A
Ananthanarayanan Chockalingam
Researcher at Indian Institute of Science
Publications - 360
Citations - 8132
Ananthanarayanan Chockalingam is an academic researcher from Indian Institute of Science. The author has contributed to research in topics: MIMO & Fading. The author has an hindex of 45, co-authored 346 publications receiving 6969 citations. Previous affiliations of Ananthanarayanan Chockalingam include University of Electronic Science and Technology of China & University of California, San Diego.
Papers
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Journal ArticleDOI
A Low-Complexity Detector for Large MIMO Systems and Multicarrier CDMA Systems
TL;DR: A low-complexity detector which achieves uncoded near-exponential diversity performance for hundreds of antennas with an average per-bit complexity of just O(NtNr), where Nt and Nr denote the number of transmit and receive antennas, respectively is presented.
Journal ArticleDOI
Single-Carrier SM-MIMO: A Promising Design for Broadband Large-Scale Antenna Systems
Ping Yang,Yue Xiao,Yong Liang Guan,K. V. S. Hari,Ananthanarayanan Chockalingam,Shinya Sugiura,Harald Haas,Marco Di Renzo,Christos Masouros,Zilong Liu,Lixia Xiao,Shaoqian Li,Lajos Hanzo +12 more
TL;DR: A comprehensive overview of the latest research achievements of SC-SM is presented, which outlines the associated transceiver design, the benefits and potential tradeoffs, the LSA aided multiuser (MU) transmission developments, the relevant open research issues as well as the potential solutions of this appealing transmission technique.
Journal ArticleDOI
Layered Tabu Search Algorithm for Large-MIMO Detection and a Lower Bound on ML Performance
TL;DR: A lower bound on the maximum-likelihood (ML) bit error performance using the local neighborhood search is obtained using the proposed low-complexity algorithm for large-MIMO detection based on a layered low- complexity localNeighborhood search.
Journal ArticleDOI
Channel Hardening-Exploiting Message Passing (CHEMP) Receiver in Large-Scale MIMO Systems
TL;DR: A multiple-input multiple-output (MIMO) receiver algorithm that exploits channel hardening that occurs in large MIMO channels and achieves a significantly better performance compared to MMSE and other message passing detection algorithms using MMSE estimate of H.
Book ChapterDOI
Large MIMO Systems
TL;DR: There is in-depth coverage of algorithms for large MIMO signal processing, based on meta-heuristics, belief propagation and Monte Carlo sampling techniques, and suited for large-scale signal detection, precoding and LDPC code designs.