Near-Optimal and Efficient Heuristic Algorithms for Resource Allocation in MISO-OFDM Systems

Perea-Vega, Diego; Frigon, Jean-François; Girard, André

doi:10.1109/icc.2010.5502029

Cited by 10 publications

(18 citation statements)

References 4 publications

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“…The rate or power constraints might be violated whenever the algorithm stops because the number of iterations has been reached before the convergence rule is met. In this subsection, we propose a simple procedure to obtain a feasible point to problem (9)(10)(11)(12)(13)(14)(15) from the dual solution found with Algorithm 1. This point is not optimal but because we start from the dual optimal solution, we expect that it will be close to the optimal solution.…”

Section: Dual-based Primal Feasible Methodsmentioning

confidence: 99%

“…Constraint (12) guarantees that we do not choose more than M users for each subcarrier, constraint (13) guarantees that if we have chosen two users k and j, they meet the ZF constraints and is redundant for other users, and constraint (14) guarantees that the beamforming vector is zero for users that are not chosen. Problem (9)(10)(11)(12)(13)(14) is a non-linear mixed integer program (MIP) and these are known to be very hard to solve exactly. In this paper, whenever we need to get an exact solution, we use a complete enumeration of the binary variables α satisfying the constraints (12).…”

Section: Zero-forcing Beamformingmentioning

confidence: 99%

“…Algorithm 1 finds the optimal dual variables (λ * , µ * ) that solve the dual problem (18) using the subgradient method [19] with a fixed step size δ and therefore yields an upper bound to problem (9)(10)(11)(12)(13)(14)(15). In algorithm 1, the pseudoinverse matrices G + k,n,s can be computed by any number of well known algorithms.…”

Section: Algorithm 1 Calculation Of the Dual Problemmentioning

confidence: 99%

See 2 more Smart Citations

Dual-based bounds for resource allocation in zero-forcing beamforming OFDMA-SDMA systems

Perea-Vega

Girard

Frigon

2013

J Wireless Com Network

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We consider multi-antenna base stations using orthogonal frequency-division multiple access and space division multiple access techniques to serve single-antenna users. Some users, called real-time users, have minimum rate requirements and must be served in the current time slot while others, called non real-time users, do not have strict timing constraints and are served on a best-effort basis. The resource allocation (RA) problem is to find the assignment of users to subcarriers and the transmit beamforming vectors that maximize the total user rates subject to power and minimum rate constraints. In general, this is a nonlinear and non-convex program and the zero-forcing technique used here makes it integer as well, exact optimal solutions cannot be computed in reasonable time for realistic cases. For this reason, we present a technique to compute both upper and lower bounds and show that these are quite close for some realistic cases. First, we formulate the dual problem whose optimum provides an upper bound to all feasible solutions. We then use a simple method to get a primal-feasible point starting from the dual optimal solution, which is a lower bound on the primal optimal solution. Numerical results for several cases show that the two bounds are close so that the dual method can be used to benchmark any heuristic used to solve this problem. As an example, we provide numerical results showing the performance gap of the well-known weight adjustment method and show that there is considerable room for improvement.

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Section: Dual-based Primal Feasible Methodsmentioning

confidence: 99%

Section: Zero-forcing Beamformingmentioning

confidence: 99%

Section: Algorithm 1 Calculation Of the Dual Problemmentioning

confidence: 99%

See 1 more Smart Citation

Dual-based bounds for resource allocation in zero-forcing beamforming OFDMA-SDMA systems

Perea-Vega

Girard

Frigon

2013

J Wireless Com Network

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“…This results to the adjacent channel interference and thus reduced the power of nth subcarrier for guaranteeing to be lower than Q. Hence, to account for such adverse effects but in a more simple form other than the optimization perspective, we define new or effective channel gain for the kth user at the nth subcarrier as S ′ k,n = g k,n g k,n+1 +g k,n−1 , which is then used for the subcarrier allocation similar to S. Like previous works, 41 it is expected that the results and achievable rates of such optimization-directed heuristic subcarrier allocation do not significantly differ from those of the actual proposed optimization scheme because of the reasonable selected metric (S ′ k,n ).…”

Section: Subcarrier Allocationmentioning

confidence: 99%

Uplink resource allocation in multiuser multicarrier cognitive radio networks under power amplifier nonlinearity

Baghani

Mohammadi

Majidi

et al. 2017

Trans. Emerging Tel. Tech.

