Name
Abstract | ||
|---|---|---|
To estimate multiple-input multiple-output (MIMO) channels, invariable step-size normalized least mean square (ISSNLMS) algorithm was applied to adaptive channel estimation (ACE). Since the MIMO channel is often described by sparse channel model due to broadband signal transmission, such sparsity can be exploited by adaptive sparse channel estimation (ASCE) methods using sparse ISS-NLMS algorithms. It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance and computational cost. The previous approaches can exploit channel sparsity but their step-sizes are keeping invariant which unable balances well the three aspects and easily cause either estimation performance loss or instability. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE for estimating MIMO channels is formulated in MIMO systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISSNLMS algorithms and sparse VSS-NLMS ones are explained. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICCS.2014.7024874 | ICCS |
Keywords | DocType | Volume |
broadband signal transmission,bit error rate,sparse channel model,asce method,issnlms algorithm,algorithm stability,computational cost,mean square error,sparse variable step-size nlms algorithm,sparse iss-nlms algorithm,ace,estimation performance,adaptive channel estimation,least mean squares methods,ber metric,mse,mimo communication,multiple-input multiple-output channel,invariable step-size normalized least mean square algorithm,error statistics,adaptive sparse channel estimation method,vss-nlms algorithm,channel estimation,mean square error methods,adaptive mimo channel estimation,vectors,signal to noise ratio,estimation,mimo | Journal | abs/1407.6081 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guan Gui | 1 | 641 | 102.53 |
| Li Xu | 2 | 52 | 11.98 |
| Lin Shan | 3 | 3 | 0.74 |
| Fumiyuki Adachi | 4 | 1588 | 195.77 |