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Adaptive bit allocation scheme in multi-cell cooperative block diagonalization systems with delayed and limited feedback
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作者 GUI Xin KANG Gui-xia +2 位作者 Huang Dong-yan LIUjia ZHANG Ping 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2012年第5期9-15,25,共8页
In multi-cell cooperative multi-input multi-output (MIMO) systems, base station (BS) can exchange and utilize channel state information (CSI) of adjacent cell users to manage co-channel interference. Users quant... In multi-cell cooperative multi-input multi-output (MIMO) systems, base station (BS) can exchange and utilize channel state information (CSI) of adjacent cell users to manage co-channel interference. Users quantize the CSIs of desired channel and interference channels using finite-rate feedback links, then BS can generate cooperative block diagonalization (BD) precoding matrices using the obtained quantized CSI at transmitter to supress co-channel interference. In this paper, a novel adaptive bit allocation scheme is proposed to minimize the rate loss due to imperfect CSI. We derive the closed-form expression of rate loss caused by both channel delay and limited feedback. Based on the derived rate loss expression, the proposed scheme can adaptively allocate more bits to quantize the better channels with smaller delays and fewer bits to worse channels with larger delays. Simulation results show that the proposed scheme yields higher performance than other allocation schemes. 展开更多
关键词 block diagonalization multi-cell cooperative MIMO system adaptive bit allocation scheme channel delay and limited feedback
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Addressing the CQI feedback delay in 5G/6G networks via machine learning and evolutionary computing
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作者 Andson Balieiro Kelvin Dias Paulo Guarda 《Intelligent and Converged Networks》 EI 2022年第3期271-281,共11页
5G networks apply adaptive modulation and coding according to the channel condition reported by the user in order to keep the mobile communication quality.However,the delay incurred by the feedback may make the channe... 5G networks apply adaptive modulation and coding according to the channel condition reported by the user in order to keep the mobile communication quality.However,the delay incurred by the feedback may make the channel quality indicator(CQI)obsolete.This paper addresses this issue by proposing two approaches,one based on machine learning and another on evolutionary computing,which considers the user context and signal-to-interference-plus-noise ratio(SINR)besides the delay length to estimate the updated SINR to be mapped into a CQI value.Our proposals are designed to run at the user equipment(UE)side,neither requiring any change in the signalling between the base station(gNB)and UE nor overloading the gNB.They are evaluated in terms of mean squared error by adopting 5G network simulation data and the results show their high accuracy and feasibility to be employed in 5G/6G systems. 展开更多
关键词 channel quality indicator(CQI)feedback delay 5G/6G networks machine learning evolutionary computing
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