CS-based Multi-user Detection Research Articles

Design and allocation of low correlated signatures for sequence block-based compressive sensing multiuser detection in massive machine-type communication

Compressed sensing-based multiuser detection (CSMUD) is a promising technique to enable non-orthogonal multiple access (NOMA) for massive machine-type communication (mMTC) in beyond 5G wireless communication systems. CSMUD facilitates grant-free multiple access by jointly detecting the activity and data at the receiver. The performance of CSMUD is predominantly determined by the activity detection, governed by the maximum correlation between the signatures. Sequence block-based CSMUD (SB-CSMUD), an enhanced CSMUD, exploits temporal and spreading diversity to improve the activity detection. In this paper, we propose a three-step approach to design and allocate low correlated signatures and improve activity detection in SB-CSMUD. In the first step, a low correlated random sensing matrix is designed, followed by the designing of low correlated signatures. The designed signatures are then allocated to the nodes according to the traffic pattern of the mMTC. The proposed signatures reduces the maximum correlation by 4.48%, which is further reduced by the proposed signature allocation scheme. The simulation results validate the analysis and show that our proposed scheme significantly reduces the detection error rate (DER) without increasing computational complexity.

Low-Complexity Block Coordinate Descend Based Multiuser Detection for Uplink Grant-Free NOMA

Grant-free non-orthogonal multiple access (NOMA) scheme is considered as a promising candidate for the enabling of massive connectivity and reduced signalling overhead for Internet of Things (IoT) applications in massive machine-type communication (mMTC) networks. Exploiting the inherent nature of sporadic transmissions in the grant-free NOMA systems, compressed sensing based multiuser detection (CS-MUD) has been deemed as a powerful solution to user activity detection (UAD) and data detection (DD). In this paper, block coordinate descend (BCD) method is employed in CS-MUD to reduce the computational complexity. We propose two modified BCD based algorithms, called enhanced BCD (EBCD) and complexity reduction enhanced BCD (CR-EBCD), respectively. To be specific, by incorporating a novel candidate set pruning mechanism into the original BCD framework, our proposed EBCD algorithm achieves remarkable CS-MUD performance improvement. In addition, the proposed CR-EBCD algorithm further ameliorates the proposed EBCD by eliminating the redundant matrix multiplications during the iteration process. As a consequence, compared with the proposed EBCD algorithm, our proposed CR-EBCD algorithm enjoys two orders of magnitude complexity saving without any CS-MUD performance degradation, rendering it a viable solution for future mMTC scenarios. Extensive simulation results demonstrate the bound-approaching performance as well as ultra-low computational complexity.

A Survey: Nonorthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC

One objective of the 5G communication system and beyond is to support massive machine type of communication (mMTC) to propel the fast growth of diverse Internet of Things use cases. The mMTC is aimed at providing connectivity to tens of billions of sensor nodes. The dramatic increase of sensor devices and massive connectivity impose critical challenges for the network to handle the enormous control signaling overhead with limited radio resources. Nonorthogonal multiple access (NOMA) is a new paradigm shift in the design of multiple user detection and multiple access. NOMA with compressive sensing-based multiuser detection is one of the promising candidates to address the challenges of mMTC. The survey article is aimed at providing an overview of the current state-of-art research work in various compressive sensing-based techniques that enable NOMA. We present characteristics of different algorithms and compare their pros and cons, thereby providing useful insights for researchers to make further contributions in NOMA using compressive sensing techniques. Nonorthogonal CDMA massive connectivity grant free medium access compressive sensing multiuser detection

