Joint Channel Estimation and Multiuser Detection for Uplink Grant-Free NOMA

Du, Yang; Dong, Binhong; Zhu, Wuyong; Gao, Pengyu; Chen, Zhi; Wang, Xiaodong; Fang, Jun

doi:10.1109/lwc.2018.2810278

Cited by 94 publications

(79 citation statements)

References 7 publications

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“…Further gains are possible, e.g., by using more advanced joint multi-user detection algorithms and multiple receive antennas to separate the superimposed signals. Note that user activity detection and channel estimation are required before the data detection, but this may also be done jointly [13]. For block-fading channels, there is further a tradeoff between the diversity achieved by using multiple slots and the required channel estimation overhead [14].…”

Section: B Grant-free Noma With Advanced Receiversmentioning

confidence: 99%

Uplink Grant-Free Access Solutions for URLLC services in 5G New Radio

Mahmood

Abreu

Böhnke

et al. 2019

2019 16th International Symposium on Wireless Communication Systems (ISWCS)

108

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The newly introduced ultra-reliable low latency communication service class in 5G New Radio depends on innovative low latency radio resource management solutions that can guarantee high reliability. Grant-free random access, where channel resources are accessed without undergoing assignment through a handshake process, is proposed in 5G New Radio as an important latency reducing solution. However, this comes at an increased likelihood of collisions resulting from uncontrolled channel access, when the same resources are preallocated to a group of users. Novel reliability enhancement techniques are therefore needed. This article provides an overview of grant-free random access in 5G New Radio focusing on the ultra-reliable low latency communication service class, and presents two reliability-enhancing solutions. The first proposes retransmissions over shared resources, whereas the second proposal incorporates grant-free transmission with non-orthogonal multiple access with overlapping transmissions being resolved through the use of advanced receivers. Both proposed solutions result in significant performance gains, in terms of reliability as well as resource efficiency. For example, the proposed non-orthogonal multiple access scheme can support a normalized load of more than 1.5 users/slot at packet loss rates of ∼ 10 −5 − a significant improvement over the maximum supported load with conventional grant-free schemes like slotted-ALOHA. Index TermsURLLC, Grant-free random access, 5G NR, NOMA.

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Section: B Grant-free Noma With Advanced Receiversmentioning

confidence: 99%

Uplink Grant-Free Access Solutions for URLLC services in 5G New Radio

Mahmood

Abreu

Böhnke

et al. 2019

2019 16th International Symposium on Wireless Communication Systems (ISWCS)

108

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“…In our analysis, we measure the complexity in terms of the number of floating point operations (flops). Initially, in the FC layer, the input vectorŷ ∈ R 2m×1 is multiplied by the initial weight W in ∈ R α×2m and the bias b in ∈ R α×1 is added (see (9)). The complexity of the initial FC layer C in is…”

Section: Comments On Complexitymentioning

confidence: 99%

“…Numerical results demonstrate that the proposed AUD scheme outperforms the conventional approaches in both AUD success probability and computational complexity.By exploiting the fact that only a few active devices in a cell transmit the information concurrently (see Fig. 1), the AUD problem can be readily formulated as a sparse recovery problem [8], [9]. Since the transmit vector is sparse, compressed sensing (CS) technique has been popularly employed [10], [11].…”

mentioning

confidence: 99%

Deep Neural Network-Based Active User Detection for Grant-Free NOMA Systems

Kim

Ahn

Shim

2020

IEEE Trans. Commun.

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As a means to support the access of massive machine-type communication devices, grant-free access and non-orthogonal multiple access (NOMA) have received great deal of attention in recent years. In the grant-free transmission, each device transmits information without the granting process so that the basestation needs to identify the active devices among all potential devices. This process, called an active user detection (AUD), is a challenging problem in the NOMA-based systems since it is difficult to identify active devices from the superimposed received signal. An aim of this paper is to put forth a new type of AUD based on deep neural network (DNN). By applying the training data in the properly designed DNN, the proposed AUD scheme learns the nonlinear mapping between the received NOMA signal and indices of active devices. As a result, the trained DNN can handle the whole AUD process, achieving an accurate detection of the active users. Numerical results demonstrate that the proposed AUD scheme outperforms the conventional approaches in both AUD success probability and computational complexity.By exploiting the fact that only a few active devices in a cell transmit the information concurrently (see Fig. 1), the AUD problem can be readily formulated as a sparse recovery problem [8], [9]. Since the transmit vector is sparse, compressed sensing (CS) technique has been popularly employed [10], [11]. In [8], the AUD problem is modeled as a single measurement vector (SMV) problem and MPA is used to solve the problem. In this CS-based AUD scheme, basestation detects devices based on the correlation between the received signal and device specific sequence. However, performance of the CS-based AUD is not that appealing when the columns of a system matrix (a.k.a. sensing matrix) are highly correlated and sparsity (the number of nonzero elements) of the underlying input vector increases. In fact, in the practical NOMAbased transmission, correlation among the NOMA sequences and also device activity (sparsity) are relatively high so that the CS-based AUD might not be effective. Indeed, it has been shown that the performance of the sparse recovery algorithm is degraded significantly when the mutual correlation and sparsity increase [11]. Therefore, it is of importance to come up with a new type of AUD scheme suitable for the overloaded yet less sparse access scenarios.An aim of this paper is to pursue an entirely different approach to detect active users in the grant-free NOMA scenario. For an efficient and accurate AUD, we exploit the deep neural network (DNN), a learning-based tool to approximate the complicated and nonlinear function.Over the years, DNN has been successfully applied in numerous applications such as image classification [12], machine translation [13], automatic speech recognition [14], and Go game [15].Recently, DNN has been also applied to various wireless systems such as multiple-input and multiple-output (MIMO) detection, wireless scheduling, and direction-of-arrival (DoA) estimation [16]. In these w...

