Asynchronous Activity Detection for Cell-Free Massive MIMO: From Centralized to Distributed Algorithms

Abstract

Device activity detection in the emerging cell-free massive multiple-input multiple-output (MIMO) systems has been recognized as a crucial task in machine-type communications, in which multiple access points (APs) jointly identify the active devices from a large number of potential devices based on the received signals. Most of the existing works addressing this problem rely on the impractical assumption that different active devices transmit signals synchronously. However, in practice, synchronization cannot be guaranteed due to the low-cost oscillators, which brings additional discontinuous and nonconvex constraints to the detection problem. To address this challenge, this paper reveals an equivalent reformulation to the asynchronous activity detection problem, which facilitates the development of a centralized algorithm and a distributed algorithm that satisfy the highly nonconvex constraints in a gentle fashion as the iteration number increases, so that the sequence generated by the proposed algorithms can get around bad stationary points. To reduce the capacity requirements of the fronthauls, we further design a communication-efficient accelerated distributed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms outperform state-of-the-art approaches, and the proposed accelerated distributed algorithm achieves a close detection performance to that of the centralized algorithm but with a much smaller number of bits to be transmitted on the fronthaul links.

Clustering and Beamforming for User-Centric Cell-Free Massive MIMO With Backhaul Capacity Limitation

“…Furthermore, all CPUs are connected with each other via wired backhaul links with capacity limitations. Note that the assumption of singleantenna UEs is common in CF-mMIMO papers thanks to the degree of freedom at multi-antenna APs [42], [43], [44]. In addition, to guarantee scalability, the system adopts usercentric clustering, as illustrated in Fig 2 . Although there are multiple CPUs in the area, the signals of the kth UE are processed at its sole host CPU.…”

Joint Activity Detection and Channel Estimation for Clustered Massive Machine Type Communications

“…Notably, [23], [28]- [30], [32], [35] all assume that devices are synchronized, which inspired Li et al [36] to propose an asynchronous device activity detection in CF-MIMO systems where communication between the BSs and the CPU is optimized. From the results of [36], it is apparent that one of the bottlenecks of decentralized algorithms is the communication overhead incurred by increased signaling between the different sub-processors. In a similar vein, Chen et al in [37] proposed a structured massive access for CF-MIMO using the per group and the IB-K-means clustering algorithms.…”

Centralized and Decentralized IDD Schemes for Cell-Free Massive MIMO Systems: AP Selection and LLR Refinement

“…To address this issue, an efficient receiver design is necessary [1], [9], [11]- [22]. Prior works on receiver design for CF-mMIMO [1], [2], [9], [10] have studied linear receivers such as receive matched filter (RMF) and minimum mean square error (MMSE) receivers for uncoded systems. However, the MUI remains constant even in high SNR regimes.…”

“….., σ 2 K denotes the covariance matrix that consists of the entries computed in (10). By substituting ( 16) and ( 17) into (15), the centralized MMSE filter is given by…”

“…where ∆ il denotes the covariance matrix that consists of the entries computed in (10), locally computed at the l-th AP. By substituting ( 29) and ( 30) into (28), the solution of the local receive filter w * kl is given by…”