Distributed Resource Allocation for Multi-Cell Relay-Aided OFDMA Systems

Jeon, Wha Sook; Han, Jeong Ae; Jeong, Dong Geun

doi:10.1109/tmc.2013.86

Cited by 12 publications

(13 citation statements)

References 25 publications

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“…Since the Lagrange dual problem could be solved by the convex optimization theory, thus, the optimal power and subcarrier allocation scheme of problem (20) can be achieved. For the purpose of simplification, we assume the symbols that the BS transmitted over the i-th first-hop subcarrier are encoded by the relay and retransmitted over the i-th subcarrier of the second-hop to destination without considering subcarrier matching.…”

Section: Problemmentioning

confidence: 99%

“…(32) Combining the constraints c 1 , c 2 , c 6 , c 7 with equation (31) and (32), the optimal power allocation at BS and relay nodes can be rewritten as…”

Section: A Optimal Power Allocationmentioning

confidence: 99%

“…However, in practical communication system each node might have its own maximum transmit power. By contrast, the systems of [14][15][19][20] consider individual power constraints at each node. [19] proposed a semi-distributed resource allocation algorithm for multi-cell DF relay enhanced OFDM networks and used intermediate variable to overcome the gap of max-min capacity which could only get the lower limit of system capacity.…”

mentioning

confidence: 99%

“…[19] proposed a semi-distributed resource allocation algorithm for multi-cell DF relay enhanced OFDM networks and used intermediate variable to overcome the gap of max-min capacity which could only get the lower limit of system capacity. Resource allocation for multicarrier cooperation systems are investigated in [14][15]20], however, different rate constraints of users are not considered which yield to a bad experience for the cell-edged destinations. In this paper, we discuss the resource allocation with the individual power constraints at both base station and relay nodes of DF relay based OFDM networks and formulate resource allocation as a joint optimization problem including relay selection, subcarrier pairing, assignment and power allocation.…”

mentioning

confidence: 99%

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Joint Optimization Methods for Nonconvex Resource Allocation Problems of Decode-and-Forward Relay-Based OFDM Networks

Zhu

2016

IEEE Trans. Veh. Technol.

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Generally, resource allocation for multi-carrier cooperation communication networks include subcarrier and power allocation, however, it is difficult to be solved because of the 0-1 integer programming of subcarrier allocation, which makes the problem non-convex. This paper focus on solving the non-convex problems and provides a general solution to the resource allocation for relay enhanced multi-carrier systems. An established scenario, decode-and-forward (DF) relay assisted orthogonal frequency division multiplex (OFDM) system, is considered and we formulate the resource allocation as a joint subcarrier pairing, assignment and power allocation problem, in which heterogeneous users data rate requirements are also considered. In order to make the expression of the proposed optimization approaches for the original maximization problem clear, a four-step methodology is given. Firstly, we transform the original non-convex problem into a standard convex problem by imposing a new constraint of subcarrier allocation index regardless of the convexity of the objective function. Further, we prove that the optimum resource allocation algorithm (ORAA) for the dual problem of the transformed optimization problem is equivalent to the optimization of the original function. Subsequently, the optimum solution could be obtained by golden section search method (GSSM) and iterative resource allocation algorithm. Lastly, a sub-optimal resource allocation algorithm (SRAA) that solves the primal problem in an asymptotically manner is proposed. Simulation results illustrate that our proposed SRAA achieves a comparable performance as the ORAA with a satisfied complexity.

show abstract

Section: Problemmentioning

confidence: 99%

“…(32) Combining the constraints c 1 , c 2 , c 6 , c 7 with equation (31) and (32), the optimal power allocation at BS and relay nodes can be rewritten as…”

Section: A Optimal Power Allocationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Joint Optimization Methods for Nonconvex Resource Allocation Problems of Decode-and-Forward Relay-Based OFDM Networks

Zhu

2016

IEEE Trans. Veh. Technol.

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“…The issue of resource allocation, which focuses on assigning subcarriers and system power to users, has been an important concern in OFDMA systems [13,14,15,16,17]. Two classes of resource allocation schemes have been proposed according to different optimization objectives, called margin adaptive (MA) [18] and rate adaptive (RA), respectively [19,20].…”

Section: Introductionmentioning

confidence: 99%

Rate Adaptive Based Resource Allocation with Proportional Fairness Constraints in OFDMA Systems

Yin

Zhuang

et al. 2015

Sensors

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Orthogonal frequency division multiple access (OFDMA), which is widely used in the wireless sensor networks, allows different users to obtain different subcarriers according to their subchannel gains. Therefore, how to assign subcarriers and power to different users to achieve a high system sum rate is an important research area in OFDMA systems. In this paper, the focus of study is on the rate adaptive (RA) based resource allocation with proportional fairness constraints. Since the resource allocation is a NP-hard and non-convex optimization problem, a new efficient resource allocation algorithm ACO-SPA is proposed, which combines ant colony optimization (ACO) and suboptimal power allocation (SPA). To reduce the computational complexity, the optimization problem of resource allocation in OFDMA systems is separated into two steps. For the first one, the ant colony optimization algorithm is performed to solve the subcarrier allocation. Then, the suboptimal power allocation algorithm is developed with strict proportional fairness, and the algorithm is based on the principle that the sums of power and the reciprocal of channel-to-noise ratio for each user in different subchannels are equal. To support it, plenty of simulation results are presented. In contrast with root-finding and linear methods, the proposed method provides better performance in solving the proportional resource allocation problem in OFDMA systems.

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3‐D quasi proportional fairness scheduling in relay‐assisted multicell LTE‐A downlink

Ahmed

2016

Trans. Emerging Tel. Tech.

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This paper presents a three‐dimensional (3‐D: space‐time‐frequency) quasi proportional fairness (QPF)‐based radio resource allocation and interference coordination scheme in multicell long‐term evolution‐advanced downlink. We formulate the resource allocation and interference coordination problem as a QPF‐based utility maximisation problem, wherein, QPF reduces the complexity compared with the conventional proportional fairness. Interference has been minimised through time, frequency and space domain resource allocation. The proposed 3‐D scheme utilises two time slots for intercell interference management, spatial position‐based frequency resource allocation for intracell interference mitigation, and frequency domain orthogonality across user equipment (UE) and relays for inter‐UE and UE‐relays interference management. It has been shown that the relaxed non‐convex primal problem and the dual problem have zero duality gap under the time‐sharing and tolerable interference constraints. The relaxed optimal problem has been solved through the Lagrange dual method. Simulation results show the efficacy of the proposed 3‐D scheme compared with the recently presented three slots time domain interference coordination with round robin and proportional fairness time domain interference coordination schemes. It has been observed that the proposed heuristic 3D with equal power scheme exhibits same performance as the heuristic 3D with water‐filling power allocation and retains the desired optimal level for more than 60% of the average users' throughput. Copyright © 2016 John Wiley & Sons, Ltd.

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Distributed Resource Allocation for Multi-Cell Relay-Aided OFDMA Systems

Cited by 12 publications

References 25 publications

Joint Optimization Methods for Nonconvex Resource Allocation Problems of Decode-and-Forward Relay-Based OFDM Networks

Joint Optimization Methods for Nonconvex Resource Allocation Problems of Decode-and-Forward Relay-Based OFDM Networks

Rate Adaptive Based Resource Allocation with Proportional Fairness Constraints in OFDMA Systems

3‐D quasi proportional fairness scheduling in relay‐assisted multicell LTE‐A downlink

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