2014
DOI: 10.1049/iet-com.2014.0104
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Resource allocation for multiple input multiple output‐orthogonal frequency division multiplexing ‐based space division multiple access systems

Abstract: The problem of the system throughput maximisation and the fairness issue among users is considered for the downlink multiuser multiple input multiple output-orthogonal frequency division multiplexing system with MMSE precoding. Since finding the optimal solution by using the exhaustive search has very high computational burden, the authors propose a lowcomplexity user selection strategy plus the optimal power allocation in this study. With resorting to the mathematical simplification process, the proposed user… Show more

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Cited by 11 publications
(4 citation statements)
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“…Traditionally, web data mining models in large data environment mainly adopt high order cumulant feature extraction, time-frequency analysis and feature extraction, wavelet analysis, support vector machine classification mining algorithm, and data mining algorithm based on rough set classification in large data environment; there are many drawbacks in the web data model, such as the inaccuracy of data, the lack of effectiveness [2], the error resulting in the rule pattern of the system coding, and the other most important thing is that in the process of mining the data [3], it is not possible to determine whether the system is safe or not, if the data is excavated into an unsafe system, not only the data are data, but the data are not the same in this case; in reference [4], a feature data mining algorithm is proposed based on the distributed feature partition extraction of mass web access time, and improved the web data by multi-layer autoregressive vector analysis. Classification mining ability, but the computation cost of the algorithm is large, and the time delay error occurs in web information retrieval.…”
Section: Introductionmentioning
confidence: 99%
“…Traditionally, web data mining models in large data environment mainly adopt high order cumulant feature extraction, time-frequency analysis and feature extraction, wavelet analysis, support vector machine classification mining algorithm, and data mining algorithm based on rough set classification in large data environment; there are many drawbacks in the web data model, such as the inaccuracy of data, the lack of effectiveness [2], the error resulting in the rule pattern of the system coding, and the other most important thing is that in the process of mining the data [3], it is not possible to determine whether the system is safe or not, if the data is excavated into an unsafe system, not only the data are data, but the data are not the same in this case; in reference [4], a feature data mining algorithm is proposed based on the distributed feature partition extraction of mass web access time, and improved the web data by multi-layer autoregressive vector analysis. Classification mining ability, but the computation cost of the algorithm is large, and the time delay error occurs in web information retrieval.…”
Section: Introductionmentioning
confidence: 99%
“…More and more enterprise managers realize the importance of information management, it is necessary for enterprises to obtain competitive advantage to set up enterprise management information system by using advanced information technology. It has great significance to study the optimal design method of mass information management system, so as to optimize information management and improve big data's ability of information scheduling [2] .…”
Section: Introductionmentioning
confidence: 99%
“…In most of existing literature on adaptive resource allocation (subcarrier, power, bit, antenna, etc.) in multiuser MIMO-OFDM systems, the proposed algorithms are mainly designed to maximize the data rate/capacity subject to a power constraint [5][6] [7] [8] or to minimize the overall transmit power subject to a rate constraint [9][10] [11] [12]. In [5], a low-complexity proportional rate-adaptive radio resource (subcarrier and power) allocation algorithm is proposed to maximize the sum-rate capacity of the system under a total power constraint.…”
Section: Introductionmentioning
confidence: 99%