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In this paper, the nonlinear distortion effects of power amplifiers are studied, in particular from uplink subcarrier and power allocation perspectives in multiuser multicarrier cognitive networks. The out‐of‐band emissions of a nonlinear power amplifier create interference to the other users. The target is then to maximize the achievable uplink rate in a multiuser multicarrier cognitive network where a cognitive user should not introduce interference to the other users more than a specified threshold level, called interference power constraint. This task is formulated as a convex optimization problem with interference power constraints. Obtaining a closed‐form solution for this problem is, however, not feasible due to its nonlinear nature. Accordingly, the problem is solved numerically, and extensive simulations are conducted to obtain performance results. The obtained results are also compared with a more simple heuristic approach. The results show that the proposed scheme provides a maximum rate while at the same time also guaranteeing that the interference levels are lower than the specified interference power constraint. The results also indicate that the maximum rate can be closely approximated by using the assisted or optimization‐directed heuristic approach.

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“…Intelligent algorithms have attracted more and more scholars' attention to solve optimisation problems (Mo et al, 2011;Gu et al, 2013). In the studies of OFDM resource allocation, a lot of reports make use of intelligent algorithm to solve the OFDM resource allocation model (Perea-Vega et al, 2010;Odhah et al, 2012;Wang et al, 2012b;Ahmed and Majumder, 2012). Sadeque et al (2011) and Annauth and Rughooputh (2011) use intelligent algorithm to allocate power to each sub-carrier.…”

Section: Introductionmentioning

confidence: 99%

A resource allocation evolutionary algorithm for OFDM system

Wang

Liu

Li³

et al. 2017

IJCSE

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Abstract:Resource allocation for orthogonal frequency division multiplexing Orthogonal Frequency Division Multiplexing (OFDM) system, as a core technology for the 4th generation mobile communication system, has shown significant importance in the improvement of system transmission rate. At present, the two-step algorithm is described as a main method to deal with resource allocation for OFDM system. As carrier allocation and power allocation are not independent of each other, the two-step algorithm may result in lower complexity but poor convergence. This paper proposes a mixed evolutionary algorithm to allocate sub-carrier and power at the same time. It combines the simulated annealing algorithm and evolutionary algorithm. This paper also improves the definition of fairness in OFDM system. It can achieves perfect fairness among users while maintaining the efficient capacity performance. Simulation results demonstrate that the proposed resource allocation algorithm can achieve perfect performance, and increase the convergence rate greatly meanwhile efficiently improving the system capacity.Keywords: OFDM; resource allocation; evolutionary algorithm; simulated annealing algorithm.Reference to this paper should be made as follows: Wang, Q., Liu, H-L., Li, J-c. and Li, Y. Q. Wang et al.This paper is a revised and expanded version of a paper entitled 'Resource allocation for OFDM system based on evolutionary algorithm' presented at International

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Near-Optimal and Efficient Heuristic Algorithms for Resource Allocation in MISO-OFDM Systems

Cited by 10 publications

References 4 publications

Dual-based bounds for resource allocation in zero-forcing beamforming OFDMA-SDMA systems

Dual-based bounds for resource allocation in zero-forcing beamforming OFDMA-SDMA systems

Uplink resource allocation in multiuser multicarrier cognitive radio networks under power amplifier nonlinearity

A resource allocation evolutionary algorithm for OFDM system

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