Uplink Grant-free Non-orthogonal Multiple Access Research Articles

New Constructions of Binary Golay Spreading Sequences for Uplink Grant-Free NOMA

Non-orthogonal multiple access (NOMA) is a promising technology for massive machine-type communications (mMTC). This letter focuses on constructing spreading sequences with low coherence as well as low peak-to-average power ratio (PAPR) for uplink grant-free NOMA. Recently, a framework of spreading matrices from Boolean functions was provided by Yu. Exploiting this framework, we first show that optimum coherence of the constructed spreading matrices can be achieved if the difference of any two distinct Boolean functions is a bent (or almost bent) function. With this result, explicit constructions of Boolean functions are then developed, which lead to new binary Golay spreading matrices with optimum coherence and yielding PAPR of each spreading sequence at most 2. Simulation results demonstrate that such spreading sequences are suitable for uplink grant-free access. Moreover, the proposed Golay spreading sequences are capable of accommodating up to 400% overloaded devices, which can not be achieved from the existing algebraic methods.

Low-Complexity Channel Estimation and Multi-User Detection for Uplink Grant-Free NOMA Systems

Grant-free non-orthogonal multiple access (NOMA) scheme is a promising candidate to accommodate massive connectivity with reduced signalling overhead for Internet of Things (IoT) services in massive machine-type communication (mMTC) networks. In this letter, we propose a low-complexity compressed sensing (CS) based sparsity adaptive block gradient pursuit (SA-BGP) algorithm in uplink grant-free NOMA systems. Our proposed SA-BGP algorithm is capable of jointly carrying out channel estimation (CE), user activity detection (UAD) and data detection (DD) without knowing the user sparsity level. By exploiting the inherent sparsity of transmission signal and gradient descend, our proposed method can enjoy a decent detection performance with substantial reduction of computational complexity. Simulation results demonstrate that the proposed method achieves a balanced trade-off between computational complexity and detection performance, rendering it a viable solution for future IoT applications.

Compressed Sensing Based Multiuser Detection of Grant-Free NOMA With Dynamic User Activity

In this letter, by exploiting both the temporal correlation of active user and signal statistical information, we propose a compressed sensing based multi-user detection for uplink grant-free non-orthogonal multiple access where the user activity varies dynamically in one frame consisting of multiple time slots. In the proposed method, channel estimation and data detection are implemented jointly by efficient slot-wise generalized approximate message passing (GAMP) algorithm, active user detection (AUD) is implemented by message passing (MP) algorithm, and GAMP and MP exchanges information through turbo iterations. Specifically, the user activity in each time slot and active user correlation in one frame are introduced as variable and check nodes, respectively, to tackle the dynamic user activity directly in the factor graph for MP-based AUD. Finally, the performance of the proposed method is demonstrated by computer simulations.

Forward-Reverse Orthogonal Matching Pursuit-Union-Subspace Pursuit-Based Multiuser Detector for Uplink Grant-Free NOMA Networks

Multiuser Detection (MUD) is quite challenging in uplink grant-free non-orthogonal multiple access wireless communication networks in which users sporadically transmit data. The reason for this is that the base station (BS) must perform detection of both multiuser activity and user signals concurrently, because knowledge of user activity status is not available at the BS. In this paper, a new multiuser detector, named the Forward-Reverse Orthogonal Matching Pursuit–Union–Subspace pursuit (FROMPUS)-based MUD, is proposed. The detector takes advantage of the concept of an initial support set. This serves as initial knowledge that is then employed in the reconstruction of active users’ signals. In addition, the detector uses the “serial-include” technique of incorporating a likely support set element candidates and a reliability testing procedure in which the most prominent elements of the support set are selected. To assess the performance of the proposed detector, computer simulations are performed. The results obtained for various parameter settings show that the FROMPUS performs better than any of the other five detectors considered in this paper. However, this excellent performance comes with a slightly higher computational complexity cost. Nonetheless, the cost is inconsequential, since the detector operates at the BS where complexity is of low priority in comparison to performance.

