Heuristic algorithms
Heuristic methods
Integer programming
Kuhn-Tucker method
Link-layer energy-rate tradeoff
Multiple access
OFDM
Optimization
Power efficiency
Provisioning
Quality of service
Resource management
Subcarriers
System effectiveness
Tradeoffs
Uplink
title Statistical Delay QoS Driven Energy Efficiency and Effective Capacity Tradeoff for Uplink Multi-User Multi-Carrier Systems
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