Uplink Grant-Free NOMA With Sinusoidal Spreading Sequences

Grant-access based legacy networks are unable to deliver Uplink (UL) dominated sporadic transmission of short packets with low signalling overhead, which are the requirements of Massive Machine Type Communication (mMTC). Hence, grant-free Non-Orthogonal Multiple Access (NOMA) techniques are more suited for mMTC. Due to the inherent sparsity in the user activity pattern in an mMTC system, the existing literature revolves around Compressive Sensing based Multi User Detection (CS-MUD) algorithms, which employ random or Zadoff-Chu spreading sequences for NOMA. In this paper we explore the utility of sinusoidal spreading sequences, which allow some non-iterative algorithms to be employed in the context of active user detection, channel estimation and data detection in a UL grant-free mMTC system. These algorithms do not need prior knowledge about the user behaviour statistics, noise statistics, and the level of sparsity in the user activity, which might be difficult to obtain at the Base Station (BS). We present a comprehensive performance and complexity analysis to assess the feasibility of our proposal. Extensive numerical trials demonstrate the utility of sinusoidal codes for mMTC.

Modeling, Analysis, and Optimization of Grant-Free NOMA in Massive MTC via Stochastic Geometry

Massive machine-type communications (mMTC) is a crucial scenario to support booming Internet of Things (IoTs) applications. In mMTC, although a large number of devices are registered to an access point (AP), very few of them are active with uplink short packet transmission at the same time, which requires novel design of protocols and receivers to enable efficient data transmission and accurate multi-user detection (MUD). Aiming at this problem, grant-free non-orthogonal multiple access (GF-NOMA) protocol is proposed. In GF-NOMA, active devices can directly transmit their preambles and data symbols altogether within one time frame, without grant from the AP. Compressive sensing (CS)-based receivers are adopted for non-orthogonal preambles (NOP)-based MUD, and successive interference cancellation is exploited to decode the superimposed data signals. In this paper, we model, analyze, and optimize the CS-based GF-MONA mMTC system via stochastic geometry (SG), from an aspect of network deployment. Based on the SG network model, we first analyze the success probability as well as the channel estimation error of the CS-based MUD in the preamble phase and then analyze the average aggregate data rate in the data phase. As IoT applications highly demands low energy consumption, low infrastructure cost, and flexible deployment, we optimize the energy efficiency and AP coverage efficiency of GF-NOMA via numerical methods. The validity of our analysis is verified via Monte Carlo simulations. Simulation results also show that CS-based GF-NOMA with NOP yields better MUD and data rate performances than contention-based GF-NOMA with orthogonal preambles and CS-based grant-free orthogonal multiple access.

Joint User Activity Identification and Channel Estimation for Grant-Free NOMA: A Spatial–Temporal Structure-Enhanced Approach

Exploiting the sparse nature of user activity, compressed sensing (CS) has been a powerful technique to realize efficient user detection in grant-free nonorthogonal multiple access (NOMA). However, most of the existing CS-based multiuser detection schemes merely independently incorporate the temporal correlation in frame-based transmission or spatial correlation induced by multiantenna reception, leading to unsatisfactory user detection performance. Driven by the observation in the CS theory that the signal recovery performance could be enhanced by an increased number of sparse vectors with a common support set, in this article, we propose a novel joint user activity identification and channel estimation (JUICE) framework by integrating the temporal correlation of active user sets with multiantenna reception, which could achieve superior user detection performance. Specifically, we first formulate the JUICE as a Kronecker CS (KCS) problem to model the CS measurement process, by fully extracting the spatial-temporal structure of user activity. Then, based on the mined spatial-temporal structure of user activity, an adaptive subspace pursuit algorithm is developed, i.e., spatial-temporal structure enhanced adaptive subspace pursuit (STS-ASP), which could realize efficient multiuser detection. A distinct advantage of the proposed algorithm is that it does not require any prior knowledge (e.g., the number of active users and the noise level), by adaptively acquiring the number of active users and employing the cross-validation technique to appropriately terminate the iterative procedures. Extensive experimental evaluation is conducted, and the results corroborate the superiority of the proposed framework compared with the existing CS-based multiuser detection methods.

