Bilinear Expectation Propagation for Distributed Semi-Blind Joint Channel Estimation and Data Detection in Cell-Free Massive MIMO - Karataev, Alexander; Forsch, Christian; Cottatellucci, Laura (IEEE Open Journal of Signal Processing, 2024)
We consider a cell-free massive multiple-input multiple-output (CF-MaMIMO) communication system in the uplink transmission and propose a novel algorithm for blind or semi-blind joint channel estimation and data detection (JCD). We formulate the problem in the framework of bilinear inference and develop a solution based on the expectation propagation (EP) method for both channel estimation and data detection. We propose a new approximation of the joint a posteriori distribution of the channel and data whose representation as a factor graph enables the application of the EP approach using the message-passing technique, local low-complexity computations at the nodes, and an effective modeling of channel-data interplay. The derived algorithm, called bilinear-EP JCD, allows for a distributed implementation among access points (APs) and the central processing unit (CPU) and has polynomial complexity. Our simulation results show that it outperforms other EP-based state-of-the-art polynomial time algorithms.
CRIS-Team Scopus Importer:2024-02-09
Keywords: approximate inference; Bayesian learning; bilinear inference; cell-free massive MIMO; distributed algorithms; Expectation propagation; joint channel estimation and data detection

Distributed Joint User Activity Detection, Channel Estimation, and Data Detection via Expectation Propagation in Cell-Free Massive MIMO - Forsch, Christian; Karataev, Alexander; Cottatellucci, Laura (2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2024)
We consider the uplink of a grant-free cell-free massive multiple-input multiple-output (GF-CF-MaMIMO) system. We propose an algorithm for distributed joint activity detection, channel estimation, and data detection (JACD) based on expectation propagation (EP) called JACD-EP. We develop the algorithm by factorizing the a posteriori probability (APP) of activities, channels, and transmitted data, then, mapping functions and variables onto a factor graph, and finally, performing a message passing on the resulting factor graph. If users with the same pilot sequence are sufficiently distant from each other, the JACD-EP algorithm is able to mitigate the effects of pilot contamination which naturally occurs in grant-free systems due to the large number of potential users and limited signaling resources. Furthermore, it outperforms state-of-the-art algorithms for JACD in GF-CF-MaMIMO system

Storage Constrained Linear Computation Coding - Karataev, Alexander; Rosenberger, Hans; Bereyhi, Ali; Müller, Ralf (Data Compression Conference Proceedings, 2023)
Linear computation coding (LCC) has been developed in [1] as a new framework for the computation of linear functions. LCC significantly reduces the complexity of matrix-vector multiplication [1]. In basic LCC, storage is not restricted i.e. the wiring exponents are arbitrary integer exponents of 2.
CRIS-Team Scopus Importer:2023-06-09
Keywords: approximate computing; computational complexity; fixed point arithmetic; function compression; linear systems; quantization