Achieving ultra-reliable low-latency communications (URLLC) is critical for the operation of data-intensive applications and for ensuring seamless connectivity. Millimeter-wave (mmWave) technology is expected to support URLLC by expanding the available spectrum and providing multi-gigabit services. However, a well-recognized challenge is that mmWave communication links are susceptible to blockage, which may lead to communication disruptions. Conventional approaches, such as interleaving and feedback mechanisms provide resilience against such blockages at the cost of incurring additional delay, which may be too large to support URLLC effectively. This calls for novel techniques to develop resilient transmission mechanisms that can support URLLC. This paper develops gracefully resilient transmission mechanisms by deploying multilevel codes over space and over time. These codes allow the control of the received information and they accommodate different quality of service requirements of different information streams. Our evaluations, carried out also within the ns-3 network simulator, show that deploying these codes leads to attractive trade-offs between rate, delay, and outage probability.

Integrated Access and Backhaul (IAB) represents a fast and cost-efficient network deployment technology that enhances the coverage of millimeter-wave (mmWave) 5G networks. In addition to the conventional challenges of wireless multi-hop relaying such as, e.g., increased interference and packet delays, traffic asymmetry can lead to significant delay degradation. While centralized coordination can mitigate these challenges, it may also lead to unnecessary overheads. In this paper, we propose an effective delay-aware distributed solution for joint access and backhaul link scheduling and route selection designed to function with limited information, which relies only on the knowledge collected from immediate neighbors. We formulate the joint upstream and downstream routing and scheduling problem, which is solved in a distributed manner for the IAB system with diverse delay requirements. To effectively tackle this problem, we employ deep reinforcement learning (DRL) algorithms. Our numerical results demonstrate that the proposed distributed solution provides improved scalability as compared to the centralized approach without a significant performance loss.

Millimeter-wave (mmWave) spectrum is expected to support data-intensive applications that require ultra-reliable low-latency communications (URLLC). However, mmWave links are highly sensitive to blockage, which may lead to disruptions in the communication. Traditional techniques that build resilience against such blockages (among which are interleaving and feed-back mechanisms) incur delays that are too large to effectively support URLLC. This calls for novel techniques that ensure resilient URLLC. In this paper, we propose to deploy multilevel codes over space and over time. These codes offer several benefits, such as they allow to control what information is received and they provide different reliability guarantees for different information streams based on their priority. We also show that deploying these codes leads to attractive trade-offs between rate, delay, and outage probability. A practically-relevant aspect of the proposed technique is that it offers resilience while incurring a low operational complexity.

The use of millimeter-wave (mmWave) frequencies by 5G/5G+ technology results in increased signal attenuation naturally requiring dense network deployments. However, traditional fiber-based backhauling proves costly for network operators. To address this issue, 3GPP proposed the Integrated Access and Backhaul (IAB) concept to enable wireless backhaul and reduce deployment costs. However, system dynamics such as user mobility and traffic variations challenge system optimization and may shift the performance from its optimized state. On top of this, in-band mmWave IAB networks are subject to the half-duplex constraint, which prevents simultaneous transmission and reception. These limitations present challenges in optimizing the IAB network. Therefore, the goal of this study is to provide a computationally-efficient methodology for resource allocation and user scheduling in mm Wave IAB networks considering the aforementioned system limitations and constraints. Moreover, we evaluate the influence of system-specific factors and dynamics on the optimization of IAB networks and the time that it takes for the system to deviate from its optimized state. Our results show that by employing an optimally-parametrized scheduler, the throughput gain is 55% as compared to the baseline, where the radio resources are split equally among the users. The cell size is the primary parameter affecting the optimization gain, i.e., smaller cell sizes result in diminishing benefits when utilizing optimized algorithms.

To achieve high reliability for Wi-Fi, we propose to use network coding (NC) as a proactive inter-frame (packet-level) redundancy technique at the medium access control (MAC) layer, which has the advantage of low latency and high spectral efficiency compared to the existing retransmission and repetition techniques. In this paper, we explore practical ways to integrate NC in Wi-Fi systems and conduct over-the-air experiments in an office environment using a Wi-Fi-based platform, where NC is implemented as a software layer. The experiment results show that NC can achieve up to two orders of magnitude reliability gain over the baseline repetition scheme with the same spectral efficiency. When interferences exist in Wi-Fi transmissions, but the packet erasures are sufficiently uncorrelated, using NC can achieve very high reliability. When the correlation increases the performance gain of NC degrades, but it still maintains a significant advantage over repetition.

