A sparsity detection framework for on-off random access channels (original) (raw)
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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.
Multiuser detection in asynchronous on-off random access channels using lasso
… (Allerton), 2010 48th …, 2010
This paper considers on-off random access channels where users transmit either a one or a zero to a base station. Such channels represent an abstraction of control channels used for scheduling requests in third-generation cellular systems and uplinks in wireless sensor networks deployed for target detection. This paper introduces a novel convex-optimization-based scheme for multiuser detection (MUD) in asynchronous on-off random access channels that does not require knowledge of the delays or the instantaneous received signal-to-noise ratios of the individual users at the base station. For any fixed number of temporal signal space dimensions N and maximum delay τ in the system, the proposed scheme can accommodate M exp(O(N 1/3 )) total users and k N/ log M active users in the system-a significant improvement over the k ≤ M N scaling suggested by the use of classical matched-filtering-based approaches to MUD employing orthogonal signaling. Furthermore, the computational complexity of the proposed scheme differs from that of a similar oracle-based scheme with perfect knowledge of the user delays by at most a factor of log(N +τ ). Finally, the results presented in here are nonasymptotic, in contrast to related previous work for synchronous channels that only guarantees that the probability of MUD error at the base station goes to zero asymptotically in M .
Improved sparse multiuser detection based on modulation-alphabets exploitation
Digital Signal Processing, 2017
In this paper, we focus on sporadic random-access communications and consider compressed-sensing (CS) techniques to perform the multiuser detection (MUD). Since all the users do not necessarily transmit information, MUD consists in detecting the transmitting users (activity detection) and their corresponding transmitted data (data detection). The main results presented here rely on the exploitation of the user signal alphabet knowledge within the detection step. To this aim, several modifications of the group orthogonal matching pursuit (GOMP) algorithm were proposed, differing in the way the modulation alphabet knowledge is considered within the detection stage. These modifications can be extended to any greedy-based CS-MUD. To overcome the error floor occurring at high SNR with a higher number of active users, we then propose an iterative 1 minimization-based MUD algorithm that alternates between activity and data detection. Compared to the existing GOMP-based CS-MUD, the proposed modified GOMP algorithms exhibit a significant gain with almost the same complexity. The iterative 1 minimization-based MUD algorithm has a higher complexity but outperforms all the others without any observed error-floor.
Radioengineering, 2021
In uplink (UL) grant-free sparse code multiple access (SCMA) systems, unlike the conventional contentionbased transmission, users' activities should be known before data decoding due to sporadic transmission in massive machine-type communication (mMTC). Since compressed sensing (CS) is the theory of sparse signal reconstruction with fewer samples, this theory is a good solution to detect active users. In this paper, we propose the dynamic and sparsity adaptive compressed sensing (DSACS) based active user detection (AUD) and channel estimation (CE) of UL grant-free SCMA. Unlike most of the CS-based methods, sparsity knowledge or potential active user list is not needed in the proposed algorithm, which is already not known in the practical systems. The proposed algorithm adopts a stagewise approach to expand the set of accurate active users for adaptively achieve the sparsity level. It uses the temporal correlation of users' activity to improve performance and reduce complexity. Then, false detected users are eliminated with joint message passing algorithm (JMPA), and channel gains of the accurate active users are estimated again in CE with feedback. The simulation results show that the proposed method without sparsity knowledge is capable of achieving detection in various scenarios in case of sporadic transmission in mMTC.
Compressive Random Access Using A Common Overloaded Control Channel
—We introduce a " one shot " random access procedure where users can send a message without a priori synchronizing with the network. In this procedure a common overloaded control channel is used to jointly detect sparse user activity and sparse channel profiles. The detected information is subsequently used to demodulate the data in dedicated frequency slots. We analyze the system theoretically and provide a link between achievable rates and standard compressing sensing estimates in terms of explicit expressions and scaling laws. Finally, we support our findings with simulations in an LTE-Alike setting allowing " one shot " sparse random access of 100 users in 1ms.