Distributed Q-Learning Aided Uplink Grant-Free NOMA for Massive Machine-Type Communications

The explosive growth of machine-type communications (MTC) devices poses critical challenges to the existing cellular networks. Therefore, how to support massive MTC devices with limited resources is an urgent problem to be solved. Bursty traffic is an important characteristic of MTC devices, which makes it difficult for agents to learn useful experience and has a negative impact on model convergence. However, most existing reinforcement learning-based literatures assume that devices have saturate data. Towards this end, we propose two distributed Q-learning aided uplink grant-free non-orthogonal multiple access (NOMA) schemes (including all-devices distributed Q-learning (ADDQ) scheme and portion-devices distributed Q-learning (PDDQ) scheme) to maximize the number of accessible devices, where the bursty traffic of massive MTC devices is carefully considered. In order to reduce the dimension of scheduling space and mitigate the impact of bursty traffic, the idea of grouping devices as well as transmission resources and the intermittent learning mode are adopted in our schemes. Extensive numerical results demonstrate the advantages of proposed schemes from multiple perspectives.

CS-Based Near-Optimal MUD for Uplink Grant-Free NOMA

In order to furnish connectivity to an incredible number of devices and to satisfy the capacity demands, non-orthogonal multiple access (NOMA) is considered as a hopeful solution for future 5G. Moreover, grant-free transmission is substantially expected in the uplink NOMA to minimize the latency time and detection overhead. However, multi-user detection (MUD) without user-activity information in uplink grant-free NOMA is hard in practice. In this paper, by benefit from the user-activity sparsity existing in uplink grant-free NOMA, the multipath matching pursuit (MMP) is used for user-activity detection, and then message passing algorithm (MPA) can be efficiently applied for active users’ data detection. Simulation results show that the proposed MMP-MAP MUD with acceptable complexity can achieve better performance than conventional solutions. Also, the MMP-MAP detector reaches the performance obtained by the MPA detector with perfect user-activity information once the level of signal-to-noise ratio exceed 11 dB.

Multiuser Detection Schemes Based on Families of OMP and MUSIC Algorithms for Uplink Grant Free NOMA Wireless Communications Systems

Background and Objective: The Non-Orthogonal Multiple Access (NOMA) technology is a multi-access scheme that overcomes most of the disadvantages of its predecessor, the OMA technology. Specifically, NOMA technology supports massive connectivity of multiple users by employing the same non-orthogonal spectrum resource. In an uplink NOMA system with grant-free transmission mode, the Base Station (BS) is unaware of which users are active in the networks at a given time. Consequently, there is a need for mechanism to ensure successful recovery of users’ transmitted signals. This paper presents some new Multiuser Detector (MUD) schemes for uplink grant-free NOMA wireless communication networks with system’s model involving Multiple Measurement Vectors (MMV) rather than the Single Measurement Vector (SMV) that many previous works have considered. These MUDs include those that are based on differential Orthogonal Matching Pursuit (OMP), adaptive Simultaneous OMP (SOMP), compressive- multiple signal classification (MUSIC), and sequential compressive-MUSIC algorithms. Methods: The MUDs are employed in the detection of users’ signals in the uplink NOMA systems. Results and Discussion: Comparative performances of these MUDs with another one that is also based on the MMV system model, the SOMP-based MUD are presented for the scenarios when the system is under-loaded, fully loaded and over-loaded. Conclusion: The results suggest that the sequential compressive-MUSIC-based MUD, though shows weak performance at lower range of SNR, outperforms all the other MUDs including the SOMP-based MUD at higher SNR. Its performance is quite outstanding during the over-loaded scenarios, especially at higher SNR. However, its computational complexity is higher that the closely performing compressive-MUSIC-based MUD and SOMP-based MUD.

An Efficient Matching Pursuit Based Compressive Sensing Detector For Uplink Grant-Free NOMA

In this paper, an efficient compressive sensing detector is proposed for uplink grant-free non-orthogonal multiple access (NOMA), where the indices and symbols of the active users are detected separately by using efficient matching pursuit (MP) based algorithms. Specifically, a low-complexity subspace MP (SMP) algorithm is designed for detecting the active user's indices, which is helpful for obtaining an initial estimated symbol vector. Then an efficient MP based symbol correcting mechanism (SCM) is designed for finding and correcting the erroneous results encountered in the initial result. Simulation results indicate that our proposed SMP based SCM (SMP-SCM) algorithm is capable of exhibiting better bit error rate (BER) performance over the oracle least squares (OLS) counterpart in the high SNR regime, which is always taken as a benchmark in conventional multi-user detection of grant-free NOMA system.

