25 research outputs found
A Coupled Compressive Sensing Scheme for Unsourced Multiple Access
This article introduces a novel paradigm for the unsourced multiple-access
communication problem. This divide-and-conquer approach leverages recent
advances in compressive sensing and forward error correction to produce a
computationally efficient algorithm. Within the proposed framework, every
active device first partitions its data into several sub-blocks, and
subsequently adds redundancy using a systematic linear block code. Compressive
sensing techniques are then employed to recover sub-blocks, and the original
messages are obtained by connecting pieces together using a low-complexity
tree-based algorithm. Numerical results suggest that the proposed scheme
outperforms other existing practical coding schemes. Measured performance lies
approximately ~dB away from the Polyanskiy achievability limit, which is
obtained in the absence of complexity constraints
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
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