12 research outputs found
Massive MIMO for Ultra-reliable Communications with Constellations for Dual Coherent-noncoherent Detection
The stringent requirements of ultra-reliable low-latency communications
(URLLC) require rethinking of the physical layer transmission techniques.
Massive antenna arrays are seen as an enabler of the emerging
generation systems, due to increases in spectral efficiency and degrees of
freedom for transmissions, which can greatly improve reliability under
demanding latency requirements. Massive array coherent processing relies on
accurate channel state information (CSI) in order to achieve high reliability.
In this paper, we investigate the impact of imperfect CSI in a single-input
multiple-output (SIMO) system on the coherent receiver. An amplitude-phase
keying (APK) symbol constellation is proposed, where each two symmetric symbols
reside on distinct power levels. The symbols are demodulated using a dual-stage
non-coherent and coherent detection strategy, in order to improve symbol
reliability. By means of analysis and simulation, we find an adequate scaling
of the constellation and show that for high signal-to-noise ratio (SNR) and
inaccurate CSI regime, the proposed scheme enhances receiver performance.Comment: Accepted at WSA 2018, special session on "Massive MIMO for mobile
broadband communications and new 5G services
Short Packet Structure for Ultra-Reliable Machine-type Communication: Tradeoff between Detection and Decoding
Machine-type communication requires rethinking of the structure of short
packets due to the coding limitations and the significant role of the control
information. In ultra-reliable low-latency communication (URLLC), it is crucial
to optimally use the limited degrees of freedom (DoFs) to send data and control
information. We consider a URLLC model for short packet transmission with
acknowledgement (ACK). We compare the detection/decoding performance of two
short packet structures: (1) time-multiplexed detection sequence and data; and
(2) structure in which both packet detection and data decoding use all DoFs.
Specifically, as an instance of the second structure we use superimposed
sequences for detection and data. We derive the probabilities of false alarm
and misdetection for an AWGN channel and numerically minimize the packet error
probability (PER), showing that for delay-constrained data and ACK exchange,
there is a tradeoff between the resources spent for detection and decoding. We
show that the optimal PER for the superimposed structure is achieved for higher
detection overhead. For this reason, the PER is also higher than in the
preamble case. However, the superimposed structure is advantageous due to its
flexibility to achieve optimal operation without the need to use multiple
codebooks.Comment: Accepted at ICASSP 2018, special session on "Signal Processing for
Machine-Type Communications
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
Outage Analysis of Downlink URLLC in Massive MIMO systems with Power Allocation
Massive MIMO is seen as a main enabler for low latency communications, thanks
to its high spatial degrees of freedom. The channel hardening and favorable
propagation properties of Massive MIMO are particularly important for
multiplexing several URLLC devices. However, the actual utility of channel
hardening and spatial multiplexing is dependent critically on the accuracy of
channel knowledge. When several low latency devices are multiplexed, the cost
for acquiring accurate knowledge becomes critical, and it is not evident how
many devices can be served with a latency-reliability requirement and how many
pilot symbols should be allocated. This paper investigates the trade-off
between achieving high spectral efficiency and high reliability in the
downlink, by employing various power allocation strategies, for maximum ratio
and minimum mean square error precoders. The results show that using max-min
SINR power allocation achieves the best reliability, at the expense of lower
sum spectral efficiency.Comment: Presented in Asilomar 201
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 is 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. (C) 2019 Elsevier B.V. All rights reserved.Funding Agencies|European Research Council (ERC) under the European UnionEuropean Research Council (ERC) [648382 WILLOW]; Danish Council for Independent Research, Denmark [8022-00284B SEMIOTIC]; National Council for Scientific and Technological Development (CNPq) of BrazilNational Council for Scientific and Technological Development (CNPq) [304066/2015-0]; Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior Brazil (CAPES)CAPES [001]; Swedish Research Council (VR)Swedish Research Council; Excellence Center at Linkoping - Lund in Information Technology (ELLIIT), Denmark; CONFAP-ERC Agreement H2020 (Brazilian National Council of State Funding Agencies); CONFAP-ERC Agreement H2020 (European Research Council)</p