針對基於壓縮感知訊號的無線網路適應性傳輸技術

在未來的無線傳輸系統中，壓縮感知是其中一項具潛力的關鍵技術之一來進一步提高頻帶使用率、傳輸速率或資料安全度等，根據壓縮感知的特殊性質，我們提出了一個優化的壓縮感知測量元子的位元長度，可以使裝置更有效的傳輸資料並且節省傳輸能量消耗。提出的演算法利用資料的特性以及利用最小化l_1解碼，其只需要少量的測量元子就可以還原重建原資料，並且不需要使用重傳機制。優化的測量元子位元長度可以使用比最低全精準位元長度更短的長度傳送，其會產生相對應的溢值機率，針對溢值不有效的測量元子，可以通過收取更多其他的測量元子來補足進行還原; 在傳送端透過調適所需的測量元子以及每個測量元子的位元長度將能使的傳輸更為有效率與靈活，並且最小化在空氣中的傳輸位元。除此之外，適應性無線傳輸也被應用在我們傳輸系統中，根據通道的訊雜比狀況以及錯誤率限制，採用合適的調變與通道編碼，以及拉長訊號週期當通道狀況不佳的時候。

關鍵字

壓縮感知；最佳化；無線網路；跨層級

並列摘要

We propose an optimal bit-length of measurements for devices that leverages the theory of compressive sensing (CS) to achieve high efficiency and save the energy of devices. Beyond the 4G communication, the CS is one of the potential techniques to further enhance sensor network performance. The proposed optimal measurements of CS exploit the sparse property and l_1-minimization decoding operation that only a relatively small number of measurements are required to be received and no retransmission is necessary. With the target probability of overflow, those optimal measurements are able to be transmitted in fewer bits than full-precision measurements, and they can be recovered by receiving more redundant measurements. This results in an efficient transmission method that decides the number of measurements and number of bits per measurement at transmission side to minimize the expected transmission bits in the air. Besides, the adaptive link is applied to the transmission that uses proper modulation, channel coding scheme (MCS) and repetition according to the signal-to-noise ratio (SNR) and the target outage rate.

並列關鍵字

compressive sensing ； optimization ； wireless networks ； cross-layer

參考文獻

[2] E. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal re-construction from highly incomplete frequency information,” IEEE Trans. Inform. Theory, vol. 52, no. 2, pp. 489–509, Feb. 2006.

[3] E. Candès and T. Tao, “Near optimal signal recovery from random projections: Uni-versal encoding strategies?,” IEEE Trans. Inform. Theory, vol. 52, no. 12, pp. 5406–5425, Dec. 2006.

[4] D. Donoho, “Compressed sensing,” IEEE Trans. Inform. Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.

[5] Ping-Heng Kuo, H. T. Kung, and Pang-AN Ting, “Compressive sensing based chan-nel feedback protocols for spatially-correlated massive antenna arrays,” in Proc., IEEE WCNC 2012, Apr. 2012.

[6] F. Fazel, M. Fazel, and M. Stojanovic, “Random access compressed sensing for ener-gy-efficient underwater sensor networks,” IEEE Journal On Selected Areas In Com-munications, vol. 29, pp. 1660–1670, 2011.

國際替代計量

針對基於壓縮感知訊號的無線網路適應性傳輸技術

全文下載

主題瀏覽