Abstract
Massive Multiple-Input Multiple-Output (MIMO) is envisioned to be a strong candidate technology for the upcoming 5th generation (5G) of wireless communication networks. This research work presents a novel Compressed Sensing (CS) and Superimposed Training (SiT) based technique for estimating the sparse uplink channels in massive MIMO systems. The proposed technique involves arithmetic addition of a periodic, but low powered training sequence with each user’s information sequence. Consequently, separately dedicated resources for the pilot symbols are not needed. Moreover, to attain the estimates of the Channel State Information (CSI) in the uplink, the sparsity exhibited by the MIMO channels is exploited by incorporating CS based Orthogonal Matching Pursuit (OMP) algorithm. For decoding the transmitted information symbols of each user, a Linear Minimum Mean Square Error (LMMSE) based equalizer is incorporated at the receiving Base Station (BS). Based on the obtained simulation results, the proposed SiT-OMP technique outperforms the existing Least Squares (SiT) channel estimation technique. The comparison is done using performance metrics of the Bit Error Rate (BER) and the Normalized Channel Mean Square Error (NCMSE).
Highlights
The exponential growth in the smart phones usage and data hungry applications has led to an unprecedented increase in the data traffic
The results demonstrate that superimposed pilot-based arrangement improves performance and has the capability for pilot decontamination in cellular Multiple-Input Multiple-Output (MIMO) systems [17]
Massive MIMO systems are foreseen to play a significant part in the emerging cellular communication networks because of their impressive gains in terms of order of magnitudes increase in data rates, and energy and spectral efficiency compared to the present-day networks
Summary
The exponential growth in the smart phones usage and data hungry applications has led to an unprecedented increase in the data traffic. The existing research papers discuss many channel estimation techniques based on training, blind, and semiblind methods to estimate CSI of MIMO systems [10]– [12]. Channel estimation techniques based upon Superimposed Training (SiT) have attracted a notable attention of the research community because of their noticeable advantages over the counterparts [13]–[16]. Such SiT based techniques utilize the spectrum more efficiently as they do not require dedicated slots for pilots. To the best of authors’ knowledge, a channel estimation technique utilizing spectrally efficient SiT along with CS based OMP for sparse massive-MIMO channels lacks in the existing literature. The sequence cn k is computed as below cn k P 1ci',n e j i,nk , k,
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