Recently proposed by 3GPP, Integrated Access and Backhaul (IAB) technology promises to deliver a cost-efficient and flexible solution for network densification in 5G/6G systems. Since IAB architecture is based on multi-hop topology and advanced functionalities, such as multi-connectivity transmission and multi-routing, the potential utilization of IAB systems raises an issue of efficient system design. In this paper, we develop an optimization framework capable of jointly selecting transmission paths and allocating radio resources in compliance with half-duplexing and interference constraints. The presented numerical results illustrate that directional mm Wave beams employed at the wireless backhaul are essential for capacity boosting, thus allowing to fully exploit the radio resources in self-backhauled systems. We also establish that the multi-hop IAB topology provides advantages in terms of end-to-end user throughput as compared to single-hop systems.

Modern wireless infrastructures provide multiple options for a transmitter to utilize multiple independent data paths. Examples include simultaneous connections via multiple radio access technologies (multi-RAT), dual/multi-connectivity and carrier aggregation in 5G, Integrated Access and Backhaul (IAB) network, etc. Such network redundancies can be utilized to provide enhanced reliability and/or delay performances over the wireless media, e.g., to support ultra-reliable low-latency (URLLC) services. However, traditional reliability enhancement techniques fall short of making efficient use of such multi-path transmission environments. Packet-level coding, in this case, can be a better candidate to support URLLC since it provides enhanced reliability with higher spectral efficiency and low latency by proactively adding coded redundancy, which also enables the effective treatment of all paths as a single data pipe. As the multi-path scenarios are oftentimes heterogeneous in terms of supported data rate, packet-level reliability, transmission delay, etc., how to optimally design the coding parameters to achieve URLLC requirements becomes an issue. In this paper we study the problem of minimizing the transmission delay while meeting the required reliability target in the multi-path environment. We assume the packets arrive in bursts and linear packet-level coding is used to enhance reliability. We propose a fast bisection algorithm to determine the optimal code rate and path traffic distribution rule for the encoded packets, which provably achieves the minimum delay with the required reliability under very general conditions.

The high propagation losses and sensitivity to link blockage naturally require dense deployments of millimeter-wave (mmWave) 5G New Radio (NR) systems. One of the inherent challenges in these deployments is cost-efficient backhauling. Addressing this issue, 3GPP has recently proposed the concept of integrated access and backhaul (IAB) to reduce the deployment costs by enabling wireless backhaul. The efficient utilization of spectrum in these systems is conditional on the ability of IAB nodes to simultaneously receive signals on their sectoral antennas. In this paper, we investigate the interference caused by this functionality and identify countermeasures including angular and spatial diversities. Our numerical results demonstrate that the angular distance of 25° between the user equipment (UE) served by adjacent sectoral antennas is sufficient to efficiently mitigate interference. A comparable reduction in the interference level can also be achieved by utilizing spatial diversity with antenna separation of at least 20 m. By combining these methods, one can identify the target levels of angular and spatial diversities suitable for the particular deployment restrictions.

Nanopore sequencing is an emerging new technology for sequencing Deoxyribonucleic acid (DNA), which can read long fragments of DNA (~50000 bases), in contrast to most current short-read sequencing technologies which can only read hundreds of bases. While nanopore sequencers can acquire long reads, the high error rates (20%-30%) pose a technical challenge. In a nanopore sequencer, a DNA is migrated through a nanopore, and current variations are measured. The DNA sequence is inferred from this observed current pattern using an algorithm called a base-caller. In this paper, we propose a mathematical model for the “channel” from the input DNA sequence to the observed current, and calculate bounds on the information extraction capacity of the nanopore sequencer. This model incorporates impairments, such as (non-linear) intersymbol interference, deletions, and random response. These information bounds have two-fold application: 1) The decoding rate with a uniform input distribution can be used to calculate the average size of the plausible list of DNA sequences given an observed current trace. This bound can be used to benchmark existing base-calling algorithms, as well as serving a performance objective to design better nanopores. 2) When the nanopore sequencer is used as a reader in a DNA storage system, the storage capacity is quantified by our bounds.

Nanopore sequencing is an emerging new technology for sequencing DNA, which can read long fragments of DNA (~50,000 bases) unlike most current sequencers which can only read hundreds of bases. While nanopore sequencers can acquire long reads, the high error rates (≈ 30%) pose a technical challenge. In a nanopore sequencer, a DNA is migrated through a nanopore and current variations are measured. The DNA sequence is inferred from this observed current pattern using an algorithm called a base-caller. In this paper, we propose a mathematical model for the “channel” from the input DNA sequence to the observed current, and calculate bounds on the information extraction capacity of the nanopore sequencer. This model incorporates impairments like inter-symbol interference, deletions, as well as random response. The practical application of such information bounds is two-fold: (1) benchmarking present base-calling algorithms, and (2) offering an optimization objective for designing better nanopore sequencers.

