Year-end Sale % OFF 
Abstract
Uplink and Downlink channel estimation in massive Multiple Input Multiple Output (MIMO) systems is an intricate issue because of the increasing channel matrix dimensions. The channel feedback overhead using traditional codebook schemes is very large, which consumes more bandwidth and decreases the overall system efficiency. The purpose of this paper is to decrease the channel estimation overhead by taking the advantage of sparse attributes and also to optimize the Energy Efficiency (EE) of the system. To cope with this issue, we propose a novel approach by using Compressed-Sensing (CS), Block Iterative-Support-Detection (Block-ISD), Angle-of-Departure (AoD) and Structured Compressive Sampling Matching Pursuit (S-CoSaMP) algorithms to reduce the channel estimation overhead and compare them with the traditional algorithms. The CS uses temporal-correlation of time-varying channels to produce Differential-Channel Impulse Response (DCIR) among two CIRs that are adjacent in time-slots. DCIR has greater sparsity than the conventional CIRs as it can be easily compressed. The Block-ISD uses spatial-correlation of the channels to obtain the block-sparsity which results in lower pilot-overhead. AoD quantizes the channels whose path-AoDs variation is slower than path-gains and such information is utilized for reducing the overhead. S-CoSaMP deploys structured-sparsity to obtain reliable Channel-State-Information (CSI). MATLAB simulation results show that the proposed CS based algorithms reduce the feedback and pilot-overhead by a significant percentage and also improve the system capacity as compared with the traditional algorithms. Moreover, the EE level increases with increasing Base Station (BS) density, UE density and lowering hardware impairments level.
Highlights
Massive Multiple Input Multiple Output (MIMO) is an upcoming technology for the future 5G Wireless Communication Systems [1].It is a physical layer (PHY-L) envisioned technology that will resolve various physical layer issues in modern cellular networks
The sub-space pursuit (SP) technique is used for the recovery process which is more robust to noise and have low complexity [36]
The results clearly reveal that the direct compressed sensing (DCS) scheme fails for 25% compression ratio as its overhead is lesser than required and the gap between this and the proposed CS differential algorithm gets larger and its NMSE does not reduce as required, while the proposed scheme performs much better than former scheme
Summary
Massive MIMO is an upcoming technology for the future 5G Wireless Communication Systems [1]. The conventional DL channel estimation techniques such as Minimum Mean Square Error (MMSE) and Least Square (LS) are not appropriate because, the number of orthogonal-pilots increases directly with the number of BS antennas, and it causes excessively large-overhead. To decrease such overhead, we deploy an enhanced Block-ISD and S-CoSaMP algorithms. We deploy Compressed Sensing (CS), Block-ISD, AoD and S-CoSaMP algorithms to reduce the channel feedback and pilot overhead for UL and DL scenarios. Ρ is the correlation coefficient, αhi is the support element of CIR hi and μ is the channel sparsity
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