Sequence Design and User Activity Detection for Uplink Grant-Free NOMA in mMTC Networks

Massive machine type communications (mMTC) is one of the three major scenarios in 5G wireless networks. mMTC is an uplink-dominant scenario that supports massive connectivity of small-size packets and sporadic traffic. To meet the requirement of mMTC, non-orthogonal multiple access (NOMA) with grant-free transmission is considered a promising technology because of its higher spectrum efficiency and lower signaling overhead. Request-and-grant-based scheduling process is eliminated in grant-free transmission and thus the base stations need to identify active users first before data detection. Previously, several user activity detection (UAD) algorithms were proposed and they can obtain good performance in flat-fading channels. However, the performance of these works degrades significantly in doubly selective fading channels. In this work, we propose an effective solution for uplink grant-free NOMA with a new user sequence scheme and a novel UAD algorithm suitable for practical channel scenarios. Simulation results validated that the proposed sequences can mitigate the inter-user interference at the base stations, and the proposed UAD algorithm can achieve better detection accuracy. Moreover, detailed complexity analysis showed that the proposed UAD algorithm has relatively low complexity and good scalability.

Binary Golay Spreading Sequences and Reed-Muller Codes for Uplink Grant-Free NOMA

Non-orthogonal multiple access (NOMA) is an emerging technology for massive connectivity in machine-type communications (MTC). In code-domain NOMA, non-orthogonal spreading sequences are uniquely assigned to all devices, where active ones attempt a grant-free access to a system. In this paper, we study a set of user-specific, non-orthogonal, binary spreading sequences for uplink grant-free NOMA. Based on binary Golay complementary sequences, each spreading sequence provides the peak-to-average power ratio (PAPR) of at most 3 dB for multicarrier transmission. Exploiting the theoretical connection to Reed-Muller codes, we conduct a probabilistic analysis to search for a permutation set for Golay sequences, which presents theoretically bounded low coherence for the spreading matrix. Simulation results confirm that the maximum PAPR of transmitted multicarrier signals via the spreading sequences is significantly lower than those for random bipolar, Gaussian, and pseudo-random sequences. Thanks to the low coherence, the performance of compressed sensing (CS) based joint channel estimation and multiuser detection using the spreading sequences turns out to be superior or similar to those for random bipolar, Gaussian, pseudo-random, and Zadoff-Chu sequences. The binary Golay spreading sequences have only two phases regardless of the sequence length, which can make them suitable for low cost MTC devices.

Active user and data detection for uplink grant-free NOMA systems

This paper proposes some low complexity algorithms for active user detection (AUD), channel estimation (CE) and multi-user detection (MUD) in uplink non-orthogonal multiple access (NOMA) systems, including single-carrier and multi-carrier cases. In particular, we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers (ADMM), where fast decaying feature of non-zero components in sparse signal is considered. More importantly, the reliable estimated information is used for AUD, and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users. Then, the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure. Besides, we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users' data, which avoids the Hessian matrix inverse. The convergence and the complexity of proposed algorithms are analyzed and discussed finally. Simulation results show that the proposed algorithms have better performance in terms of AUD, CE and MUD. Moreover, we can detect active users perfectly for multi-carrier NOMA system.

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.

Deep Reinforcement Learning for Throughput Improvement of the Uplink Grant-Free NOMA System

Facing the dramatic increase of mobile devices and the scarcity of spectrum resources, grant-free nonorthogonal multiple access (NOMA) emerges as an enabling technology for massive access, which also reduces signaling overhead and access latency effectively. However, in grant-free NOMA systems, the collisions resulting from uncoordinated resource selection can cause severe interference and reduce system throughput. In this article, we apply deep reinforcement learning (DRL) in the decision making for grant-free NOMA systems, to mitigate collisions and improve the system throughput in an unknown network environment. To reduce collisions in the frequency domain and the computational complexity of DRL, subchannel and device clustering are first designed, where a cluster of devices compete for a cluster of subchannels following grant-free NOMA. Furthermore, discrete uplink power control is proposed to reduce intracluster collisions. Then, the long-term cluster throughput maximization problem is formulated as a partially observable Markov decision process (POMDP). To address the POMDP, a DRL-based grant-free NOMA algorithm is proposed to learn about the network contention status and output subchannel and received power-level selection with less collisions. The numerical results verify the effectiveness of the proposed algorithm and reveal that DRL-based grant-free NOMA outperforms slotted ALOHA NOMA with 32.9% and 156% performance gain on the system throughput when the number of devices is twice and five times that of the subchannels, respectively. When the number of devices is five times that of the subchannels, the success access probability of DRL-based grant-free NOMA is above 85%, compared to 33% in the slotted ALOHA NOMA system.

