2,450 research outputs found
User Detection Performance Analysis for Grant-Free Uplink Transmission in Large-Scale Antenna Systems
In this paper, user detection performance of a grant-free uplink transmission
in a large scale antenna system is analyzed, in which a general grant-free
multiple access is considered as the system model and Zadoff-Chu sequence is
used for the uplink pilot. The false alarm probabilities of various user
detection schemes under the target detection probabilities are evaluated
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
How Much Do Downlink Pilots Improve Cell-Free Massive MIMO?
In this paper, we analyze the benefits of including downlink pilots in a
cell-free massive MIMO system. We derive an approximate per-user achievable
downlink rate for conjugate beamforming processing, which takes into account
both uplink and downlink channel estimation errors, and power control. A
performance comparison is carried out, in terms of per-user net throughput,
considering cell-free massive MIMO operation with and without downlink
training, for different network densities. We take also into account the
performance improvement provided by max-min fairness power control in the
downlink. Numerical results show that, exploiting downlink pilots, the
performance can be considerably improved in low density networks over the
conventional scheme where the users rely on statistical channel knowledge only.
In high density networks, performance improvements are moderate.Comment: 7 pages, 5 figures. IEEE Global Communications Conference 2016
(GLOBECOM). Accepte
Downlink Training in Cell-Free Massive MIMO: A Blessing in Disguise
Cell-free Massive MIMO (multiple-input multiple-output) refers to a
distributed Massive MIMO system where all the access points (APs) cooperate to
coherently serve all the user equipments (UEs), suppress inter-cell
interference and mitigate the multiuser interference. Recent works demonstrated
that, unlike co-located Massive MIMO, the \textit{channel hardening} is, in
general, less pronounced in cell-free Massive MIMO, thus there is much to
benefit from estimating the downlink channel. In this study, we investigate the
gain introduced by the downlink beamforming training, extending the previously
proposed analysis to non-orthogonal uplink and downlink pilots. Assuming
single-antenna APs, conjugate beamforming and independent Rayleigh fading
channel, we derive a closed-form expression for the per-user achievable
downlink rate that addresses channel estimation errors and pilot contamination
both at the AP and UE side. The performance evaluation includes max-min
fairness power control, greedy pilot assignment methods, and a comparison
between achievable rates obtained from different capacity-bounding techniques.
Numerical results show that downlink beamforming training, although increases
pilot overhead and introduces additional pilot contamination, improves
significantly the achievable downlink rate. Even for large number of APs, it is
not fully efficient for the UE relying on the statistical channel state
information for data decoding.Comment: Published in IEEE Transactions on Wireless Communications on August
14, 2019. {\copyright} 2019 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other use
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