Side-Information Aided Compressed Multi-User Detection for Up-Link Grant-Free NOMA

Grant-free non-orthogonal multiple access (NOMA) is considered as one of the most important methodologies for the machine-type communications (MTC). In the field of MTC, compressed sensing based multi-user detection (CS-MUD) has been recognized as an excellent candidate for joint user activity and data detection, since many users sporadically transmit short-size data packets at low rates. This article focuses on the CS-MUD problem in the up-link grant-free NOMA scenario, where users are (in)-active randomly in each time slot yet with high temporal correlation. First, we investigate the CS framework to fully extract the underlying side information in the temporal correlation and propose a novel CS-MUD algorithm. Then, to mitigate the performance degradation due to the imperfect channel estimation in practice, the proposed algorithm is further extended by utilizing the perturbed CS, where the impact of channel estimation errors is modeled as certain perturbation in the measurement matrix. Different from most of the state-of-the-art CS-MUD algorithms, both proposed algorithms can apply even in the absence of prior knowledge on the number of active users. Simulation results indicate that the proposed algorithms achieve better performance than the existing CS-MUD methods. Their convergence and complexity issues are also discussed theoretically and numerically.

Low-Complexity Multi-User Detection Based on Gradient Information for Uplink Grant-Free NOMA

Massive machine type communication (mMTC) serves an irreplaceable role in the development process of the Internet of Things (IoT). Because of its characteristics of massive connection and sporadic transmission, compressed sensing (CS) has been applied in joint user activity and data detection in the uplink grant-free non-orthogonal multiple access (NOMA) system. In previous work, greedy iterative-based multi-user detection (MUD) algorithms were developed in mMTC scenarios because of the computational benefit and competitive performance. However, conventional greedy iterative-based MUD algorithms still suffer from high computational complexity due to the process of large-size matrix inversion with the accession of massive devices into the system. In this paper, gradient information is used to address this problem. A low-complexity gradient descent-based gradient pursuit MUD (GDGP-MUD) algorithm is proposed, which uses the gradient information of error function in the process of iteration as a new updating direction, instead of the matrix inversion process. Then, a multi-step quasi-Newton MUD (MSQN-MUD) algorithm is proposed to improve the precision of detection while maintaining low complexity. In the algorithm, high-order information in the process of adjacent iteration is used effectively to update data values more accurately. Moreover, the convergence and complexity analysis of both algorithms are derived. The analysis shows that both proposed algorithms have lower computational consumption than most of the state-of-the-art greedy-based MUD algorithms. It is worth noting that in comparison to most existing CS-based MUD algorithms, the two proposed algorithms do not require the exact user sparsity level and, thus, reduce the dependence on prior knowledge. The numerical experiments demonstrate that the proposed algorithms have better real-time performance than existing greedy-based MUD algorithms with similar symbol error rate performance.

Multiuser Activity and Data Detection via Sparsity-Blind Greedy Recovery for Uplink Grant-Free NOMA

Exploiting the sparse activity of users, compressed sensing (CS) has been of interest in multiuser detection (MUD) for non-orthogonal multiple access (NOMA), to enable a massive connection of users in machine-type communications (MTC). In this letter, we propose a greedy algorithm with unknown sparsity level for CS-based multiuser activity and data detection in uplink grant-free NOMA. To accommodate practical scenarios, the algorithm employs a criterion to stop the iteration with no prior knowledge of sparsity level. Also, it requires no knowledge of noise variance by computing the log-likelihood ratios (LLR) approximately in its operation. With no need of sparsity and noise levels, we perform CS-based MUD with low complexity. Simulation results demonstrate that the proposed algorithm outperforms conventional CS-based solutions, nearly achieving the oracle performance of fully known supports.

