Orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has recently emerged as a promising paradigm for ensuring reliable communications in doubly-selective channels. This work investigates the spectra and orthogonality characteristics of analog (direct) and approximate digital implementations of ODDM systems. We first determine the time and frequency domain representations of the basis functions for waveform in analog and approximate digital ODDM systems. Thereafter, we derive their power spectral densities and show that while the spectrum of analog ODDM waveforms exhibits a step-wise behavior in its transition regions, the spectrum of approximate digital ODDM waveforms is confined to that of the ODDM sub-pulse. Next, we prove the orthogonality characteristics of approximate digital ODDM waveforms and show that, unlike analog ODDM waveforms, the approximate digital ODDM waveforms satisfy orthogonality without the need of additional time domain resources. Additionally, we examine the similarities and differences that implementations of approximate digital ODDM share with the other variants of DD modulations, focusing on the domain changes the symbols undergo, the type of pulse shaping and windowing used, and the domains and the sequence in which they are performed. Finally, we present numerical results to validate our findings and draw further insights.

The orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has been proposed as a promising paradigm for ensuring reliable communications in doublyselective channels. Although ODDM functions as a multicarrier modulation on the DD plane, academia and industry are more accustomed to understanding and altering the time and frequency resources of signals and waveforms. Recognizing this, in this work, we investigate the time and spectral occupancies of the transmitted signals in both analog (direct) and digital (approximate) implementations of ODDM systems. We begin by explicitly determining the basis functions for both ODDM systems in time and frequency domains, and highlight their phase term difference in the time domain and their envelope difference in the frequency domain. We then derive the spectral responses of their transmit signals. We reveal that while the spectral response of signals in analog ODDM systems exhibits a step-wise behavior in its transition regions, the spectral response of signals in digital ODDM systems is confined to that of the ODDM sub-pulse. Finally, through numerical results, we verify our findings and show that out-of-band-emission of ODDM systems can be further improved by increasing the duration of the ODDM sub-pulse.

We investigate the coexistence of orthogonal time-frequency space (OTFS) modulation with current fourth- and fifth-generation (4G/5G) communication systems that primarily use orthogonal frequency-division multiplexing (OFDM) waveforms. We first derive the input-output-relation of OTFS in the considered coexisting system. In this derivation, we consider 1) the inclusion of multiple cyclic prefixes (CPs) with unequal lengths to the OTFS signal and 2) edge carrier unloading (ECU), to account for the impacts of CP length, frame structure, and subcarrier arrangement described in 3GPP standards for 4G/5G systems. Our analysis reveals that the inclusion of multiple CPs to the OTFS signal and ECU lead to the channel response exhibiting spreading effects/leakage along the Doppler and delay dimensions, respectively. Consequently, the effective sampled delay-Doppler (DD) domain channel model for OTFS in coexisting systems may exhibit reduced sparsity. We also show that the effective DD domain channel coefficients for OTFS in coexisting systems are influenced by the unequal lengths of CPs. Subsequently, we propose an interference cancellation-based channel estimation (CE) technique for OTFS in coexisting systems. Through numerical results, we validate our analysis, highlight the importance of not ignoring the unequal lengths of CPs during signal detection, and show the significance of the proposed CE technique.

This study examines the coexistence of orthogonal time-frequency space (OTFS) modulation with current fourth-and fifth-generation (4G/5G) wireless communication systems that primarily use orthogonal frequency-division multiplexing (OFDM) waveforms. We first derive the input-output-relation (IOR) of OTFS when it coexists with an OFDM system while considering the impact of unequal lengths of the cyclic prefixes (CPs) in the OTFS signal. We show analytically that the inclusion of multiple CPs to the OTFS signal results in the effective sampled delay-Doppler (DD) domain channel response to be less sparse. We also show that the effective DD domain channel coefficients for OTFS in coexisting systems are influenced by the unequal lengths of the CPs. Subsequently, we propose an embedded pilotaided channel estimation (CE) technique for OTFS in coexisting systems that leverages the derived IOR for accurate channel characterization. Using numerical results, we show that ignoring the impact of unequal lengths of the CPs during signal detection can degrade the bit error rate performance of OTFS in coexisting systems. We also show that the proposed CE technique for OTFS in coexisting systems outperforms the state-of-the-art threshold-based CE technique.

