1,165 research outputs found
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Spatially Coupled Sparse Regression Codes for Single- and Multi-user Communications
Sparse regression codes (SPARCs) are a class of channel codes for efficient communication over the single-user additive white Gaussian noise (AWGN) channel at rates approaching the channel capacity. In a standard SPARC, codewords are sparse linear combinations of columns of an i.i.d. Gaussian design matrix, and the user message is encoded in the indices of those columns. Techniques such as power allocation and spatial coupling have been proposed to improve the performance of low-complexity iterative decoding algorithms such as approximate message passing (AMP).
In this thesis we investigate spatially coupled SPARCs, where the design matrix has a block- wise band-diagonal structure, and modulated SPARCs, which generalise standard SPARCs by introducing modulation to the encoding of user messages. We introduce a base matrix framework which provides a unified way to construct power allocated and spatially coupled design matrices, and propose AMP decoders for modulated SPARCs constructed using base matrices.
We prove that phase shift keying modulated and spatially coupled SPARCs with AMP decoding asymptotically achieve the capacity of the (complex) AWGN channel. We also show via numerical simulations that they can achieve lower error rates than standard coded modulation schemes at finite code lengths. A sliding window AMP decoder is proposed for spatially coupled SPARCs that significantly reduces the decoding latency and complexity.
We then investigate coding schemes based on random linear models and AMP decoding for the multi-user Gaussian multiple access channel in the asymptotic regime where the number of users grows linearly with the code length. For a fixed target error rate and message size per user (in bits), we obtain the exact trade-off between energy-per-bit and the user density achievable in the large system limit. We show that a coding scheme based on spatially coupled Gaussian matrices and AMP decoding achieves near-optimal trade-off for a large range of user densities. To the best of our knowledge, this is the first efficient coding scheme to do so in this multiple access regime. Moreover, the spatially coupled coding scheme has a practical interpretation: it can be viewed as block-wise time-division with overlap.Funded by a Doctoral Training Partnership Award from the Engineering and Physical Sciences Research Council
Approximate Message-Passing Decoder and Capacity Achieving Sparse Superposition Codes
We study the approximate message-passing decoder for sparse superposition
coding on the additive white Gaussian noise channel and extend our preliminary
work [1]. We use heuristic statistical-physics-based tools such as the cavity
and the replica methods for the statistical analysis of the scheme. While
superposition codes asymptotically reach the Shannon capacity, we show that our
iterative decoder is limited by a phase transition similar to the one that
happens in Low Density Parity check codes. We consider two solutions to this
problem, that both allow to reach the Shannon capacity: i) a power allocation
strategy and ii) the use of spatial coupling, a novelty for these codes that
appears to be promising. We present in particular simulations suggesting that
spatial coupling is more robust and allows for better reconstruction at finite
code lengths. Finally, we show empirically that the use of a fast
Hadamard-based operator allows for an efficient reconstruction, both in terms
of computational time and memory, and the ability to deal with very large
messages.Comment: 40 pages, 18 figure
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Space-time-frequency methods for interference-limited communication systems
textTraditionally, noise in communication systems has been modeled as an additive, white Gaussian noise process with independent, identically distributed samples. Although this model accurately reflects thermal noise present in communication system electronics, it fails to capture the statistics of interference and other sources of noise, e.g. in unlicensed communication bands. Modern communication system designers must take into account interference and non-Gaussian noise to maximize efficiencies and capacities of current and future communication networks. In this work, I develop new multi-dimensional signal processing methods to improve performance of communication systems in three applications areas: (i) underwater acoustic, (ii) powerline, and (iii) multi-antenna cellular. In underwater acoustic communications, I address impairments caused by strong, time-varying and Doppler-spread reverberations (self-interference) using adaptive space-time signal processing methods. I apply these methods to array receivers with a large number of elements. In powerline communications, I address impairments caused by non-Gaussian noise arising from devices sharing the powerline. I develop and apply a cyclic adaptive modulation and coding scheme and a factor-graph-based impulsive noise mitigation method to improve signal quality and boost link throughput and robustness. In cellular communications, I develop a low-latency, high-throughput space-time-frequency processing framework used for large scale (up to 128 antenna) MIMO. This framework is used in the world's first 100-antenna MIMO system and processes up to 492 Gbps raw baseband samples in the uplink and downlink directions. My methods prove that multi-dimensional processing methods can be applied to increase communication system performance without sacrificing real-time requirements.Electrical and Computer Engineerin
