دانلود رایگان مقاله برآورد کانال رتبه معیوب موج میلیمتری بر اساس سنجش فضایی-فشرده

Abstract

Millimeter-wave communication (mmWC) is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue the channel propagation characteristics in this band, high dimensional antenna arrays need to be deployed at both the base station (BS) and mobile sets (MS). Unlike the conventional MIMO systems, Millimeter-wave (mmW) systems lay away to employ the power predatory equipment such as ADC or RF chain in each branch of MIMO system because of hardware constraints. Such systems leverage to the hybrid precoding (combining) architecture for downlink deployment. Because there is a large array at the transceiver, it is impossible to estimate the channel by conventional methods. This paper develops a new algorithm to estimate the mmW channel by exploiting the sparse nature of the channel. The main contribution is the representation of a sparse channel model and the exploitation of a modified approach based on Multiple Measurement Vector (MMV) greedy sparse framework and subspace method of Multiple Signal Classification (MUSIC) which work together to recover the indices of non-zero elements of an unknown channel matrix when the rank of the channel matrix is defected. In practical rank-defective channels, MUSIC fails, and we need to propose new extended MUSIC approaches based on subspace enhancement to compensate the limitation of MUSIC. Simulation results indicate that our proposed extended MUSIC algorithms will have proper performances and moderate computational speeds, and that they are even able to work in channels with an unknown sparsity level.

6. Conclusion and future works

In this paper, we explored the potential of MUSIC-based and Rankaware algorithms in rank-defective or ill-conditioned mmW channel estimation while these approaches exploit the sparse nature of the channel with small training overhead. The hybrid architecture is composed of analog phase-shifters and digital base-band processor in the transceiver along with the large antenna array and RF chains very smaller than the length of the array, achieving near optimal spectral efficiencies even in rank-imperfect outdoor channels. We first enumerated the conventional MMV algorithms as extended SMV methods for full-rank channel, and then developed subspace enhancement approaches for channel with imperfect rank. Numerical results showed that the proposed rank-aware OMP offers near-optimal solution and achieves better spectral efficiency similar to the fully digital counterparts. We also provided a channel estimation method that can succeed in the unknown multipath (sparsity) and noisy measurement conditions. For future work, one can extend rank-defective mmW channel estimation based on Bayesian enhancement approaches, for example mentioned in [35]. It would also be interesting to extract the hybrid precoding/combining of rank-defective multi-user mmW according to some studies such as [36]. Furthermore, it would be arousing to consider the interference-cancellation problem in Multi-User frequency- selective mmW networks.