9 research outputs found
ΠΠ΅ΠΊΠΎΡΠΎΡΡΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΡΠ΅ΠΊΡΠΎΠ½ΠΈΠΊΠΈ ΡΠ³ΠΎΠ»ΡΠ½ΡΡ ΠΌΠ΅ΡΡΠΎΡΠΎΠΆΠ΄Π΅Π½ΠΈΠΉ Π² ΠΏΡΠ΅Π΄Π΅Π»Π°Ρ Π·Π°ΠΏΠ°Π΄Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ½ΠΎΠΊΠ»ΠΈΠ½Π°Π»Π° (Π’ΠΎΠΌΡ-Π£ΡΠΈΠ½ΡΠΊΠΈΠΉ ΡΠ°ΠΉΠΎΠ½ ΠΡΠ·Π±Π°ΡΡΠ°)
ΠΠ° ΠΎΡΠ½ΠΎΠ²Π΅ Π°Π½Π°Π»ΠΈΠ·Π° ΠΎΠ±ΡΠ΅Π³Π΅ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠΉ Π² ΠΈΠ·ΡΡΠ΅Π½ΠΈΠΈ Π΄Π΅ΡΠ°Π»ΡΠ½ΠΎΠΉ ΡΠ΅ΠΊΡΠΎΠ½ΠΈΠΊΠΈ ΠΠ°ΠΏΠ°Π΄Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ½ΠΎΠΊΠ»ΠΈΠ½Π°Π»Π° ΡΠΊΠ»Π°Π΄ΡΠ²Π°Π΅ΡΡΡ ΠΌΠ½Π΅Π½ΠΈΠ΅ ΠΎ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΌ Π²Π»ΠΈΡΠ½ΠΈΠΈ Π½Π° ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ΅ΠΊΡΠΎΠ½ΠΈΠΊΠΈ Π’ΠΎΠΌΡ-Π£ΡΠΈΠ½ΡΠΊΠΎΠ³ΠΎ ΡΠ°ΠΉΠΎΠ½Π° Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΠΉ ΡΠΎ ΡΡΠΎΡΠΎΠ½Ρ ΠΡΠ·Π½Π΅ΡΠΊΠΎΠ³ΠΎ ΠΠ»Π°ΡΠ°Ρ. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΡΠ°ΠΊΠΆΠ΅ Π²ΠΎΠΏΡΠΎΡΡ ΡΠ³Π»Π΅Π½ΠΎΡΠ½ΠΎΡΡΠΈ ΡΠ°ΠΉΠΎΠ½Π° ΠΈ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠ΅ΠΉ ΡΠ°Π·Π²Π΅Π΄ΠΊΠΈ
Low-complexity DCD-based sparse recovery algorithms
Sparse recovery techniques find applications in many areas. Real-time implementation of such techniques has been recently an important area for research. In this paper, we propose computationally efficient techniques based on dichotomous coordinate descent (DCD) iterations for recovery of sparse complex-valued signals. We first consider optimization that can incorporate \emph{a priori} information on the solution in the form of a weight vector. We propose a DCD-based algorithm for optimization with a fixed -regularization, and then efficiently incorporate it in reweighting iterations using a \emph{warm start} at each iteration. We then exploit homotopy by sampling the regularization parameter and arrive at an algorithm that, in each homotopy iteration, performs the optimization on the current support with a fixed regularization parameter and then updates the support by adding/removing elements. We propose efficient rules for adding and removing the elements. The performance of the homotopy algorithm is further improved with the reweighting. We then propose an algorithm for optimization that exploits homotopy for the regularization; it alternates between the least-squares (LS) optimization on the support and the support update, for which we also propose an efficient rule. The algorithm complexity is reduced when DCD iterations with a \emph{warm start} are used for the LS optimization, and, as most of the DCD operations are additions and bit-shifts, it is especially suited to real time implementation. The proposed algorithms are investigated in channel estimation scenarios and compared with known sparse recovery techniques such as the matching pursuit (MP) and YALL1 algorithms. The numerical examples show that the proposed techniques achieve a mean-squared error smaller than that of the YALL1 algorithm and complexity comparable to that of the MP algorithm