Non-orthogonal multiple access (NOMA) has been recognized as a key driving technology for the fifth generation (5G) and beyond 5G cellular networks. For a practical downlink NOMA system with imperfect successive interference cancellation (SIC), we derive bounds on power allocation factors and formulate a minimum signal-to-interference-plus-noise ratio (SINR) difference criterion for NOMA pair formation. Through extensive simulations, we show the effect of imperfect SIC on the rate performance of a NOMA pair and the trade-off with its OMA (orthogonal multiple access) counterparts. We propose an adaptive user pairing (A-UP) algorithm for achieving better user rates. Further, the proposed A-UP algorithm results in better performance than state-of-the-art NOMA pairing algorithms in the presence of SIC imperfections.

A continuous evaluation of the end user's quality-of-experience (QoE) is essential for efficient video streaming. This is crucial for networks with constrained resources that offer time-varying channel quality to its users. In hypertext transfer protocol-based video streaming, the QoE is measured by quantifying the perceptual impact of distortions caused by rate adaptation or interruptions in playback due to rebuffering events. The resulting impact on the QoE due to these distortions has been studied individually in the literature. However, the QoE is determined by an interplay of these distortions, and therefore necessitates a combined study of them. To the best of our knowledge, there is no publicly available database that studies these distortions jointly on a continuous time basis. In this paper, our contributions are twofold. First, we present a database consisting of videos at full high definition and ultrahigh definition resolutions. We consider various levels of rate adaptation and rebuffering distortions together in these videos as experienced in a typical realistic setting. A subjective evaluation of these videos is conducted on a continuous time scale. Second, we present a QoE evaluation framework comprising a learning-based model during playback and an exponential model during rebuffering. Furthermore, we perform an objective evaluation of popular video quality assessment and continuous time QoE metrics over the constructed database. The objective evaluation study demonstrates that the performance of the proposed QoE model is superior to that of the objective metrics. The database is publicly available for download at http://www.iith.ac.in/~lfovia/downloads.html.

Base station operation consumes a lot of energy, a considerable amount of which can be saved by switching off base stations during low user demand (for example, at night). Base station switching (BSS) can result in loss in coverage if not performed properly. We show that coverage is closely related to scheduling via power management and that the bottleneck is typically the uplink. To save energy, we propose a set of BSS patterns, at a global system-level, that have the potential to provide full coverage if the appropriate schedulers are used. We further show that the existing benchmark uplink scheduling schemes do not provide full coverage when BSS is used in urban as well as rural macro-cell environments (the downlink benchmark scheduling scheme provides full coverage only for some of the BSS patterns). Hence, we propose novel scheduling schemes for both uplink and downlink that realistically model interference, ensure full coverage, and provide good energy-performance trade-offs for the proposed BSS patterns. We also present a low complexity high performance heuristic for the proposed uplink scheduler. Finally, we show the presented models and results can be used to quantify, offline, the energy-performance trade-offs under different operating scenarios.

Radars with mmWave frequency modulated continuous wave (FMCW) technology accurately estimate the range and velocity of targets in their field of view (FoV). The targeted angle of arrival (AoA) estimation can be improved by increasing receiving antennas or by using multiple-input multiple-output (MIMO). However, obtaining target features such as target type remains challenging. In this paper, we present a novel target classification method based on machine learning and features extracted from a range fast Fourier transform (FFT) profile by using mmWave FMCW radars operating in the frequency range of 77–81 GHz. The measurements are carried out in a variety of realistic situations, including pedestrian, automotive, and unmanned aerial vehicle (UAV) (also known as drone). Peak, width, area, variance, and range are collected from range FFT profile peaks and fed into a machine learning model. In order to evaluate the performance, various light weight classification machine learning models such as logistic regression, Naive Bayes, support vector machine (SVM), and lightweight gradient boosting machine (GBM) are used. We demonstrate our findings by using outdoor measurements and achieve a classification accuracy of 95.6% by using LightGBM. The proposed method will be extremely useful in a wide range of applications, including cost-effective and dependable ground station traffic management and control systems for autonomous operations, and advanced driver-assistance systems (ADAS). The presented classification technique extends the potential of mmWave FMCW radar beyond the detection of range, velocity, and AoA to classification. mmWave FMCW radars will be more robust in computer vision, visual perception, and fully autonomous ground control and traffic management cyber-physical systems as a result of the added new feature.

The lifetime of a UAV-assisted wireless network is determined by the amount of energy consumed by the UAVs during flight, data collection, and transmission to the ground station. Routing protocols are commonly used for data transmission in a communication network. However, because of the mobility of UAVs, using a routing protocol with a single communication technology results in higher delay and more energy consumption in a UAV-assisted wireless network. To overcome this, we propose two reinforcement learning (RL) algorithms, Q-learning and deep Q-network (DQN), for energy-efficient data transmission over a hybrid BLE/LTE/Wi-Fi/LoRa UAV-assisted wireless network. We consider BLE, LTE, Wi-Fi, and LoRa for communication over a UAV-GS link. The RL algorithms take any random network as input and learn the best policy to output the network with less energy consumption. The reward/penalty is chosen in such a way that the network with the highest energy consumption is penalized and the one with the lowest is rewarded, thereby minimizing total network energy consumption. Based on learning, it creates a hybrid BLE/LTE/Wi-Fi/LoRa UAV-assisted wireless network by assigning the best communication technology to a UAV-GS link. Further, we compare the performance of proposed RL algorithms with a rule-based algorithm and random hybrid scheme. In addition, we propose a theoretical framework for constructing hybrid network for both free space and free space multipath path loss models. We demonstrate the performance comparison of the proposed work with the conventional shortest path routing algorithm in terms of network energy consumption and average network delay using extensive results. Finally, the effect of the velocity of the UAV and the number of packets on the performance of the proposed framework is analyzed.

In a conventional Domain Adaptation (DA) setting, we only have one source and target domain, whereas, in many real-world applications, data is often collected from several related sources in different conditions. This has led to a more practical and challenging knowledge transfer problem called Multi-source Domain Adaptation (MDA). Several methodologies, such as prototype matching, explicit distance discrepancy, adversarial learning, etc., have been considered to tackle the MDA problem in recent years. Among them, the adversarial-based learning framework is a popular methodology for transferring knowledge from multiple sources to target domains using a minmax optimization strategy. Despite the advances in adversarial-based methods, several limitations exist, such as the need for a classifier-aware discrepancy metric to align the domains and the need to consider target samples’ consistency and semantic information while aligning the domains. To mitigate these issues, in this work, we propose a novel adversarial learning MDA algorithm, MDAMA, which aligns the target domain with a mixture distribution that consists of source domains. MDAMA uses margin-based discrepancy and augmented intermediate distributions to align the domains effectively. We also propose consistency of target samples by confidence thresholding and transfer of semantic information from multiple source domains to the augmented target domain to further improve the performance of the target domain. We extensively experiment with the MDAMA algorithm on popular real-world MDA datasets such as OfficeHome, Office31, PACS, Office-Caltech, and DomainNet. We evaluate the MDAMA model on these benchmark datasets and demonstrate top performance in all of them.