6,874 research outputs found
Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach
A key challenge of massive MTC (mMTC), is the joint detection of device
activity and decoding of data. The sparse characteristics of mMTC makes
compressed sensing (CS) approaches a promising solution to the device detection
problem. However, utilizing CS-based approaches for device detection along with
channel estimation, and using the acquired estimates for coherent data
transmission is suboptimal, especially when the goal is to convey only a few
bits of data.
First, we focus on the coherent transmission and demonstrate that it is
possible to obtain more accurate channel state information by combining
conventional estimators with CS-based techniques. Moreover, we illustrate that
even simple power control techniques can enhance the device detection
performance in mMTC setups.
Second, we devise a new non-coherent transmission scheme for mMTC and
specifically for grant-free random access. We design an algorithm that jointly
detects device activity along with embedded information bits. The approach
leverages elements from the approximate message passing (AMP) algorithm, and
exploits the structured sparsity introduced by the non-coherent transmission
scheme. Our analysis reveals that the proposed approach has superior
performance compared to application of the original AMP approach.Comment: Submitted to IEEE Transactions on Communication
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
Many Access for Small Packets Based on Precoding and Sparsity-aware Recovery
Modern mobile terminals produce massive small data packets. For these
short-length packets, it is inefficient to follow the current multiple access
schemes to allocate transmission resources due to heavy signaling overhead. We
propose a non-orthogonal many-access scheme that is well suited for the future
communication systems equipped with many receive antennas. The system is
modeled as having a block-sparsity pattern with unknown sparsity level (i.e.,
unknown number of transmitted messages). Block precoding is employed at each
single-antenna transmitter to enable the simultaneous transmissions of many
users. The number of simultaneously served active users is allowed to be even
more than the number of receive antennas. Sparsity-aware recovery is designed
at the receiver for joint user detection and symbol demodulation. To reduce the
effects of channel fading on signal recovery, normalized block orthogonal
matching pursuit (BOMP) algorithm is introduced, and based on its approximate
performance analysis, we develop interference cancellation based BOMP (ICBOMP)
algorithm. The ICBOMP performs error correction and detection in each iteration
of the normalized BOMP. Simulation results demonstrate the effectiveness of the
proposed scheme in small packet services, as well as the advantages of ICBOMP
in improving signal recovery accuracy and reducing computational cost.Comment: 30 pages 8 figures ,submited to tco
- …