131 research outputs found
Ultra-Reliable Short-Packet Communications: Fundamental Limits and Enabling Technologies
The paradigm shift from 4G to 5G communications, anticipated to enable ultra-reliable low-latency communications (URLLC), will enforce a radical change in the design of wireless communication systems. Unlike in 4G systems, where the main objective is to provide a large transmission rate, in URLLC, as implied by its name, the objective is to enable transmissions with low latency and, simultaneously, very high reliability. Since low latency implies the use of short data packets, the tension between blocklength and reliability is studied in URLLC.Several key enablers for URLLC communications have been designated in the literature. Of special importance are diversity-enabling technologies such as multiantenna systems and feedback protocols. Furthermore, it is not only important to introduce additional diversity by means of the above examples, one must also guarantee that thescarce number of channel uses are used in an optimal way. Therefore, it is imperative to develop design guidelines for how to enable reliable detection of incoming data, how to acquire channel-state information, and how to construct efficient short-packet channel codes. The development of such guidelines is at the heart of this thesis. This thesis focuses on the fundamental performance of URLLC-enabling technologies. Specifically, we provide converse (upper) bounds and achievability (lower) bounds on the maximum coding rate, based on finite-blocklength information theory, for systems that employ the key enablers outlined above. With focus on the wireless channel, modeled via a block-fading assumption, we are able to provide answers to questions like: howto optimally utilize spatial and frequency diversity, how far from optimal short-packet channel codes perform, how multiantenna systems should be designed to serve a given number of users, and how to design feedback schemes when the feedback link is noisy. In particular, this thesis is comprised out of four papers. In Paper A, we study the short-packet performance over the Rician block-fading channel. In particular, we present achievability bounds for pilot-assisted transmission with several different decoders that allow us to quantify the impact, on the achievable performance, of imposed pilots and mismatched decoding. Furthermore, we design short-packet channel codes that perform within 1 dB of our achievability bounds. Paper B studies multiuser massive multiple-input multiple-output systems with short packets. We provide an achievability bound on the average error probability over quasistatic spatially correlated Rayleigh-fading channels. The bound applies to arbitrary multiuser settings, pilot-assisted transmission, and mismatched decoding. This makes it suitable to assess the performance in the uplink/downlink for arbitrary linear signal processing. We show that several lessons learned from infinite-blocklength analyses carry over to the finite-blocklength regime. Furthermore, for the multicell setting with randomly placed users, pilot contamination should be avoided at all cost and minimum mean-squared error signal processing should be used to comply with the stringent requirements of URLLC.In Paper C, we consider sporadic transmissions where the task of the receiver is to both detect and decode an incoming packet. Two novel achievability bounds, and a novel converse bound are presented for joint detection-decoding strategies. It is shown that errors associated with detection deteriorates performance significantly for very short packet sizes. Numerical results also indicate that separate detection-decoding strategies are strictly suboptimal over block-fading channels.Finally, in Paper D, variable-length codes with noisy stop-feedback are studied via a novel achievability bound on the average service time and the average error probability. We use the bound to shed light on the resource allocation problem between the forward and the feedback channel. For URLLC applications, it is shown that enough resources must be assigned to the feedback link such that a NACK-to-ACK error becomes rarer than the target error probability. Furthermore, we illustrate that the variable-length stop-feedback scheme outperforms state-of-the-art fixed-length no-feedback bounds even when the stop-feedback bit is noisy
Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G
We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback
schemes enhanced by machine learning techniques as a path towards
ultra-reliable and low-latency communication (URLLC). To this end, we propose
machine learning methods to predict the outcome of the decoding process ahead
of the end of the transmission. We discuss different input features and
classification algorithms ranging from traditional methods to newly developed
supervised autoencoders. These methods are evaluated based on their prospects
of complying with the URLLC requirements of effective block error rates below
at small latency overheads. We provide realistic performance
estimates in a system model incorporating scheduling effects to demonstrate the
feasibility of E-HARQ across different signal-to-noise ratios, subcode lengths,
channel conditions and system loads, and show the benefit over regular HARQ and
existing E-HARQ schemes without machine learning.Comment: 14 pages, 15 figures; accepted versio
Reliable Transmission of Short Packets through Queues and Noisy Channels under Latency and Peak-Age Violation Guarantees
This work investigates the probability that the delay and the peak-age of
information exceed a desired threshold in a point-to-point communication system
with short information packets. The packets are generated according to a
stationary memoryless Bernoulli process, placed in a single-server queue and
then transmitted over a wireless channel. A variable-length stop-feedback
coding scheme---a general strategy that encompasses simple automatic repetition
request (ARQ) and more sophisticated hybrid ARQ techniques as special
cases---is used by the transmitter to convey the information packets to the
receiver. By leveraging finite-blocklength results, the delay violation and the
peak-age violation probabilities are characterized without resorting to
approximations based on large-deviation theory as in previous literature.