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“…Fig. 1 shows the example of the standard Throughout the paper, we make the following assumptions [34], [36].…”

Section: A Grant-free Noma Transmissionmentioning

confidence: 99%

“…In Fig. 5, we compare the AER and SER performance of our proposed algorithms with the state-of-the-art BSASP algorithm in [36]. For comparison, we also include the performance of the two-phase detection scheme [30] in which the receiver first estimates the user activities and channel coefficients jointly, and then recovers the signals sent by the active users, both through AMP algorithms.…”

Section: A Error Rate Performancementioning

confidence: 99%

Joint User Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA

Jiang

Yuan²,

Wang

et al. 2019

2019 IEEE Global Communications Conference (GLOBECOM)

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For massive machine-type communications, centralized control may incur a prohibitively high overhead. Grant-free non-orthogonal multiple access (NOMA) provides possible solutions, yet poses new challenges for efficient receiver design. In this paper, we develop a joint user identification, channel estimation, and signal detection (JUICESD) algorithm. Specifically, we divide the whole detection scheme into linear and non-linear modules. Then we handle the linear module by leveraging the existing approximate message passing (AMP) algorithms, and deal with the non-linear module based on generalized messaging passing. The exact calculation of the messages exchanged within the non-linear module and between the two modules is complicated due to phase ambiguity issues. By noticing that the messages under phase ambiguity exhibit a rotational invariance property, we propose a rotationally invariant Gaussian mixture (RiGm) model, and develop an efficient JUICESD-RiGm algorithm. JUICESD-RiGm achieves a performance close to JUICESD with a much lower complexity.Capitalizing on the feature of RiGm, we further analyze the performance of JUICESD-RiGm with state evolution techniques. Numerical results demonstrate that the proposed algorithms achieve a significant performance improvement over the existing alternatives, and even outperform oracle linear minimum mean square error (LMMSE) receivers; and the derived state evolution method predicts the system performance accurately. be accurately characterized by the state evolution [24]. Furthermore, sparse signal recovery algorithms for more general system models have been developed recently, including turbo compressed sensing [16], orthogonal AMP (OAMP) [17] and vector AMP (VAMP) [18] for linear systems with a non-i.i.d. sensing matrix, generalized AMP (GAMP) [20], [21] for systems with non-linear output, and bilinear GAMP (BiGAMP) [22], [23] for bi-linear systems. These message passing based algorithms provide the current state of the art for sparse signal reconstruction. Based on aforementioned algorithms, joint designs of channel estimation, user identification, and/or signal detection have been pursued to improve the system performance. Specifically, under the assumption of perfect channel state information (CSI) at the receiver (CSIR), joint active user identification and signal detection algorithms were developed in [25], [26]. For systems without CSIR, [27]-[30] established joint channel estimation and active user identification algorithms, followed by separated signal detection operations. In addition, joint channel and data estimation algorithms were developed for massive MIMO systems [31]-[34] and for single carrier systems [35]. Recently, [36] proposed a joint channel estimation and multiuser detection algorithm, named block sparsity adaptive subspace pursuit (BSASP). This algorithm transfers the single-measurementvector compressive sensing (SMV-CS) problem to multiple-measurement-vector compressive sensing (MMV-CS), and reconstructs the sparse signal by exploiting the in...

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Joint Channel Estimation and Multiuser Detection for Uplink Grant-Free NOMA

Cited by 94 publications

References 7 publications

Uplink Grant-Free Access Solutions for URLLC services in 5G New Radio

Uplink Grant-Free Access Solutions for URLLC services in 5G New Radio

Deep Neural Network-Based Active User Detection for Grant-Free NOMA Systems

Joint User Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA

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