Two-Channel Linear Phase FIR QMF Bank Minimax Design via Global Nonconvex Optimization Programming

Ho, Charlotte Yuk-Fan; Ling, Bo; Benmesbah, Lamia; Kok, T C-W; Siu, Wan-Chi; Teo, K. L.

doi:10.1109/tsp.2010.2049107

Cited by 43 publications

(13 citation statements)

References 14 publications

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“…The first approach is filled function approach. 33,34 Many constraints are applied to the optimization problem so that the required properties of the filled function are satisfied. The second approach is evolutionary algorithms, 31 which are based on natural evolution.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of fractional delay FIR filter using cuckoo search algorithm

Kumar

2018

Circuit Theory & Apps

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Section: Introductionmentioning

confidence: 99%

Optimal design of fractional delay FIR filter using cuckoo search algorithm

Kumar

2018

Circuit Theory & Apps

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“…Since the approximated objective function is differentiable, gradient descent based methods are then applied for finding a locally optimal solution of the approximated problem [1,2]. However, as the approximated problem is non-convex [11,12], there are many locally optimal solutions. It is still very difficult to guarantee to find its globally optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Efficient method for finding globally optimal solution of problem with weighted L _p norm and L ₂ norm objective function

Xie

Ling

et al. 2016

IET signal process.

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This study proposes an iterative method to approximate an N-dimensional optimisation problem with a weighted L p and L 2 norm objective function by a sequence of N independent one-dimensional optimisation problems. Inspired by the existing weighted L 1 and L 2 norm separable surrogate functional (SSF) iterative shrinkage algorithm, there are N independent one-dimensional optimisation problems with weighted L p and L 2 norm objective functions. However, these optimisation problems are non-convex. Hence, they may have more than one locally optimal solutions and it is very difficult to find their globally optimal solutions. This paper proposes to partition the feasible set of each approximated problem into various regions such that the sign of the convexity of the objective function in each region remains unchanged. Here, there is no more than one stationary point in each region. By finding the stationary point in each region, the globally optimal solution of each approximated optimisation problem can be found. Besides, this study also shows that the sequence of the globally optimal solutions of the approximated problems converge to the globally optimal solution of the original optimisation problem. Computer numerical simulation results show that the proposed method outperforms the existing weighted L 1 and L 2 norm SSF iterative shrinkage algorithm.

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“…An iterative power allocation scheme is developed in [3]. It employs theNash equilibrium (NE) theory for solving the non-convex optimization problem [4], [5]. Here, the objective function is to maximizethe capacity.…”

Section: Introductionmentioning

confidence: 99%

Problem formulation for joint cooperative downlink scheduling and power allocation for joint processing coordinated multipoint

Hooshmand

Aghvami

Ling³

2014

2014 IEEE International Conference on Consumer Electronics - China

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The true potential of the orthogonal frequency divisional multiple access (OFDMA)coordinated multipoint (CoMP) is in turning the adverse effects of the inter-cell interference (ICI) into the system performance gains. The small ultradense deployments of data-hungry users in the forthcoming generations of wireless mobile access systems, namely the long term evolution (LTE) A as 4G and the subsequent 5G, will be pushing for the revolutionary innovations to accommodate the required peak rates and the reliability in the cell edge. CoMP has been identified as a competent solution to exploit the interference in the cell-edge. To this end, we consider the non-coherent joint processing (JP) mode of CoMP in the downlink (DL) of an LTE system. Here, we formulate the problem of the joint cooperative scheduling and transmit power allocationas a hybrid mixed integer non-convex optimization problem. In particular, the transmit power allocation is based on the allocation of the time-frequency resources according to a proportionally fair (PF) utility function with certain CoMPspecific constraints on the transmission.This problem formulation serves a twofold purpose of both the interference coordination under a full frequency reuse regime and the inherent interference mitigation capabilities.

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Two-Channel Linear Phase FIR QMF Bank Minimax Design via Global Nonconvex Optimization Programming

Cited by 43 publications

References 14 publications

Optimal design of fractional delay FIR filter using cuckoo search algorithm

Optimal design of fractional delay FIR filter using cuckoo search algorithm

Efficient method for finding globally optimal solution of problem with weighted L _p norm and L ₂ norm objective function

Problem formulation for joint cooperative downlink scheduling and power allocation for joint processing coordinated multipoint

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Two-Channel Linear Phase FIR QMF Bank Minimax Design via Global Nonconvex Optimization Programming

Cited by 43 publications

References 14 publications

Optimal design of fractional delay FIR filter using cuckoo search algorithm

Optimal design of fractional delay FIR filter using cuckoo search algorithm

Efficient method for finding globally optimal solution of problem with weighted L p norm and L 2 norm objective function

Problem formulation for joint cooperative downlink scheduling and power allocation for joint processing coordinated multipoint

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Efficient method for finding globally optimal solution of problem with weighted L _p norm and L ₂ norm objective function