Grant-free Non-orthogonal Multiple Access System Research Articles

Constructions of Low/Zero Correlation Zone Sequence Sets and Their Application in Grant-Free Non-Orthogonal Multiple Access System

Constructions of Low/Zero Correlation Zone Sequence Sets and Their Application in Grant-Free Non-Orthogonal Multiple Access System

Deep Reinforcement Learning-Based Grant-Free NOMA Optimization for mURLLC

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential technique to support massive Ultra-Reliable and Low-Latency Communication (mURLLC) service. However, the dynamic resource configuration in GF-NOMA systems is challenging due to random traffics and collisions, that are unknown at the base station (BS). Meanwhile, joint consideration of the latency and reliability requirements makes the resource configuration of GF-NOMA for mURLLC more complex. To address this problem, we develop a novel learning framework for signature-based GF-NOMA in mURLLC service taking into account the multiple access signature collision, the UE detection, as well as the data decoding procedures for the K-repetition GF and the Proactive GF schemes. The goal of our learning framework is to maximize the long-term average number of successfully served users (UEs) under the latency constraint. We first perform a real-time repetition value configuration based on a double deep Q-Network (DDQN) and then propose a Cooperative Multi-Agent learning technique based DQN (CMA-DQN) to optimize the configuration of both the repetition values and the contention-transmission unit (CTU) numbers. Our results show the superior performance of CMA-DQN over the conventional load estimation-based uplink resource configuration approach (LE-URC) in heavy traffic and demonstrate its capability in dynamically configuring in long term for mURLLC service. In addition, with our learning optimization, the Proactive scheme always outperforms the K-repetition scheme in terms of the number of successfully served UEs, especially under the high backlog traffic scenario.

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.

Dynamic User Activity and Data Detection for Grant-Free NOMA via Weighted ℓ2,1 Minimization

Grant-free non-orthogonal multiple access (NOMA) has recently received wide attention for reducing signaling overhead and transmission latency in massive machine-type communications (mMTC). In grant-free NOMA systems, user activity and data (UAD) has to be detected, which is challenging in practice. As an emerging technique, compressive sensing (CS) shows great promise in solving this problem by exploiting the inherent sparsity nature of user activity. This paper proposes to use the weighted <inline-formula> <tex-math notation="LaTeX">$\ell _{2, 1}$ </tex-math></inline-formula> minimization (WL21M) to jointly detect UAD in realistic dynamic scenarios. At first, the average recoverability of the WL21M is analyzed. This analysis reveals the fact that the WL21M can improve the detection performance by means of an appropriate weighting and the incorporation of intrinsic temporal correlation. Motivated by the analysis, a collaborative hierarchical match pursuit (C-HiMP) algorithm is proposed for dynamic UAD detection. In the C-HiMP, a sequence of WL21M problems are solved in the subspaces spanned by all of the components in the hierarchical estimated support sets, where the weights are collaboratively updated by the solutions in previous time slots so that an attractive self-correction capacity is obtained. Simulation results demonstrate that the proposed C-HiMP can obtain significant performance improvements, in terms of detection accuracy and computational complexity, compared with several state-of-the-art CS-based detection algorithms.

DDI: A Novel Architecture for Joint Active User Detection and IoT Device Identification in Grant-Free NOMA Systems for 6G and Beyond Networks

Nonorthogonal multiple access (NOMA) with a grant-free access has received a lot of attention due to its support to massive machine-type communication (mMTC) devices. The devices in grant-free systems are allowed to transmit information without undergoing an authentication process. Therefore, in such systems, the base station needs to distinguish between active and nonactive devices, called the active user detection (AUD) process. This process is challenging as the active device needs to be detected from the received signals that are superimposed. Furthermore, the identification of the Internet of Things (IoT) devices from these signals also poses a great challenge, which could help allocate resources in future generation communication systems. Motivated from the aforementioned facts, this article proposes a device detection and identification (DDI) architecture for joint AUD and IoT device identification from the received superimposed signals. The architecture extracts the Fourier patterns as the representative feature vector, which results in an improved detection and identification process. Experimental results show that the architecture not only outperforms the conventional schemes and deep neural network-based approaches in terms of success probability for the AUD task but also yields lower computational complexity. The evaluation of the DDI architecture for IoT device identification problems has also been performed and compared to various shallow learning methods to prove its efficacy.

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.

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.

AI-enhanced constellation design for NOMA system: A model driven method

Grant-free Non-orthogonal Multiple Access (GF-NOMA) is a promising technology for massive access users and sporadic small-packet transmission for Beyond the 5th Generation mobile communication system(B5G) / the 6th Generation mobile communication system (6G). One of the key aspects in GF-NOMA system is the signature/constellation design. However, due to the channel variation and random activation of users, conventional optimization approaches seem unsuitable for such complex models. In this paper, as an initial attempt, we propose a human intelligence (HI)-guided artificial intelligence (AI)-enhanced signature/constellation design method. By separate design of modulation and power allocation inspired by prior knowledge, the proposed deep neuron network (DNN) for NOMA signature/constellation design not only has smaller size of DNN and less training data, but also has stronger interpret-ability. In the last section, via simulations we demonstrate that in terms of bit error rate, the proposed scheme can achieve significant performance gain over the conventional NOMA schemes.

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.

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.

Novel Transceiver Architecture for an Asynchronous Grant-Free IDMA System

This paper investigates a grant-free non-orthogonal multiple access (NOMA) system where massive number of users wake up and send data right away without performing a grant-based initial access procedure. Since no scheduling grant is used in this system, the base station (BS) does not know which user is transmitting on each resource. Also, due to the lack of an uplink (UL) timing control process, multiple user’s signals are asynchronously received at the BS. This causes asynchronous interference, or multiuser inter-carrier and inter-symbol interference. We propose two ideas to address these two problems. We first propose an auxiliary preamble structure to successfully detect the user activity, even in the presence of a massive number of users. We, then, propose a modification to the interleave-division multiple access (IDMA) receiver to mitigate asynchronous interference. The simulation results show that the proposed scheme significantly improves the preamble detection performance and BER performance compared to the conventional schemes. Furthermore, we show that the proposed grant-free NOMA system can achieve much better performance than the grant-based NOMA system in terms of transmission time and signaling overhead.

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.

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.

Joint User Activity and Data Detection Based on Structured Compressive Sensing for NOMA

Non-orthogonal multiple access (NOMA) has been regarded as one of the promising key technologies for future 5G systems. In the uplink grant-free NOMA schemes, dynamic scheduling is not required, which can significantly reduce the signaling overhead and transmission latency. However, user activity has to be detected in grant-free NOMA systems, which is challenging in practice. In this letter, by exploiting the inherent structured sparsity of user activity naturally existing in NOMA systems, we propose a low-complexity multi-user detector based on structured compressive sensing to realize joint user activity and data detection. In particular, we propose a structured iterative support detection algorithm by exploiting such structured sparsity, which is able to jointly detect user activity and transmitted data in several continuous time slots. Simulation results show that the proposed scheme can achieve better performance than conventional solutions.