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

Multi-user mmWave MIMO channel estimation with hybrid Beamforming over frequency selective fading channels

In multi-user millimeter wave (mmWave) multiple input multiple output (MIMO) systems, obtaining accurate information/knowledge regarding the channel state is crucial to achieving multi-user interference cancellation and reliable beamforming (BF)-to compensate for severe path loss. This knowledge is nonetheless very challenging to acquire in practice since large antenna arrays experience a low signal-to-noise ratio (SNR) before BF. In this paper, a multi-user channel estimation (CE) scheme namely generalized-block compressed sampling matching pursuit (G-BCoSaMP), is proposed for multi-user mmWave MIMO systems over frequency selective fading channels. This scheme exploits the cluster-structured sparsity in the angular and delay domain of mmWave channels determined by the actual spatial frequencies of each path. As the corresponding spatial frequencies of multi-user mmWave MIMO systems with Hybrid BF often fall between the discrete Fourier transform (DFT) bins due to the continuous Angle of Arrival (AoA)/Angle of Departure (AoD), the proposed G-BCoSaMP algorithm can address the resulting power leakage problem. Simulation results show that the proposed algorithm is effective and offer a better CE performance in terms of MSE when compared to the generalized block orthogonal matching pursuit (G-BOMP) algorithm that does not possess a pruning step

Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels

Doubly selective (DS) channel estimation for the downlink massive multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is a challenging problem, due to the requirements on high pilots overhead and prohibitive complexity. In this paper, by exploiting the highly correlated spatial structure of the obtained array response vectors and sparsity of the multipath signal components of the massive MIMO-OFDM channels, a modified spatial basis expansion model (modified-SBEM) is introduced. Thus, using complex exponential (CE-) modified-SBEM (i.e., modified CE-SBEM) can improve the resolution of the angles of departures (AoDs) information to represent the downlink with far fewer parameter dimensions, since the AoDs are much slower than path gains. Subsequently, we jointly design the effective pilot power and pilot placement for sparse channel estimation by means of an extended model. Our design is based on the block-coherence and sub-coherence simultaneous minimization of the measurement matrix associated with the massive MIMO-OFDM system pilot subcarriers. Furthermore, we leverage the sparse nature of the massive MIMO-OFDM system to formulate the quantized AoDs estimation into a block-sparse signal recovery problem, where the measurement matrix is designed based on the estimated virtual AoD. Thus, a new algorithm namely, generalized quasi-block simultaneous orthogonal matching pursuit (gQBSO), is introduced to solve the problem by providing sparse signal reconstruction solution. Simulation results demonstrate that the proposed scheme can effectively estimate the DS channel for massive MIMO-OFDM systems compared with other existing algorithms. For example, at SNR=20 dB for K=4 users, Doppler shift=0.093 with NT=32 antenna size, the adaptive-QBSO algorithm with G-SBEM and the proposed gQBSO with modified-SBEM can realize approximately 75.44%and 85.14% of the NMSE achieved by the oracle estimator with modified-SBEM

On the spectral-efficiency of low-complexity and resolution hybrid precoding and combining transceivers for mmWave MIMO systems

Millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems will almost certainly use hybrid precoding to realize beamforming with few numbers of RF chains to reduce energy consumption, but require low complexity technique to improve spectral efficiency. While energy-efficient hybrid analog/digital precoders and combiners designs can subdue the high pathloss inherent in mmWave channels, they assume the use of infinite- (or high-) resolution phase shifters to realize the analog precoder and combiner pair which results in high hardware cost and power consumption. One promising solution is to employ the use of low-resolution phase shifters. In this paper, we first diverse the exploration of multiple candidates of array response vectors, to propose low-complexity hybrid precoder and combiner (LcHPC) design via stage-determined matching pursuit (SdMP) namely, LcHPC-SdMP for pursuing better achievable rate for mmWave MIMO systems. We initially decouple the joint optimization over hybrid precoders and combiners into two separate sparse recovery problems. Specifically, LcHPC-SdMP algorithm revises the identification step of orthogonal matching pursuit (OMP) to the selection of multiple "correct" column indices of the matrix of array response vectors, per iteration. Then adds a pruning step-after satisfying a sparsity level condition, to iteratively refine the sparse solution which aids in further accelerating the algorithm, by requiring fewer iterations. We then propose an algorithm which iteratively designs low-resolution (two-bit) hybrid analog-digital precoder and combiner (LrHPC), for pursuing efficiency while maximizing spectral efficiency. Simulation results demonstrate that the proposed LcHPC-SdMP algorithm performs very close to its full-digital precoding and achieves better spectral efficiency over state-of-the-art algorithms with a substantially reduced number of iteration than the recently proposed schemes. In addition, simulation results also reveal that the achievable rate of the proposed LrHPC algorithm is higher than those of the existing algorithms under consideration