492 research outputs found
On the Performance of Turbo Signal Recovery with Partial DFT Sensing Matrices
This letter is on the performance of the turbo signal recovery (TSR)
algorithm for partial discrete Fourier transform (DFT) matrices based
compressed sensing. Based on state evolution analysis, we prove that TSR with a
partial DFT sensing matrix outperforms the well-known approximate message
passing (AMP) algorithm with an independent identically distributed (IID)
sensing matrix.Comment: to appear in IEEE Signal Processing Letter
Over-the-Air Federated Learning Over MIMO Channels: A Sparse-Coded Multiplexing Approach
The communication bottleneck of over-the-air federated learning (OA-FL) lies
in uploading the gradients of local learning models. In this paper, we study
the reduction of the communication overhead in the gradients uploading by using
the multiple-input multiple-output (MIMO) technique. We propose a novel
sparse-coded multiplexing (SCoM) approach that employs sparse-coding
compression and MIMO multiplexing to balance the communication overhead and the
learning performance of the FL model. We derive an upper bound on the learning
performance loss of the SCoM-based MIMO OA-FL scheme by quantitatively
characterizing the gradient aggregation error. Based on the analysis results,
we show that the optimal number of multiplexed data streams to minimize the
upper bound on the FL learning performance loss is given by the minimum of the
numbers of transmit and receive antennas. We then formulate an optimization
problem for the design of precoding and post-processing matrices to minimize
the gradient aggregation error. To solve this problem, we develop a
low-complexity algorithm based on alternating optimization (AO) and alternating
direction method of multipliers (ADMM), which effectively mitigates the impact
of the gradient aggregation error. Numerical results demonstrate the superb
performance of the proposed SCoM approach
Sparsity Enhanced Decision Feedback Equalization
For single-carrier systems with frequency domain equalization, decision
feedback equalization (DFE) performs better than linear equalization and has
much lower computational complexity than sequence maximum likelihood detection.
The main challenge in DFE is the feedback symbol selection rule. In this paper,
we give a theoretical framework for a simple, sparsity based thresholding
algorithm. We feed back multiple symbols in each iteration, so the algorithm
converges fast and has a low computational cost. We show how the initial
solution can be obtained via convex relaxation instead of linear equalization,
and illustrate the impact that the choice of the initial solution has on the
bit error rate performance of our algorithm. The algorithm is applicable in
several existing wireless communication systems (SC-FDMA, MC-CDMA, MIMO-OFDM).
Numerical results illustrate significant performance improvement in terms of
bit error rate compared to the MMSE solution
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