Orthogonal delay-Doppler division multiplexing (ODDM) modulation was recently proposed as a promising solution for high-mobility communication systems. To achieve the potential of ODDM, reliable signal detection is essential, hence, in this work, we propose a contrastive learning-based signal detection approach for ODDM systems, named CL-ODDM. Unlike the conventional deep learning-based methods which focus on positive samples alone, we creatively leverage contrastive learning to exploit both positive and negative samples in the training dataset. By doing so, more distinguishable information of signals can be captured and extracted, contributing to reliable detection results. Moreover, we employ a convolutional neural network and recurrent encoder-decoder (CREN) to represent the comprehensive properties and features of ODDM signals. In addition, an adaptive correction method (ACM) is proposed to increase the convergence rate and improve the stability of the detection model. Extensive simulation results validate that the proposed CL-ODDM is significantly superior state-of-the-art related work, regarding the detection accuracy and convergence rate.

Deep learning (DL) demonstrates tremendous potential in high-mobility communication systems, especially from the perspective of signal detection. However, most existing DL-based detection methods are data/environment specific and the re-training process is resource intensive. To address these shortcomings, we propose a contrastive learning-based environmentally robust signal detection approach in orthogonal delay-Doppler division multiplexing (CL-ODDM) to achieve fast convergence, high accuracy and strong robustness to variations in wireless environments. Specifically, unlike conventional methods which explore only positive samples in the dataset for detection, in this work, we propose to leverage contrastive learning to fully exploit both positive and negative samples in the training dataset. This enables us to extract more comprehensive features of signals, which can accelerate convergence, improve detection accuracy, and enhance the generalized ability of our CL-ODDM. Moreover, we creatively employ a convolutional neural network and recurrent encoder-decoder (CREN) to represent the underlying properties of ODDM signals and extract high-quality features. To further improve environmental robustness, we propose novel training strategies, i.e., data augmentation (DA) and adaptive updating scheme (AUS). The proposed DA method is expected to increase the diversity of the dataset and represent more effective signal features. The designed AUS leverages transfer learning to adapt partial layers of CREN to make our CL-ODDM suitable for various wireless environments. Numerous simulation results validate that the proposed CL-ODDM significantly outperforms state-of-the-art related works, in terms of detection accuracy, environmental robustness and convergence rate.

Orthogonal time frequency space (OTFS) modulation is a recently proposed delay-Doppler (DD) domain communication scheme, which has shown promising performance in general wireless communications, especially over high-mobility channels. In this paper, we investigate DD domain Tomlinson-Harashima precoding (THP) for downlink multiuser multiple-input and multiple-output OTFS (MU-MIMO-OTFS) transmissions. Instead of directly applying THP based on the huge equivalent channel matrix, we propose a simple implementation of THP that does not require any matrix decomposition or inversion. Such a simple implementation is enabled by the DD domain channel property, i.e., different resolvable paths do not share the same delay and Doppler shifts, which makes it possible to pre-cancel all the DD domain interference in a symbol-by-symbol manner. We also study the achievable rate performance for the proposed scheme by leveraging the information-theoretical equivalent models. In particular, we show that the proposed scheme can achieve a near optimal performance in the high signal-to-noise ratio (SNR) regime. More importantly, scaling laws for achievable rates with respect to number of antennas and users are derived, which indicate that the achievable rate increases logarithmically with the number of antennas and linearly with the number of users. Our numerical results align well with our findings and also demonstrate a significant improvement compared to existing MU-MIMO schemes on OTFS and orthogonal frequency-division multiplexing (OFDM).