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.

Compressive Sensing for Joint User Activity and Data Detection in Grant-Free NOMA

This letter studies the joint user activity and data detection problem in an uplink grant-free non-orthogonal multiple access system with binary phase-shift keying modulation. First, we propose a new compressive sensing (CS)-based algorithm, namely the information-enhanced adaptive matching pursuit (IE-AMP), which exploits both the frame-wise user sparsity and the ternary nature of transmit signals, to increase the detection performance with reduced computation complexity. Next, building upon this method, we propose a robust IE-AMP (RIE-AMP) algorithm for further performance improvement. The RIE-AMP first extracts the support information (i.e., the number of nonzero elements in every row) of the output signals of IE-AMP, and then employs a weighted $ {\ell }_{ {1, 2}}$ minimization to refine the result. Numerical results show that the IE-AMP outperforms several state-of-the-art CS-based algorithms, with reduced computational efforts required, while the RIE-AMP achieves a better and more robust detection performance than the IE-AMP at the cost of higher computation complexity.

Grant-Free Non-Orthogonal Multiple Access With Multiple-Antenna Base Station and Its Efficient Receiver Design

This paper investigates an uplink grant-free non-orthogonal multiple access (NOMA) system with a multiple-antenna base station (BS), in which each user autonomously transmits its own data by multiplying its unique but non-orthogonal spreading sequence, namely code-domain NOMA, and proposes two receivers efficiently performing active user detection (AUD), channel estimation (CE), and data detection, simultaneously. We first propose a receiver based on multiple measurement vector approximate message passing (MMV-AMP), and the threshold for AUD is theoretically analyzed. Numerical results show that the symbol error rate (SER) of our receiver with the designed threshold is comparable to that of grant NOMA in which active users are ideally known. To reduce the complexity, we further propose a receiver based on Boosted AMP, which converts the original multiple-vector reconstruction problem into a smaller-sized vector reconstruction problem. Upon AUD via Boosted AMP, this receiver performs both CE and data detection via well-known linear minimum mean squared error (MMSE). Computer simulations reveal that our low-complexity receiver exhibits SER performance comparable to that of a conventional scheme based on block sparse adaptive subspace pursuit (BSASP), while the complexity is significantly reduced.

Block Sparse Bayesian Learning Based Joint User Activity Detection and Channel Estimation for Grant-Free NOMA Systems

This paper concerns uplink grant-free nonorthogonal multiple access, where the handshaking procedure is not required to reduce control signaling overhead and transmission latency. In especially the dynamic scenarios, e.g., Internet of vehicles, the active users have to be identified and their channel state information needs to be estimated before performing multiuser detection. We investigate the joint user activity detection (UAD) and channel estimation (CE), which provides necessary information for data detection. In this paper, the joint UAD and CE is formulated as a block sparse signal recovery problem. First, the block orthogonal matching pursuit (BOMP) algorithm is studied for this problem, but its complexity grows with the fourth power of active user number, which hinders its application. Then, block sparse Bayesian learning (BSBL) is investigated to solve this problem, and in particular a low complexity message passing based implementation of BSBL with belief propagation and mean field is developed. The proposed message passing based BSBL (MP-BSBL) algorithm has a complexity independent of active user number, which can be significantly lower than that of the BOMP algorithm. In addition, MP-BSBL provides an estimate of the noise power, which can be readily used for data detection. Simulation results show that the MP-BSBL algorithm delivers almost the same performance as BOMP with the exact knowledge of active user number and can reach the performance bound for channel estimation.

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.

Approximate Message Passing-Based Joint User Activity and Data Detection for NOMA

This letter focuses on joint user activity and data detection in the uplink grant-free non-orthogonal multiple access systems based on approximate message passing (AMP) and expectation maximization (EM) algorithms. The proposed Joint-EM-AMP detection algorithm consists of three steps in each iteration. First, AMP decouples the superimposed received signal into uncoupled scalar problems. Then, at the denoising step, AMP computes the posterior means and variances of the transmitted symbols with the extended modulation constellation. The third step is to estimate user activity parameters using EM based on the frame-wise joint sparsity of user activity. In contrast to existing state-of-the-art algorithms, the proposed Joint-EM-AMP algorithm demonstrates significant performance gain in terms of bit error rate, which will be verified in simulation results.