We study the channel capacity of a general discrete energy harvesting channel with a finite battery. Contrary to traditional communication systems, the transmitter of such a channel is powered by a device that harvests energy from a random exogenous energy source and has a finite-sized battery. As a consequence, at each transmission opportunity, the system can only transmit a symbol whose energy is no more than the energy currently available. This new type of power supply introduces an unprecedented input constraint for the channel, which is simultaneously random, instantaneous, and influenced by the full history of the inputs and the energy harvesting process. Furthermore, naturally, in such a channel, the energy information is observed causally at the transmitter. Both of these characteristics pose great challenges for the analysis of the channel capacity. In this paper, we use techniques developed for channels with side information and finite-state channels, to obtain lower and upper bounds on the capacity of energy harvesting channels. In particular, in a general case with Markov energy harvesting processes, we use stationarity and ergodicity theory to compute and optimize the achievable rates for the channels, and derive a series of computable capacity upper and lower bounds.

Given n discrete random variables, its entropy vector is the 2n- 1-dimensional vector obtained from the joint entropies of all non-empty subsets of the random variables. It is well known that there is a close relation between such an entropy vector and a certain group-characterizable vector obtained from a finite group and n of its subgroups; indeed, roughly speaking, knowing the region of all such group-characterizable vectors is equivalent to knowing the region of all entropy vectors. This correspondence may be useful for characterizing the space of entropic vectors and for designing network codes. If one restricts attention to abelian groups then not all entropy vectors can be obtained. This is an explanation for the fact shown by Dougherty et al. that linear network codes cannot achieve capacity in general network coding problems (since linear network codes come from abelian groups). All abelian groupcharacterizable vectors, and by fiat all entropy vectors generated by linear network codes, satisfy a linear inequality called the Ingleton inequality. General entropy vectors, however, do not necessarily have this property. It is, therefore, of interest to identify groups that violate the Ingleton inequality. In this paper, we study the problem of finding nonabelian finite groups that yield characterizable vectors, which violate the Ingleton inequality. Using a refined computer search, we find the symmetric group S5 to be the smallest group that violates the Ingleton inequality. Careful study of the structure of this group, and its subgroups, reveals that it belongs to the Ingleton-violating family PGL(2, q) with a prime power q ≥ 5, i.e., the projective group of 2 × 2 nonsingular matrices with entries in Fq. We further interpret this family of groups, and their subgroups, using the theory of group actions and identify the subgroups as certain stabilizers. We also extend the construction to more general groups such as PGL(n, q) and GL(n, q). The families of groups identified here are therefore good candidates for constructing network codes more powerful than linear network codes, and we discuss some considerations for constructing such group network codes.

We study two channels with causal CSIT: a finite state channel with input constraints and a finite-battery energy harvesting channel, considered in [1] and for the latter [2]-[5]. The capacity of these channels remains open and the calculation of the upper bounds often has a complexity double exponential in the block size N. In this paper we obtain an alternative upper bound which has a complexity linear in N. While, for any N, this bound is looser than the bound in [1], since it can be readily computed for very large values of N, it leads to numerically tighter bounds in many cases. Furthermore, for the energy harvesting channel we calculate the pairwise error probabilities of the ML decoder, which provides a useful guideline for the code design.

We study two types of channels in this paper: certain channels with states and energy harvesting channels. The first is based on Gallager's finite state channel with input constraints and causal CSIT. The second deals with two different scenarios with regard to the availability of energy information at the transmitter. For both channels we derive new capacity bounds, mostly using bounding techniques of Verdú and Han and Gallager.

We consider the problem of determining the capacity of an energy-harvesting transmitter with finite battery communicating over a discrete memoryless channel. When the battery is unlimited, or zero, the capacity has been determined, but it remains unknown for a finite non-zero battery. In this paper we assume that the harvested energy at each time, the total battery storage, and the transmitter signal energy at each time can be quantized to the same unit (i.e., the same energy interval). Under this assumption, we show that the capacity can be described using the Verdú-Han general framework. If we further assume that the transmitted symbol at each time depends only on the energy currently available, and not on the entire past history of energy harvests and symbols transmitted, then we show that the system reduces to a finite state channel (FSC) with the required ergodic and Markov properties so that lower bounds on the capacity can be readily numerically computed. We conjecture that our numerical bounds are tight. Our numerical results indicate that even the minimal possible battery storage can reap a significant fraction of the infinite battery capacity.

In principle, network codes derived from non-Abelian groups can be used to attain every point in the capacity region of wired acyclic networks. However, group codes derived from a particular group, and its subgroups, is useful only if it can model independent sources, as well as violate the Ingleton bound which restricts the capacity region obtainable by linear network codes. We study both the independent source and the Ingleton-violating requirement for subgroups of the groups PGL(2, ρ) and GL(2, ρ) with primes ρ ≥ 5. For both these groups we demonstrate that the requirements can be met, which suggests that PGL(2, ρ) and GL(2, ρ) are rich enough groups to construct network codes superior to linear ones. We also construct a model for independent sources using the direct product of the aforementioned groups.