In this paper, we investigate the delay-Doppler (D-D) domain Tomlinson-Harashima precoding (THP) for downlink multiuser multiple-input and multiple-output orthogonal time frequency space (MU-MIMO-OTFS) transmissions. Instead of directly applying THP based on the huge equivalent channel matrix, we propose a simple implementation of THP that does not require any matrix decomposition or inversion. Such a simple implementation is enabled by the DD domain channel property, i.e., different resolvable paths do not share the same delay and Doppler shifts, which makes it possible to pre-cancel all the DD domain inter-ference in a symbol-by-symbol manner. We also demonstrate the theoretical results on the sum-rate performance of the proposed scheme. In particular, we show that the sum-rate of the proposed scheme increases logarithmically with the number of antennas, while increases linearly with the number of users. Our numerical results align well with our findings and also verify the effectiveness of the proposed scheme.

Streaming video continues to experience unprecedented growth. This underscores the need to identify user-centric performance measures and models that will allow operators to satisfy requirements for cost-effective network dimensioning delivered with an acceptable level of user experience. This paper presents an analysis of two novel metrics in the context of fixed capacity links: (i) the average proportion of a video’s playing time during which the quality is reduced and (ii) the average proportion of videos which experience reduced quality at least once during their playing time, based on an M/M/∞ system. Our analysis is shown to hold for the more general M/G/∞ system for metric (i), but not for (ii) and simulation studies show an unexpected form of sensitivity of metric (ii) to the flow duration distribution, contrary to the norm of increasing variance causing worse performance. At typical operational loads these new metrics provide a more sensitive and information rich guide for understanding how user experience degrades, than the widely used average throughput metric does. We further show that only the combined use of this existing and our new metrics can provide a holistic perspective on overall user performance.

Efficient fault detection and characterization are crucial requirements for automated network diagnosis systems. In this paper, we present normalized statistical signatures (NSSs), a network ‘soft-failure’ characterization technique for user devices (UDs) based upon exhaustive sets of aggregated statistical features extracted from transmission control protocol (TCP) packet streams. TCP streams are collected on-demand (e.g. upon user complaint) with fixed data transfer limits to capture the artifacts resulting from UD faults. NSSs created using live network and testbed data are able to uniquely characterize many faults and offer insight into how various types of features are affected by the faults and networks. We then introduce the link adaptive signature estimation (LASE) technique to reduce the quantities of collected NSSs required for generalized diagnostic systems with variable link parameters. We create feature estimator functions using multivariate regression techniques to generate artificial NSSs, which are subsequently used to train machine learning systems that have robust generalization capabilities. Performance of a prototype fault classifier system based on NSSs shows that an overall detection accuracy of 98% can be achieved for eight types of faults in a live network environment. In this paper, we specifically focus on formulating the basic framework of NSS and LASE, and limit the analysis to wired networks. This work can later be extended to encompass more complex fixed and mobile wireless networking environments. We expect that the combination of NSSs and LASE can serve as the foundation of next-generation automated network diagnosis systems.

We present an automated solution for rapid diagnosis of both known and unknown “soft-failures” in network User Devices (UDs). A multiclass classifier is first trained with the known faults and during diagnosis, the unknown faults are clustered to determine the existence of a new fault. Then, in an iterative process, the classifier is re-trained with the newly detected fault. The system relies on 410 features long Normalized Statistical Signature (NSSs) for fault characterization. Since, the high dimensionality of the NSS can create model overfitting, we propose EigenNSS, a transformed signature with lower dimensions and minimum information loss. The system is evaluated with live network data of 17 emulated UD faults. The results show an overall detection accuracy of 97.2%with minimum false positives and dimensionality reduction of 93.9%. Also, compared with the NSS, the EigenNSS has faster training and diagnosis times suitable for on-demand as well as real-time diagnostic applications.