Inference of the sparse kinetic Ising model using the decimation method
In this paper we study the inference of the kinetic Ising model on sparse
graphs by the decimation method. The decimation method, which was first
proposed in [Phys. Rev. Lett. 112, 070603] for the static inverse Ising
problem, tries to recover the topology of the inferred system by setting the
weakest couplings to zero iteratively. During the decimation process the
likelihood function is maximized over the remaining couplings. Unlike the
-optimization based methods, the decimation method does not use the
Laplace distribution as a heuristic choice of prior to select a sparse
solution. In our case, the whole process can be done automatically without
fixing any parameters by hand. We show that in the dynamical inference problem,
where the task is to reconstruct the couplings of an Ising model given the
data, the decimation process can be applied naturally into a maximum-likelihood
optimization algorithm, as opposed to the static case where pseudo-likelihood
method needs to be adopted. We also use extensive numerical studies to validate
the accuracy of our methods in dynamical inference problems. Our results
illustrate that on various topologies and with different distribution of
couplings, the decimation method outperforms the widely-used -optimization based methods.Comment: 11 pages, 5 figure
Graph Neural Network-Enhanced Expectation Propagation Algorithm for MIMO Turbo Receivers
Deep neural networks (NNs) are considered a powerful tool for balancing the
performance and complexity of multiple-input multiple-output (MIMO) receivers
due to their accurate feature extraction, high parallelism, and excellent
inference ability. Graph NNs (GNNs) have recently demonstrated outstanding
capability in learning enhanced message passing rules and have shown success in
overcoming the drawback of inaccurate Gaussian approximation of expectation
propagation (EP)-based MIMO detectors. However, the application of the
GNN-enhanced EP detector to MIMO turbo receivers is underexplored and
non-trivial due to the requirement of extrinsic information for iterative
processing. This paper proposes a GNN-enhanced EP algorithm for MIMO turbo
receivers, which realizes the turbo principle of generating extrinsic
information from the MIMO detector through a specially designed training
procedure. Additionally, an edge pruning strategy is designed to eliminate
redundant connections in the original fully connected model of the GNN
utilizing the correlation information inherently from the EP algorithm. Edge
pruning reduces the computational cost dramatically and enables the network to
focus more attention on the weights that are vital for performance. Simulation
results and complexity analysis indicate that the proposed MIMO turbo receiver
outperforms the EP turbo approaches by over 1 dB at the bit error rate of
, exhibits performance equivalent to state-of-the-art receivers with
2.5 times shorter running time, and adapts to various scenarios.Comment: 15 pages, 12 figures, 2 tables. This paper has been accepted for
publication by the IEEE Transactions on Signal Processing. Copyright may be
transferred without notice, after which this version may no longer be
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Massive Unsourced Random Access: Exploiting Angular Domain Sparsity
This paper investigates the unsourced random access (URA) scheme to accommodate numerous machine-type users communicating to a base station equipped with multiple antennas. Existing works adopt a slotted transmission strategy to reduce system complexity; they operate under the framework of coupled compressed sensing (CCS) which concatenates an outer tree code to an inner compressed sensing code for slot-wise message stitching. We suggest that by exploiting the MIMO channel information in the angular domain, redundancies required by the tree encoder/decoder in CCS can be removed to improve spectral efficiency, thereby an uncoupled transmission protocol is devised. To perform activity detection and channel estimation, we propose an expectation-maximization-aided generalized approximate message passing algorithm with a Markov random field support structure, which captures the inherent clustered sparsity structure of the angular domain channel. Then, message reconstruction in the form of a clustering decoder is performed by recognizing slot-distributed channels of each active user based on similarity. We put forward the slot-balanced K-means algorithm as the kernel of the clustering decoder, resolving constraints and collisions specific to the application scene. Extensive simulations reveal that the proposed scheme achieves a better error performance at high spectral efficiency compared to the CCS-based URA schemes
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