Multiple Access for Small Packets Based on Precoding and Sparsity-Aware Detection
Modern mobile terminals often produce a large number of small data packets. For these packets, it is inefficient to follow the conventional medium access control protocols because of poor utilization of service resources. We propose a novel multiple access scheme that employs block-spreading based precoding at the transmitters and sparsity-aware detection schemes at the base station. The proposed scheme is well suited for the emerging massive multiple-input multiple-output (MIMO) systems, as well as conventional cellular systems with a small number of base-station antennas. The transmitters employ precoding in time domain to enable the simultaneous transmissions of many users, which could be even more than the number of receive antennas at the base station. The system is modeled as a linear system of equations with block-sparse unknowns. We first adopt the block orthogonal matching pursuit (BOMP) algorithm to recover the transmitted signals. We then develop an improved algorithm, named interference cancellation BOMP (ICBOMP), which takes advantage of error correction and detection coding to perform perfect interference cancellation during each iteration of BOMP algorithm. Conditions for guaranteed data recovery are identified. The simulation results demonstrate that the proposed scheme can accommodate more simultaneous transmissions than conventional schemes in typical small-packet transmission scenarios.
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.
Efficient limited data multi-antenna compressed spectrum sensing exploiting angular sparsity
In this paper, we propose a novel approach for multiple antenna spectrum sensing based on compressed sensing. Our focus is on the angular sparsity of the received signal given an unknown number of primary user source signals impinging upon the antenna array from different Directions Of Arrival (DOA). Given multiple snapshots over a small time period, multiple measurement vectors are available and a joint sparse recovery is performed to estimate the common sparsity profile over the angular domain. In this estimation process, we employ the regularized M-FOCUSS algorithm [1], which is the noisy multiple snapshot extension of the iterative weighted minimumnorm algorithm, called FOCUSS. The contribution of this paper is to take advantage of the sparse primary user DOA estimation within the detection framework of multiple antenna spectrum sensing. In this scope, an accurate sparse reconstruction is not required and a coarse estimation using a reduced number of snapshots is sufficient to decide about the number of present primary users reflected by the angular sparsity order of the received signal. A simulation study shows significant constant false alarm rate performance gain of the proposed approach compared to the conventional maximum to minimum eigenvalue detector especially when the number of PUs increases.
Multi-User Detection Using ADMM-Based Compressive Sensing for Uplink Grant-Free NOMA
IEEE Wireless Communications Letters, 2018
Non-orthogonal multiple access (NOMA) is being considered as a primary candidate to address the challenge of massive connectivity in the fifth generation (5G) wireless communication systems. In this letter, we propose a low-complexity NOMA mechanism with efficient multiuser detection (MUD) based on the adaptive alternating direction method of multipliers (ADMM), which is able to jointly detect user activity and transmitted data. The proposed algorithm leverages the transmit symbol estimate and active user set as "prior knowledge", which can be obtained from the previous iterations/time intervals, for improved MUD performance. We demonstrate that our proposed mechanism outperforms the state-of-art MUD NOMA schemes. Keywords-Alternative direction method of multipliers (ADMM), compressive sensing, non-orthogonal multiple access (NOMA).
Subspace compressive detection for sparse signals
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008
The emerging theory of compressed sensing (CS) provides a universal signal detection approach for sparse signals at sub-Nyquist sampling rates. A small number of random projection measurements from the received analog signal would suffice to provide salient information for signal detection. However, the compressive measurements are not efficient at gathering signal energy. In this paper, a set of detectors called subspace compressive detectors are proposed where a more efficient detection scheme can be constructed by exploiting the sparsity model of the underlying signal. Furthermore, we show that the signal sparsity model can be approximately estimated using reconstruction algorithms with very limited random measurements on the training signals. Based on the estimated signal sparsity model, an effective subspace random measurement matrix can be designed for unknown signal detection, which significantly reduces the necessary number of measurements. The performance of the subspace compressive detectors is analyzed. Simulation results show the effectiveness of the proposed subspace compressive detectors.