This paper studies throughput metrics for elastic traffic in a beyond 3G wireless system with QoS differentiation. We extend previous work by studying the capacity for elastic traffic subject to simultaneous variation due to the loading of higher priority traffic and the radio channel. The results yield novel insights that show: (i) load dependence in determining an optimal throughput metric, (ii) call-average throughput (thetasmacr) is not affected by the radio channel variability while flow-average (Phi) and the median throughputs, as well as the sojourn time are affected, and (iii) the proximity of thetasmacr to the median over a range of typical system loads (les0.7) confirms it as the best throughput estimator for elastic traffic, except at extreme loads (>0.85) where Phi and the median converge.

The paper presents results from an analysis of traffic measurements taken at the G/sub b/ interface of a GPRS network. Data was captured in one-week long data sets between November 2002 and March 2004. The paper provides new results, based on three of these data sets, and extends our previous work (Ivanovich, M. et al., 16th ITC Specialist Seminar, 2004). The data was analysed to characterise the aggregate workload statistics, including marginal distribution and autocorrelation, and identify any trends over this timeframe. A study of models that adequately characterise the traffic was also undertaken We find that the aggregate work process (bytes "departing" the SGSN) has a marginal distribution closely matching a negative exponential; however, this does not allow us to conclude that the packet arrival or departure processes are Poisson. The "burstiness" of the traffic (i.e. the coefficient of variation) was found to become smaller over time, i.e. the traffic became smoother. This occurred due to a greater level of aggregation, as a direct result of more "low traffic" users connecting to the network. This may be indicative of an increase in WAP and mobile e-mail usage. Four potential models for the aggregate work process were investigated. The latest data shows that the trend towards a more Gaussian-type marginal distribution model continues. Of the four models studied, the Poisson Pareto burst process gave the best fit to the queueing performance compared with the recorded traffic traces. The fit to the most recent data traffic measurements has substantially improved compared with the earlier results.

We present an empirical 3-D user satisfaction model dealing with quality of service (QoS) in 2.5/3G wireless data networks. An OSI-based hierarchical framework is proposed, with mappings between performance attributes of adjacent network layers in a wireless environment. This concept is effectively a data-centric variant of a voice-service mean opinion score (MOS) to quantify user-perceived QoS, albeit in a multidimensional sense. The proposed approach is validated with a comprehensive 166-participant experiment encompassing the relationships between the top three levels of the framework: User-perceived, application- and Internet protocol (IP)-layer QoS. Finally, a wireless access technology mapping method is synthesised, aimed at giving engineers and application developers insight into the approximate bounds of an application's user-level performance for a given access technology.

Cellular radio networks supporting both circuit switched voice and packet switched data services aim to maximise the resource utilisation through efficient sharing of the resource. These sharing algorithms can use pre-emption to give voice service priority over data, thus preserving the grade of service for voice at the expense of delaying data calls. This paper develops models for a pre-emptive voice and data system, with a particular focus on GPRS. The model is based on an assumption of independence between the voice and data traffic. These models allow estimates of (i) the probability that a voice call causes pre-emption of a data call, as well as (ii) the probability that a data call will be delayed due to either being pre-empted by a voice call while in progress or due to not finding free resources at its initial attempt. The results show that a Poisson model for data traffic gives the best results for pre-emption probability when compared with measurements taken from a GSM/GPRS network.

We investigate performance improvements through TCP window size optimisation achievable when TCP Reno is used over a highly heterogeneous network, such as an 802.11b wireless LAN or a GPRS-based internet connection. Initially, our modelling focuses on a constant rate, buffered access link, based on a loss-less wireless channel and with a bandwidth at least one order of magnitude less than its fixed-network continuation into the core. We implement the model guidelines on wireless LAN and GPRS access links, confirming that TCP throughput can be increased by optimising the TCP receiver window size to the link characteristics, and more significantly, that there exists a broad range of near-optimal awnd values.