In this paper, a general recursive least square (GRLS) detection algorithm is proposed for the uplink of distributed massive multiple-input multiple-output (mimo) to alleviate the bottlenecks in both computational com...
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In this paper, a general recursive least square (GRLS) detection algorithm is proposed for the uplink of distributed massive multiple-input multiple-output (mimo) to alleviate the bottlenecks in both computational complexity and data bandwidth for interconnection. Different from the existing recursive least square (RLS) detection algorithm which only supports a single antenna in each distributed unit (DU), the proposed GRLS allows for multiple antennas in each DU, rendering it adaptable to a variety of practical scenarios. Moreover, among the total C DUs and with an integer parameter C-0, the computational complexity of C-C-0 DUs in GRLS can be significantly reduced by leveraging the channel hardening property. Through analysis, we demonstrate that the convergence of the GRLS algorithm is guaranteed if C-0 >= [ ( root B / 2 + root K )(2) / B ] holds, where K and B denote the numbers of antennas at the user side and each DU, respectively. Furthermore, based on the daisy-chain architecture, the proposed GRLS algorithm also enjoys excellent scalability, which can be easily extended with extra DUs for further improvement. Finally, the detection complexity and data bandwidth analysis are also provided to unveil the superiority of GRLS compared to other distributeddetection schemes for massive mimo.
In this paper, we propose the decentralized likelihood ascent search (DLAS)-aided detection for the distributed large-scale multiple-input multiple-output (mimo) systems to achieve more remarkable performance gains. W...
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In this paper, we propose the decentralized likelihood ascent search (DLAS)-aided detection for the distributed large-scale multiple-input multiple-output (mimo) systems to achieve more remarkable performance gains. With the help of DLAS, traditional distributed iterative methods are able to achieve better performance than the linear detection schemes such as ZF and MMSE. According to analysis, we derive the equivalent noise and the post-processing SNR for DLAS. More importantly, based on them, we demonstrate that the proposed DLAS-aided detection achieves the full received diversity. To further facilitate its implementation in practice, we design the decentralized effective ring (DER) architecture with significantly reduced bandwidth requirement and better parallel computation. Finally, simulation results demonstrate that the proposed DLAS-aided detection attains the same received diversity as ML detection while surpassing state-of-the-art decentralized schemes in terms of BER performance, with reduced complexity and bandwidth costs.
A joint processing framework is proposed for a distributed-phased multiple-input-multiple-output (phased-mimo) sonar for detection of a small target. A transmit-receive-interactive strategy is combined with the array ...
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A joint processing framework is proposed for a distributed-phased multiple-input-multiple-output (phased-mimo) sonar for detection of a small target. A transmit-receive-interactive strategy is combined with the array processing for a high signal-to-noise ratio (SNR) required by the distributed mimo detection. The former provides the approximate location of a likely target by transmitting a probe signal to sense the environment. Then, the transmitting beams are steered to illuminate the target from different angles by which target echoes are enhanced and reverberation is suppressed. It is seen from the receiver operating characteristic curves that the distributed-phased-mimo sonar is intermediate to the distributedmimo sonar and the phased-array sonar in the low-SNR scenario. In the at-lake experiments of localisation of a small target, the distributed-phased-mimo sonar system performs better than other two sonar systems due to exploiting the diversity gain and the transmitting array gain.
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