A Novel Tensor CS-Based NOMA MIMO System for the Downlink of Massive Mission-Critical MTC in 5G and Beyond

The introduction of 5G and beyond systems brings new challenges for efficiently supporting the random access of a massive number of users with ultra-reliable low-latency requirements. To address these challenges, we propose a novel compressed sensing (CS)-based nonorthogonal multiple access (NOMA) multiple-input multiple-output (MIMO) scheme, called the CS-NOMA MIMO scheme, for the downlink of massive mission-critical machine-type communication (mmcMTC). By taking advantage of tensor CS techniques, a novel tensor CS-based packet (frame) structure, called TCS packet (frame), is proposed for the CS-NOMA MIMO scheme to further reduce the transmit time intervals (TTIs), resulting in smaller latency than that using the conventional packet (frame) structure. In the TCS packet (frame) structure, the CS signatures are assigned not only to different users but also to different time slots and different symbols of a time slot, thus achieving a user, time slot, and symbol overload system. We present a sufficient condition for the construction of the CS signatures that ensure effective deployment of tensor CS-based multiuser detection (CS-MUD) in user receivers. Furthermore, we consider the case of imperfect channel state information (CSI) and present an upper bound for the performance of the proposed scheme. Finally, the simulation results show that the proposed scheme can achieve high user overload (up to 5) and time slot overload (up to 3) when the activity ratio of users is 1%. When the time slot overload is 3, the proposed scheme with the TCS packet (frame) shortens time slots by at least 2/3 times that of the typical grant-based NOMA schemes. In addition, the block error rate (BLER) of the proposed scheme with the TCS packet (frame) is much lower than that with the conventional packet (frame), even less than 10−9 by repeat transmission when the time slot overload is 3.

On Simultaneous Multipacket Channel Estimation and Reception in Random Access for MTC Under Frequency-Selective Fading

In random access for machine-type communications, it is required not only to detect active devices, but also to estimate their channel state information when they transmit preambles. In this paper, we consider an approach for simultaneous multipacket channel estimation and reception (SMuCER) with a multicarrier system under frequency-selective fading, where the channel estimation becomes challenging due to frequency-selective fading and different round-trip delay between the devices. In the proposed approach, circular-shifted preambles are used to effectively increase the number of preambles (that can lower the probability of preamble collision), and compressive sensing (CS)-based multiuser detection (MUD) is employed. In order to have a good performance of CS-based MUD for SMuCER, we consider Alltop sequences. Furthermore, we formulate an optimization problem to maximize the number of successfully recovered active devices, where a large single system is to be divided into multiple subsystems. It is shown that multiple subsystems have not only a higher throughput, but also a lower computational complexity than a large single system.

EP-Based Joint Active User Detection and Channel Estimation for Massive Machine-Type Communications

Massive machine-type communication (mMTC) is a newly introduced service category in 5G wireless communication systems to support a variety of Internet-of-Things (IoT) applications. In recovering sparsely represented multi-user vectors, compressed sensing based multi-user detection (CS-MUD) can be used. CS-MUD is a feasible solution to the grant-free uplink non-orthogonal multiple access (NOMA) environments. In CS-MUD, active user detection (AUD) and channel estimation (CE) should be performed before data detection. In this paper, we propose the expectation propagation based joint AUD and CE (EP-AUD/CE) technique for mMTC networks. The expectation propagation (EP) algorithm is a Bayesian framework that approximates a computationally intractable probability distribution to an easily tractable distribution. The proposed technique finds the best approximation of the posterior distribution of the sparse channel vector. Using the approximate distribution, AUD and CE are jointly performed. We show by numerical simulations that the proposed technique substantially enhances AUD and CE performances over competing algorithms.