Numerical results illuminate the dependence of delay and peak-age violation
probability on system parameters such as the frame size and the undetected
error probability, and on the chosen packet-management policy. The guidelines
provided by our analysis are particularly useful for the design of low-latency
ultra-reliable communication systems.Comment: To appear in IEEE journal on selected areas of communication (IEEE
JSAC
Achievable Sum Rate Optimization on NOMA-aided Cell-Free Massive MIMO with Finite Blocklength Coding
Non-orthogonal multiple access (NOMA)-aided cell-free massive multiple-input
multiple-output (CFmMIMO) has been considered as a promising technology to
fulfill strict quality of service requirements for ultra-reliable low-latency
communications (URLLC). However, finite blocklength coding (FBC) in URLLC makes
it challenging to achieve the optimal performance in the NOMA-aided CFmMIMO
system. In this paper, we investigate the performance of the NOMA-aided CFmMIMO
system with FBC in terms of achievable sum rate (ASR). Firstly, we derive a
lower bound (LB) on the ergodic data rate. Then, we formulate an ASR
maximization problem by jointly considering power allocation and user equipment
(UE) clustering. To tackle such an intractable problem, we decompose it into
two sub-problems, i.e., the power allocation problem and the UE clustering
problem. A successive convex approximation (SCA) algorithm is proposed to solve
the power allocation problem by transforming it into a series of geometric
programming problems. Meanwhile, two algorithms based on graph theory are
proposed to solve the UE clustering problem by identifying negative loops.
Finally, alternative optimization is performed to find the maximum ASR of the
NOMA-aided CFmMIMO system with FBC. The simulation results demonstrate that the
proposed algorithms significantly outperform the benchmark algorithms in terms
of ASR under various scenarios
Resource Allocation for Uplink Cell-Free Massive MIMO enabled URLLC in a Smart Factory
Smart factories need to support the simultaneous communication of multiple
industrial Internet-of-Things (IIoT) devices with ultra-reliability and
low-latency communication (URLLC). Meanwhile, short packet transmission for
IIoT applications incurs performance loss compared to traditional long packet
transmission for human-to-human communications. On the other hand, cell-free
massive multiple-input and multiple-output (CF mMIMO) technology can provide
uniform services for all devices by deploying distributed access points (APs).
In this paper, we adopt CF mMIMO to support URLLC in a smart factory.
Specifically, we first derive the lower bound (LB) on achievable uplink data
rate under the finite blocklength (FBL) with imperfect channel state
information (CSI) for both maximum-ratio combining (MRC) and full-pilot
zero-forcing (FZF) decoders. \textcolor{black}{The derived LB rates based on
the MRC case have the same trends as the ergodic rate, while LB rates using the
FZF decoder tightly match the ergodic rates}, which means that resource
allocation can be performed based on the LB data rate rather the exact ergodic
data rate under FBL. The \textcolor{black}{log-function method} and successive
convex approximation (SCA) are then used to approximately transform the
non-convex weighted sum rate problem into a series of geometric program (GP)
problems, and an iterative algorithm is proposed to jointly optimize the pilot
and payload power allocation. Simulation results demonstrate that CF mMIMO
significantly improves the average weighted sum rate (AWSR) compared to
centralized mMIMO. An interesting observation is that increasing the number of
devices improves the AWSR for CF mMIMO whilst the AWSR remains relatively
constant for centralized mMIMO.Comment: Accepted by Transactions on Communication
Joint Pilot and Payload Power Allocation for Massive-MIMO-enabled URLLC IIoT Networks
The Fourth Industrial Revolution (Industrial 4.0) is coming, and this
revolution will fundamentally enhance the way the factories manufacture
products. The conventional wired lines connecting central controller to robots
or actuators will be replaced by wireless communication networks due to its low
cost of maintenance and high deployment flexibility. However, some critical
industrial applications require ultra-high reliability and low latency
communication (URLLC). In this paper, we advocate the adoption of massive
multiple-input multiple output (MIMO) to support the wireless transmission for
industrial applications as it can provide deterministic communications similar
as wired lines thanks to its channel hardening effects. To reduce the latency,
the channel blocklength for packet transmission is finite, and suffers from
transmission rate degradation and decoding error probability. Thus,
conventional resource allocation for massive MIMO transmission based on Shannon
capacity assuming the infinite channel blocklength is no longer optimal. We
first derive the closed-form expression of lower bound (LB) of achievable
uplink data rate for massive MIMO system with imperfect channel state
information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF)
receivers. Then, we propose novel low-complexity algorithms to solve the
achievable data rate maximization problems by jointly optimizing the pilot and
payload transmission power for both MRC and ZF. Simulation results confirm the
rapid convergence speed and performance advantage over the existing benchmark
algorithms.Comment: Accepted in IEEE JSAC with special issue on Industry 4.0. Keywords:
URLLC, Industrial 4.0, Industrial Internet-of-Things (IIoT), Massive MIM
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