Name
Abstract | ||
|---|---|---|
Channel state information at transmitter (CSIT) is vital for massive multiple-input-multiple-output (MIMO) system to achieve unprecedented improvement in capacity and spectral efficiency. Unfortunately, CSIT is acquired via channel feedback in frequency-division duplexing (FDD) system, which suffers from large volume of feedback overhead and size of codebook, since they increase with the number of antennas at transmitter. In this paper, by exploiting the channel sparsity in virtual angular domain, a sparsity-aided codebook is proposed for FDD massive MIMO system to alleviate the channel feedback overhead and codebook size. Specifically, a sparsity aided limited channel feedback model is proposed, where the high dimensional channel is decomposed to the limited linear combination with channel coefficients in virtual angular domain. Then, the high dimensional channel can be quantized by the proposed codebook in a more precise way with significantly small codebook size and fed back in low overhead within virtual coherence time. Furthermore, the upper bound of inter-user interference and the per user rate loss incurred by the proposed sparsity-aided codebook are theoretically analyzed, in which the required quantization bits only increase linearly with the channel sparsity level rather than number of BS antennas when the rate loss is limited to a constant level. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.phycom.2018.07.012 | Physical Communication |
Keywords | Field | DocType |
Massive MIMO,Frequency division duplexing,Sparse channel,Limited feedback,Codebook design | Transmitter,Computer science,Algorithm,Communication channel,MIMO,Real-time computing,Spectral efficiency,Quantization (signal processing),Channel state information,Duplex (telecommunications),Codebook | Journal |
Volume | ISSN | Citations |
30 | 1874-4907 | 0 |
PageRank | References | Authors |
0.34 | 19 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruoyu Zhang | 1 | 6 | 2.79 |
| Jia Yan Zhang | 2 | 10 | 5.24 |
| H. L. Zhao | 3 | 9 | 4.87 |
| Yulong Gao | 4 | 0 | 3.72 |