Scalable Cell-Free Massive MIMO Unsourced Random Access System

Cell-Free Massive MIMO systems aim to expand the coverage area of wireless networks by replacing a single high-performance Access Point (AP) with multiple small, distributed APs connected to a Central Processing Unit (CPU) through a fronthaul. Another novel wireless approach, known as the unsourced random access (URA) paradigm, enables a large number of devices to communicate concurrently on the uplink. This article considers a quasi-static Rayleigh fading channel paired to a scalable cell-free system, wherein a small number of receive antennas in the distributed APs serve devices equipped with a single antenna each. The goal of the study is to extend previous URA results to more realistic channels by examining the performance of a scalable cell-free system. To achieve this goal, we propose a coding scheme that adapts the URA paradigm to various cell-free scenarios. Empirical evidence suggests that using a cell-free architecture can improve the performance of a URA system, especially when taking into account large-scale attenuation and fading

A Fully Bayesian Approach for Massive MIMO Unsourced Random Access

In this paper, we propose a novel fully Bayesian approach for the massive multiple-input multiple-output (MIMO) massive unsourced random access (URA). The payload of each user device is coded by the sparse regression codes (SPARCs) without redundant parity bits. A Bayesian model is established to capture the probabilistic characteristics of the overall system. Particularly, we adopt the core idea of the model-based learning approach to establish a flexible Bayesian channel model to adapt the complex environments. Different from the traditional divide-and-conquer or pilot-based massive MIMO URA strategies, we propose a three-layer message passing (TLMP) algorithm to jointly decode all the information blocks, as well as acquire the massive MIMO channel, which adopts the core idea of the variational message passing and approximate message passing. We verify that our proposed TLMP significantly enhances the spectral efficiency compared with the state-of-the-arts baselines, and is more robust to the possible codeword collisions

Massive MIMO for Internet of Things (IoT) Connectivity

Massive MIMO is considered to be one of the key technologies in the emerging 5G systems, but also a concept applicable to other wireless systems. Exploiting the large number of degrees of freedom (DoFs) of massive MIMO essential for achieving high spectral efficiency, high data rates and extreme spatial multiplexing of densely distributed users. On the one hand, the benefits of applying massive MIMO for broadband communication are well known and there has been a large body of research on designing communication schemes to support high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT) is still a developing topic, as IoT connectivity has requirements and constraints that are significantly different from the broadband connections. In this paper we investigate the applicability of massive MIMO to IoT connectivity. Specifically, we treat the two generic types of IoT connections envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC). This paper fills this important gap by identifying the opportunities and challenges in exploiting massive MIMO for IoT connectivity. We provide insights into the trade-offs that emerge when massive MIMO is applied to mMTC or URLLC and present a number of suitable communication schemes. The discussion continues to the questions of network slicing of the wireless resources and the use of massive MIMO to simultaneously support IoT connections with very heterogeneous requirements. The main conclusion is that massive MIMO can bring benefits to the scenarios with IoT connectivity, but it requires tight integration of the physical-layer techniques with the protocol design.Comment: Submitted for publicatio

Massive Access for Future Wireless Communication Systems

Multiple access technology played an important role in wireless communication in the last decades: it increases the capacity of the channel and allows different users to access the system simultaneously. However, the conventional multiple access technology, as originally designed for current human-centric wireless networks, is not scalable for future machine-centric wireless networks. Massive access (studied in the literature under such names as massive-device multiple access, unsourced massive random access, massive connectivity, massive machine-type communication, and many-access channels) exhibits a clean break with current networks by potentially supporting millions of devices in each cellular network. The tremendous growth in the number of connected devices requires a fundamental rethinking of the conventional multiple access technologies in favor of new schemes suited for massive random access. Among the many new challenges arising in this setting, the most relevant are: the fundamental limits of communication from a massive number of bursty devices transmitting simultaneously with short packets, the design of low complexity and energy-efficient massive access coding and communication schemes, efficient methods for the detection of a relatively small number of active users among a large number of potential user devices with sporadic transmission pattern, and the integration of massive access with massive MIMO and other important wireless communication technologies. This paper presents an overview of the concept of massive access wireless communication and of the contemporary research on this important topic.Comment: A short version has been accepted by IEEE Wireless Communication

Unsourced Random Access with the MIMO Receiver: Projection Decoding Analysis

We consider unsourced random access with MIMO receiver - a crucial communication scenario for future 5G/6G wireless networks. We perform a projection-based decoder analysis and derive energy efficiency achievability bounds when channel state information is unknown at transmitters and the receiver (no-CSI scenario). The comparison to the maximum-likelihood (ML) achievability bounds by Gao et al. (2023) is performed. We show that there is a region where the new bound outperforms the ML bound. The latter fact should not surprise the reader as both decoding criteria are suboptimal when considering per-user probability of error (PUPE). Moreover, transition to projection decoding allows for significant dimensionality reduction, which greatly reduces the computation time