A Novel Partial Joint Processing Architecture for distributed Massive MIMO

Gunasekara, Supuni; Senanayake, Rajitha; Smith, Peter J.; Kuijper, Margreta

doi:10.1109/vtc2022-spring54318.2022.9860596

Cited by 2 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We explain these terms with a simple example in Section III. As such, this paper extends the results of our earlier conference version [20] and makes the following contributions:…”

Section: Introductionsupporting

confidence: 71%

“…We highlight the advantages of the proposed architecture by comparing the achievable rate of a user processed by our method against two traditional clustering architectures namely non-cooperation and static clustering. It is important to note that in the special case when perfect CSI is accessible at the CPU the results derived for ZF in this study converge to that of [20] in which only the ZF was considered. The remainder of the paper is organized as follows.…”

Section: Introductionsupporting

confidence: 52%

“…The first contribution of our paper, which is the design of a new user-centric clustering approach, is presented in this section. Our modified user-centric approach is based on a partial joint processing (PJP) technique to cluster BSs [20]. Let us explain the proposed method using a toy example.…”

Section: The Modified User-centric Approachmentioning

confidence: 99%

“…The three BSs in the PJP cluster are named BS i , BS j and BS k , 1 Although we have chosen three as the cluster cardinality, the approach can be readily extended to larger cluster sizes as needed. 2 In [20], only two BSs collaborated in a PJP cluster due to the 1D simulation setup in which the BSs were arranged in a row. 3 While we employ a Voronoi tessellated cell structure to explain the modified user-centric approach, we emphasize that this method relies solely on utilizing long-term power information.…”

Section: The Modified User-centric Approachmentioning

confidence: 99%

“…In our previous work [20] we have assumed that perfect CSI is available a priori wherever required. In this paper, the system is analyzed under imperfect CSI conditions where the channels are estimated utilizing the LMMSE estimator.…”

Section: Received Sinr Analysismentioning

confidence: 99%

See 4 more Smart Citations

Zeroing Unknown Terms: A Novel Clustering Architecture for Low Network Overhead in Distributed Massive MIMO

Gunasekara,

Senanayake,

Smith

et al. 2024

IEEE Open J. Commun. Soc.

Self Cite

View full text Add to dashboard Cite

We propose a novel user-centric clustering architecture for distributed massive multiple-inputmultiple-output networks. We examine the uplink of a general multi-cell scenario in which a cluster of base stations (BSs) with large antenna arrays detect the signals of multiple users simultaneously. As opposed to traditional clustering schemes, where the channel coefficients of all the users within the cooperating cluster are learned at the BSs, in the proposed approach, BSs in the network learn the channels of only the strongest users based on the received signal-to-noise ratio. We consider two linear receivers, namely, zero forcing and linear minimum mean squared error receivers at the central processing unit. The two receivers operate with only partial knowledge of the user channels and the unknown terms are set to zero. We conduct a thorough analytical investigation and derive novel approximations for the instantaneous received signal-to-interference-and-noise ratio of an arbitrary user processed by a cluster of BSs. Furthermore, we develop simple closed-form expressions for the achievable rate and symbol error probability of an arbitrary user processed by a cluster of BSs. Numerical examples are used to illustrate the accuracy of the proposed approach and also to compare it to existing approaches. We show that our proposed architecture outperforms traditional clustering methods, particularly for cluster edge users.

show abstract

“…We explain these terms with a simple example in Section III. As such, this paper extends the results of our earlier conference version [20] and makes the following contributions:…”

Section: Introductionsupporting

confidence: 71%

Section: Introductionsupporting

confidence: 52%

Section: The Modified User-centric Approachmentioning

confidence: 99%