Compressed Sensing Implementations For Sparse Channel Estimation In OFDM Systems

The ever-increasing demand for high-data-rate communication over a wireless multipath fading channel usually necessitates that at the receiver, prior knowledge about the channel is known. This is often achieved using knowledge of current Channel State Information (CSI) to produce at the receiver channel impulse response reconstruction obtained from the received signals. For coherent detection based OFDM system, CE is critical for the receiver design as accurate CSI can remarkably improve performance. However, such information is seldom available a priori and needs to beestimated. CS uses the prior knowledge that many physical signals are sparse and acquire them with few measurements. Therefore, the main challenge in CS-based CE in OFDM system is two-fold: firstly, the design of proper measurements matrix, exploiting signal sparsity structure over certain transform basis. Secondly, based on prior knowledge of the measurement vector and measurement matrix, to accurately find the support of the unknown signal-vector from very few noisy measurements. The optimization of pilot symbols values and their placement as a disjoint optimization problem may not necessarily exhibit low coherence compressed CE. Hence, a joint pilot symbol and placement scheme is proposed that optimizes over both the pilot symbol values and their placements as a single design optimization problem. Simulation results demonstrate that the proposed scheme is effective and offer a better CE performance compared to other schemes, and can realize 18.75% improvement in bandwidth efficiency with the same CE performance compared to the Least Squares (LS) CE. Fusing different reconstruction algorithms may result in the probability of fusing several incorrectly estimated indices over noisy channels. Hence, a new fusion framework namely, Collaborative Framework of Algorithms (CoFA) is proposed, to pursue accurate recovery of the sparse signals from few linear measurements. Additionally, for low latency applications an algorithm namely, Stage-determined Matching Pursuit (SdMP) is proposed to provide tractable and fast signal reconstruction. By using the restricted isometry property, the theoretical analysis of both CoFA and SdMP algorithms and the sufficient conditions for realizing an improved reconstruction performance were presented. Simulation results demonstrate that the proposed CoFA and SdMP algorithms for CE have around 11.1%, 18.3%, 28.9% and 42.8% and around 5.6%, 13.9%, 22.8% and 33.3% performance improvement at MSE value of 2 × 10−3 when compared to FACS, gOMP, OMP and ROMP algorithms, respectively. Additionally, at BER value of 2×10−3 the proposed CoFA and SdMP algorithms for CE have around 9%, 14%, 19.5% and 25% and around 5%, 10%, 14% and 22.5% performance improvement when compared to FACS, gOMP, OMP and ROMP algorithms, respectively