Joint channel estimation and activity detection for multiuser communication systems

IEEE J. Sel. Top. Signal Process.

Hanzo

2019

The requirements to support massive connectivity and low latency in massive Machine Type Communications (mMTC) bring a huge challenge in the design of its random access (RA) procedure, which usually calls for efficient joint active user detection and channel estimation. In this paper, we exploit the vast sequence space and the beneficial nested structure of the length-2 m second-order Reed-Muller (RM) sequences for designing an efficient RA scheme, which is capable of reliably detecting multiple active users from the set of unknown potential users with a size as large as 2 m(m−1)/2 , whilst simultaneously estimating their channel state information as well. Explicitly, at the transmitter each user is mapped to a specially designed RM sequence, which facilitates reliable joint sequence detection and channel estimation based on a single transmission event. To elaborate, as a first step, at the receiver we exploit the elegant nested structure of the RM sequences using a layer-by-layer RM detection algorithm for the single-user (single-sequence) scenario. Then an iterative RM detection and channel estimation algorithm is conceived for the multi-user (multi-sequence) scenario. As a benefit of the information exchange between the RM sequence detector and channel estimator, a compelling performance vs. complexity trade-off is struck, as evidenced both by our analytical and numerical results.

Section: B Related Workmentioning

confidence: 99%

Joint Active User Detection and Channel Estimation in Massive Access Systems Exploiting Reed–Muller Sequences

Wang

IEEE J. Sel. Top. Signal Process.

Hanzo

2019

“…W l γ , W one→h , and W l vγ→h are diagonal weighting matrices with size d c K × d c K. Layer 5: The weighted output message vectorγ l = {γ l k , ∀k} is derived as follows, and O l A m 2 , whose elements correspond to the terms in the cumulative summation in (11). (20) where P = {P n,kd , ∀n, k, d} is a row vector with length L t d c K.…”

Section: B Weighted Message Passingmentioning

confidence: 99%

“…4. Withλ l−1 , v l−1 δ→z , m l−1 δ→z in the (l − 1)-th iteration and v l h , m l h in the l-th iteration, update v l δ→z and m l δ→z by (20) and (21). 5.…”

Section: With V Lmentioning

confidence: 99%

DNN-Aided Block Sparse Bayesian Learning for User Activity Detection and Channel Estimation in Grant-Free Non-Orthogonal Random Access

IEEE Trans. Veh. Technol.

Liu

Huang

et al. 2019

In the upcoming Internet-of-Things (IoT) era, the communication is often featured by massive connection, sporadic transmission, and small-sized data packets, which poses new requirements on the delay expectation and resource allocation efficiency of the Random Access (RA) mechanisms of the IoT communication stack. A grant-free non-orthogonal random access (NORA) system is considered in this paper, which could simultaneously reduce the access delay and support more Machine Type Communication (MTC) devices with limited resources. In order to address the joint user activity detection (UAD) and channel estimation (CE) problem in the grant-free NORA system, we propose a deep neural network-aided message passing-based block sparse Bayesian learning (DNN-MP-BSBL) algorithm. In the DNN-MP-BSBL algorithm, the iterative message passing process is transferred from a factor graph to a deep neural network (DNN). Weights are imposed on the messages in the DNN and trained to minimize the estimation error. It is shown that the trained weights could alleviate the convergence problem of the MP-BSBL algorithm, especially on crowded RA scenarios. Simulation results show that the proposed DNN-MP-BSBL algorithm could improve the UAD and CE accuracy with a smaller number of iterations, indicating its advantages for low-latency grant-free NORA systems.

“…For example, the joint UAD and CE problem was addressed by a modified Bayesian compressed sensing algorithm [5] for the cloud radio access network (C-RAN). In addition, the powerful approximate message passing (AMP) algorithm was employed for the joint UAD and CE problem when the BS is equipped either with a single antenna [6], [7] or with multiple antennas [8].…”

Section: A Grant-based Random Accessmentioning

confidence: 99%

DNN-Aided Message Passing Based Block Sparse Bayesian Learning for Joint User Activity Detection and Channel Estimation

2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)

Liu

Huang

et al. 2019

Faced with the massive connection, sporadic transmission, and small-sized data packets in future cellular communication, a grant-free non-orthogonal random access (NORA) system is considered in this paper, which could reduce the access delay and support more devices. In order to address the joint user activity detection (UAD) and channel estimation (CE) problem in the grant-free NORA system, we propose a deep neural network-aided message passing-based block sparse Bayesian learning (DNN-MP-BSBL) algorithm. In this algorithm, the message passing process is transferred from a factor graph to a deep neural network (DNN). Weights are imposed on the messages in the DNN and trained to minimize the estimation error. It is shown that the weights could alleviate the convergence problem of the MP-BSBL algorithm. Simulation results show that the proposed DNN-MP-BSBL algorithm could improve the UAD and CE accuracy with a smaller number of iterations.Index Terms-deep neural network, sparse Bayesian learning, grant-free, user activity detection, channel estimation arXiv:1910.04154v1 [cs.IT]