MAP-Based Active User and Data Detection for Massive Machine-Type Communications

With the advent of the Internet of things, massive machine-type communications (mMTC) have become one of the most important requirements for next generation communication systems. In the mMTC scenarios, grant-free nonorthogonal multiple access on the transmission side and compressive sensing-based multi-user detection (CS-MUD) on the reception side are a promising solution because many users sporadically transmit small data packets at low rates. In this paper, we propose a novel CS-MUD algorithm for the active user and data detection in the mMTC systems. The proposed scheme consists of the maximum a posteriori probability (MAP) based active user detector (MAP-AUD) and the MAP-based data detector (MAP-DD). By exchanging the extrinsic information between MAP-AUD and MAP-DD, the proposed algorithm improves the active user detection performance and the reliability of the data detection. In addition, we extend the proposed algorithm to exploit group sparsity. By jointly processing the multiple received data with common activity, the proposed algorithm dramatically enhances the active user detection performance. We show by numerical experiments that the proposed algorithm achieves a substantial performance gain over existing algorithms.

A novel compressed sensing-based non-orthogonal multiple access scheme for massive MTC in 5G systems

The main challenges for massive machine type communication in 5G system are to support random access for massive users and to control signaling overhead and data processing complexity. To address these challenges, we propose a novel compressed sensing (CS)-based non-orthogonal multiple access (NOMA) scheme, called CS-NOMA, which introduces low coherence spreading (LCS) signatures to enable joint activity and data detection without requiring the activity information of users in advance. We present a sufficient condition for the construction of the LCS signatures to ensure that a CS-based multi-user detection (CS-MUD) can be effectively deployed in base station. Furthermore, we study the CS-NOMA scheme with imperfect channel state information (CSI) and present a bound for the performance of the CS-NOMA scheme. Simulation results show that the proposed scheme achieves a relatively high system overload (up to 4) when the active users are relatively sparse with an activity ratio of 1%, which implies that the CS-NOMA scheme can significantly improve the spectral efficiency, avoid the control signaling overhead, and reduce the transmission latency.

Stability and Throughput of Random Access With CS-Based MUD for MTC

Recently, compressive random access has been considered for machine-type communications (MTC) as it has a potential to support massive connectivity by exploiting the sparsity of device activity. However, its performance limitations are not well studied yet compared with other well-known candidates for MTC, e.g., multichannel ALOHA. In this paper, we investigate the stability of compressive random access with a controlled access probability strategy under ideal conditions, and derive the maximum stable throughput, which shows that the maximum stable throughput of compressive random access is higher than that of multichannel ALOHA by a factor of 2. We also carry out simulations to see the performance of compressive random access under more realistic conditions with low-complexity multiuser detection (MUD) algorithms. Based on analysis and simulation results, we can claim that compressive random access can support more devices than conventional multichannel ALOHA and is well-suited for MTC.

Sparsity-Aware Ordered Successive Interference Cancellation for Massive Machine-Type Communications

In massive machine-type communication systems, by exploiting sporadic device activities, compressed sensing-based multi-user detection (CS-MUD) is used to recover sparse multi-user vectors. In CS-MUD, multi-user vectors are detected based on a sparsity-aware maximum a posteriori probability (S-MAP) criterion. To reduce the computational complexity of S-MAP detection, a sparsity-aware successive interference cancellation (SA-SIC) technique can be used. However, SA-SIC does not perform well without proper layer sorting due to error propagation. In this letter, we propose a novel sparsity-aware ordered SIC scheme that finds the optimal detection order based on the activity probabilities and channel gains of devices. Simulation results verify that the proposed scheme greatly improves the performance of SA-SIC.

Non-Orthogonal Multiple Access With Sequence Block Compressed Sensing Multiuser Detection for 5G

