Artificial Bee Colony and Tabu Search Enhanced TTCM Assisted MMSE Multi-User Detectors for Rank Deficient SDMA-OFDM System

Haris, P. A.; Gopinathan, E.; Ali, C. K.

doi:10.1007/s11277-011-0264-0

Cited by 15 publications

(8 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MATLAB inbuilt commands ('poly2trellis', and 'convenc' with [133 171] as generator polynomial) were used for half-rate error control coding. In their papers, Jiang and Hanzo (2007), and Haris et al (2010Haris et al ( , 2012 use turbo trellis coded modulation (TTCM) as FEC which improves the performance but with a overhead larger compared to simple trellis coding used in this paper. Input data was scrambled before convolutional encoding and interleaved after encoding.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The higher the number of users to be supported, optimisation task becomes more challenging, due to the exponentially increased number of dimensions to be estimated. Genetic algorithm (GA) (Haupt and Haupt, 2004), particle swarm optimisation (PSO) (Kennedy and Eberhart, 1995), artificial bee colony (ABC) and tabu search (TS) algorithms perform better in this context (Jiang and Hanzo, 2007;Haris et al, 2010Haris et al, , 2012. They have found numerous applications in wireless communication recently.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Firefly algorithm assisted multi-user detection for OFDM/SDMA uplink system

Shahnaz

Ali

2015

IJRIS

View full text Add to dashboard Cite

Orthogonal frequency division multiplexing/space division multiple access (OFDM/SDMA), as a technique proposed for future high data-rate requirements, is capable of achieving higher user capacity. The error free detection of the transmitted data of the users poses many challenging issues. Alternatives are explored for optimum maximum likelihood (ML) aided detection due to its exponential complexity with the number of users and constellation size. Conventional detection techniques using zero forcing (ZF) and minimum mean square error (MMSE) are less complex with much poor performance. The possibility of nature-inspired meta-heuristic algorithms to detect the user data have been of great research interest these days. With these optimisation algorithms, we can obtain almost optimum results with much reduced computational complexity. In this paper, recently developed firefly algorithm (FA) has been utilised to detect the users' transmitted data and the result has been compared with genetic algorithm (GA) and particle swarm optimisation (PSO) aided data detection.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Firefly algorithm assisted multi-user detection for OFDM/SDMA uplink system

Shahnaz

Ali

2015

IJRIS

View full text Add to dashboard Cite

show abstract

“…Thus, the employed and onlooker bees usually perform the exploitation whereas the scouts perform the exploration of the search space. This process of foraging can be viewed as a complex problem broken down into many parts and the ultimate task is to find a viable solution since there are many ways in reaching the goal [9], [18], [23]. Let us examine figure 1 as illustrated by Karaboga [12], for a better understanding of this foraging behaviour.…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

“…Here, we have the input sequence denoted by x = (x1, x2, ..., xt), the hidden layer denoted by h = (h1, h2, ..., ht) and the output vector sequence denoted by y = (y1, y2, ..., yt). Usually the RNN calculates the hidden vector sequence h using equation (8) and the output vector sequence y using equation (9) with t = 1 to T [20];…”

Section: Rnn Training Using Abc Algorithmmentioning

confidence: 99%

Recurrent Neural Network Training using ABC Algorithm For Traffic Volume Prediction

Bosire

2019

IJCAI

View full text Add to dashboard Cite

This study evaluates the use of the Artificial Bee Colony (ABC) algorithm to optimize the Recurrent Neural Network (RNN) that is used to analyze traffic volume. Related studies have shown that Deep Neural Networks are superseding the Shallow Neural Networks especially in terms of performance. Here we show that using the ABC algorithm in training the Recurrent Neural Network yields better results, compared to several other algorithms that are based on statistical or heuristic techniques that were preferred in earlier studies. The ABC algorithm is an example of swarm intelligence algorithms which are inspired by nature. Therefore, this study evaluates the performance of the RNN trained using the ABC algorithm for the purpose of forecasting. The performance metric used in this study is the Mean Squared Error (MSE) and ultimately, the outcome of the study may be generalized and extended to suit other domains.Povzetek: Ocena uspešnosti algoritma umetne kolonije čebelje pri optimizaciji ponavljajoče se nevronske mreže.

show abstract

“…In contrast, classic MUD schemes, such as Minimum Mean Square Error (MMSE) and Order Successive Interference Cancellation (OSIC) have low performance but have the advantage of low complexity. As a result, some researchers have devoted their energy to developing suboptimum techniques and algorithms that can reach near-optimal performance with reduced computation complexity [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of Multi-User Detection Genetic Algorithm Tool for SDMA-OFDM Systems

Alansi

Elshafiey

Alsanie

et al. 2015

Wireless Pers Commun

View full text Add to dashboard Cite

Robust multi-user detection (MUD) methods based on space division multiple access (SDMA) techniques are essential to efficiently exploit the electromagnetic spectrum. In this paper, an adaptive Genetic Algorithm-based tool for SDMA-OFDM Systems (GASOS) is developed to improve the performance and computational complexity in cases of fully-loaded and overloaded multi-user scenarios. The data flow in GASOS is appropriate in pipelining and parallelization to reduce operational time. A new GASOS-based MUD hardware design for SDMA-OFDM systems is proposed using FPGA architecture. The design details are presented together with their planned operational modules. Resource utilization is optimized, and the total number of clock cycles required is found to be 15 initially, in addition to one clock cycle per member of algorithm population. A clock frequency of 100 MHz is used and implementation is carried out on Xilinx Ò Virtex-6 FPGA, built in the development platform ML605 edition with JTAG Hardware Co-simulation. According to the results obtained from the developed algorithm and implementation tools, a high number of users can be physically possible and provided with support. Real-time based implementation of MUD systems has the potential to play a major role in next-generation communication systems.

show abstract

Artificial Bee Colony and Tabu Search Enhanced TTCM Assisted MMSE Multi-User Detectors for Rank Deficient SDMA-OFDM System

Cited by 15 publications

References 21 publications

Firefly algorithm assisted multi-user detection for OFDM/SDMA uplink system

Firefly algorithm assisted multi-user detection for OFDM/SDMA uplink system

Recurrent Neural Network Training using ABC Algorithm For Traffic Volume Prediction

FPGA Implementation of Multi-User Detection Genetic Algorithm Tool for SDMA-OFDM Systems

Contact Info

Product

Resources

About