Sparse Signal Processing Concepts for Efficient 5G System Design (original) (raw)
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– submitted for publication Sparse Signal Processing Concepts for Efficient 5G System Design
2016
As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will also describe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.
2020
Applying compressive sensing (CS) allows for subNyquist sampling in several application areas in 5G and beyond. This reduces the associated training, feedback, and computation overheads in many applications. However, the applicability of CS relies on the validity of a signal sparsity assumption and knowing the exact sparsity level. It is customary to assume a foreknown sparsity level. Still, this assumption is not valid in practice, especially when applying learned dictionaries as sparsifying transforms. The problem is more strongly pronounced with multidimensional sparsity. In this paper, we propose an algorithm for estimating the composite sparsity lying in multiple domains defined by learned dictionaries. The proposed algorithm estimates the sparsity level over a dictionary by inferring it from its counterpart with respect to a compact discrete Fourier basis. This inference is achieved by a machine learning prediction. This setting learns the intrinsic relationship between the co...
APPLICATION OF COMPRESSIVE SENSING TO SPARSE CHANNEL ESTIMATION2
Compressive sensing is a novel scenario in which a sparse signal in a known transform domain is acquired with much fewer samples than is required by the dimensions of the domain. The only condition is that the sampling process is incoherent with the transform that achieves the sparse representation and by sparse we mean that most of the weighting coefficients of the signal representation in the transform domain are zero. By incoherent it is meant that the distances between sparse signals are approximately conserved as distances between their respective measurements generated by the sampling process.
Robust massive MIMO channel estimation for 5G networks using compressive sensing technique
AEU - International Journal of Electronics and Communications, 2020
The pilot overhead provides fundamental limits on the performance of massive multiple-input multipleoutput (MIMO) systems. This is because the performance of such systems is based on the failure of the presentation of accurate channel state information (CSI). Based on the theory of compressive sensing, this paper presents a novel channel estimation technique as the mean of minimizing the problems associated with pilot overhead. The proposed technique is based on the combination of the compressive sampling matching and sparsity adaptive matching pursuit techniques. The sources of the signals in MIMO systems are sparsely distributed in terms of spatial correlations. This distribution pattern enables then use of compressive sampling techniques to solve the channel estimation problem in MIMO systems. Simulation results demonstrate that the proposed channel estimation outperforms the conventional compressive sensing (CS)-based channel estimation algorithms in terms of the normalized mean square error (NMSE) performance at high signal-to-noise ratios (SNRs). Furthermore, it reduces the computational complexity of the channel estimation compared to conventional methods. In addition to the achieved performance gain in terms of NMSE, the presented method significantly reduces pilot overhead compared to conventional channel estimation techniques.
A sparsity detection framework for on-off random access channels
2009
This paper considers a simple on-off random multiple access channel, where users communicate simultaneously to a single receiver over degrees of freedom. Each user transmits with probability , where typically < ≪ , and the receiver must detect which users transmitted. We show that when the codebook has i.i.d. Gaussian entries, detecting which users transmitted is mathematically equivalent to a certain sparsity detection problem considered in compressed sensing. Using recent sparsity results, we derive upper and lower bounds on the capacities of these channels. We show that common sparsity detection algorithms, such as lasso and orthogonal matching pursuit (OMP), can be used as tractable multiuser detection schemes and have significantly better performance than single-user detection. These methods do achieve some near-far resistance but-at high signal-to-noise ratios (SNRs)-may achieve capacities far below optimal maximum likelihood detection. We then present a new algorithm, called sequential OMP, that illustrates that iterative detection combined with power ordering or power shaping can significantly improve the high SNR performance. Sequential OMP is analogous to successive interference cancellation in the classic multiple access channel. Our results thereby provide insight into the roles of power control and multiuser detection on random-access signaling.
On-Off Random Access Channels: A Compressed Sensing Framework
Computing Research Repository, 2009
This paper considers a simple on-off random multiple access channel, where n users communicate simultaneously to a single receiver over m degrees of freedom. Each user transmits with probability λ, where typically λn < m ≪ n, and the receiver must detect which users transmitted. We show that when the codebook has i.i.d. Gaussian entries, detecting which users transmitted is mathematically equivalent to a certain sparsity detection problem considered in compressed sensing. Using recent sparsity results, we derive upper and lower bounds on the capacities of these channels. We show that common sparsity detection algorithms, such as lasso and orthogonal matching pursuit (OMP), can be used as tractable multiuser detection schemes and have significantly better performance than single-user detection. These methods do achieve some near-far resistance but-at high signal-to-noise ratios (SNRs)-may achieve capacities far below optimal maximum likelihood detection. We then present a new algorithm, called sequential OMP, that illustrates that iterative detection combined with power ordering or power shaping can significantly improve the high SNR performance. Sequential OMP is analogous to successive interference cancellation in the classic multiple access channel. Our results thereby provide insight into the roles of power control and multiuser detection on random-access signalling.
2016
Many wireless channel behavior exhibits approximate sparse modeling in time domain, therefore compressive sensing (CS) approaches are applied for more accurate wireless channel estimation than traditional least squares approach. However, the CS approach is not applied for multicarrier data information recovery because the transmitted symbol can be sparse neither in time domain nor in frequency domain. In this paper, a new Sparse Frequency Division Multiplexing (SFDM) approach is suggested to generate sparse multicarrier mapping in frequency domain based on the huge combinatorial domain. The subcarriers will be mapped in sparse manner according to data stream for taking advantages of multicarrier modulation with lower number of subcarriers. The number of activated subcarriers is designed to achieve the same as Orthogonal Frequency–Division Multiplexing data rate under lower signal-to-noise ratio. The proposed approach exploits the double sparsity of data symbol in the frequency domai...
A Compressive Signal Detection Scheme Based on Sparsity
Compressed sensing is a revolutionary technology in the research field of signal processing, which can reconstruct the sparse signal using fewer number of compressive measurements compared with conventional reconstruction methods. Compressed sensing can also be utilized to detect the sparse signal. However, the exact reconstruction operation is not necessary when the system aims to detect such sparse signal. Based on compressed sensing, a new compressive signal detection scheme using the sparsity order of the sparse signal is proposed in this paper. Compared with similar detection scheme using the supports of the sparse signal, the newly proposed scheme requires much fewer number of compressive samples. In particular, the proposed scheme does not require the support prior-information of the sparse signal. Simulation results verify the advantages of the proposed scheme and indicate that the new scheme can achieve better detection performance.
Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels
Proceedings of the IEEE, 2010
High-rate data communication over a multipath wireless channel often requires that the channel response be known at the receiver. Training-based methods, which probe the channel in time, frequency, and space with known signals and reconstruct the channel response from the output signals, are most commonly used to accomplish this task. Traditional training-based channel estimation methods, typically comprising of linear reconstruction techniques (such as the maximum likelihood or the minimum mean squared error estimators), are known to be optimal for rich multipath channels. However, physical arguments and growing experimental evidence suggest that wireless channels encountered in practice exhibit a sparse multipath structure that gets pronounced as the signal space dimension gets large (e.g., due to large bandwidth or large number of antennas). In this paper, we formalize the notion of multipath sparsity and present a new approach to estimating sparse multipath channels that is based on some of the recent advances in the theory of compressed sensing. In particular, it is shown in the paper that the proposed approach, which is termed as compressed channel sensing, achieves a target reconstruction error using far less energy and, in many instances, latency and bandwidth than that dictated by the traditional training-based methods.