Compressed sensing-based multiuser detection (CSMUD) is a promising candidate to enable grant-free non-orthogonal multiple access for massive machine-type communication in the fifth-generation (5G) wireless communication system. Exploiting the sporadic node activity of the massive machine-type communication, CSMUD recovers the signal from far fewer linear measurements than the number of users, thereby facilitating massive connectivity. However, with the increase in the number of sensor nodes, the errors in the activity detection also increase. In this paper, we propose sequence block-based compressed sensing multiuser detection (SB-CSMUD), which enhances the activity detection by using block of multiple spreading sequences as signature of the node. It is demonstrated both empirically and theoretically that the SB-CSMUD scheme besides improving the detection error rate also increases the spectrum efficiency. Simulation results show that for the same least square estimation error rate (LSER), SB-CSMUD not only reduces the bandwidth by 10% but also reduces the bit error rate by more than 1.5 order of magnitude at signal-to-noise ratio (SNR) of 15 dB. The bandwidth can even be reduced by 25%–30% at SNR > 20 dB for achieving the same detection error rate at the cost of LSER. Furthermore, owing to the diversity in the spreading, the proposed scheme provides a more homogeneous performance for all the nodes. In addition, the computational complexity of the SB-CSMUD is the same as that of CSMUD at the only expense of the memory requirement for hosting the block sequence at the node.

Efficient Multi-User Detection for Uplink Grant-Free NOMA: Prior-Information Aided Adaptive Compressive Sensing Perspective

Non-orthogonal multiple access (NOMA) is an emerging research topic in the future fifth generation wireless communication networks, which is expected to support massive connectivity for massive machine-type communications (mMTC). Due to the sporadic communication nature of mMTC, the grant-free transmission methodology is highly expected in uplink NOMA systems, to drastically reduce the transmission latency and signaling overhead. Exploiting the inherent sparsity nature of user activity, compressive sensing (CS) techniques have been applied for efficient multi-user detection in the uplink grant-free NOMA. In this paper, we propose a prior-information-aided adaptive subspace pursuit (PIA-ASP) algorithm to improve the multi-user detection performance. In this algorithm, a parameter evaluating the quality of the prior-information support set is introduced, in order to exploit the intrinsically temporal correlation of active user support sets in several continuous time slots adaptively. Then, to mitigate the incorrect estimation effect of the prior support quality information, a robust PIA-ASP algorithm is further proposed, which adaptively exploits the prior support based on the corresponding support quality information in a conservative way. It is noted that both of the two proposed algorithms do not require the knowledge of the user sparsity level, while most of the state-of-the-art CS-based multi-user detection algorithms usually need. Moreover, for the two proposed algorithms, the upper bound of the signal detection error and the computational complexity is derived. Simulation results demonstrate that the two proposed algorithms are capable of achieving much better performance than that of the existing CS-based multi-user detection algorithms with a similar computational complexity.

Dynamic Compressive Sensing-Based Multi-User Detection for Uplink Grant-Free NOMA

Non-orthogonal multiple access (NOMA) can support more users than OMA techniques using the same wireless resources, which is expected to support massive connectivity for Internet of Things in 5G. Furthermore, in order to reduce the transmission latency and signaling overhead, grant-free transmission is highly expected in the uplink NOMA systems, where user activity has to be detected. In this letter, by exploiting the temporal correlation of active user sets, we propose a dynamic compressive sensing (DCS)-based multi-user detection (MUD) to realize both user activity and data detection in several continuous time slots. In particular, as the temporal correlation of the active user sets between adjacent time slots exists, we can use the estimated active user set in the current time slot as the prior information to estimate the active user set in the next time slot. Simulation results show that the proposed DCS-based MUD can achieve much better performance than that of the conventional CS-based MUD in NOMA systems.

Efficient Detectors for Joint Compressed Sensing Detection and Channel Decoding

In slotted random access of many nodes, multi-user detection (MUD) can be applied to handle collisions. One novel PHY layer approach for jointly detecting activity and data in such a setting is Compressed Sensing based Multi-User Detection (CS-MUD). In this paper, we first summarize previous investigations on CS-MUD and subsequently propose two novel solutions for problems which have not yet been fully addressed: Firstly, we improve on previous results, by introducing a new approach which incorporates the channel decoder into the Compressed Sensing (CS) detector. Secondly, we analyze the resource efficiency of CS-MUD by adapting phase diagrams known from CS literature to the application of sporadic communication.