This work explores the integration of Non-Orthogonal Multiple Access (NOMA) and Orthogonal Delay-Doppler Division Multiplexing (ODDM) within an Integrated Sensing and Communication (ISAC) framework. The proposed system leverages ODDM for high-mobility scenarios and NOMA for efficient resource utilization, thereby enabling enhanced trade-offs between communication and sensing. To achieve a balance between communication and sensing performance, an optimization problem is formulated to maximize the weighted sum of a sensing performance metric and communication throughput by jointly optimizing user grouping, power allocation, and beamforming, which are inherently coupled. To solve this problem efficiently, it is decomposed into three subproblems—user grouping, power allocation, and beamforming—which are then addressed iteratively to improve overall system performance. Simulation results validate the efficacy of the proposed framework under various mobility conditions, demonstrating improved sum-rate and sensing accuracy compared to Orthogonal Multiple Access (OMA) systems. This study offers valuable insights into advanced ISAC architectures, which are critically important for future 6G networks.

In this paper, a cost-effective and high-accuracy precoding technique based on$\epsilon $-Fuzzy pareto active learning (FPAL) is proposed for millimeter wave (mmWave) massive MIMO communications. The proposed method achieves a low iteration convergence with low complexity. Two practical structures, namely fully-connected and partially-connected structures are considered for hybrid precoding. Furthermore, the effect of high and low-resolution quantization in the digital-to-analog converter, phase shifter, and imperfect channel state information are discussed. The performance results of the proposed technique and alternating minimization methods beside fully digital techniques are compared and discussed in the terms of the spectral efficiency (SE), bit error rate (BER), normalized mean square error (NMSE), energy efficiency (EE) for phase-shifter (PS) with low bit quantization, and imperfect channel state information (CSI). To address the applicability of the proposed method, experimental results are obtained from a real mmWave hardware setup compliant with 3GPP standards, and verify the simulated ones for the proposed$\epsilon $-FPAL hybrid precoding scheme. The reduced complexity, higher performance and EE make the proposed method suitable for 5G and beyond communication systems.

Integrated sensing and communication (ISAC) is a key enabler for next-generation wireless communication systems to improve spectral efficiency. However, the coexistence of sensing and communication functionalities can cause harmful interference. In this paper, we propose to use a reconfigurable intelligent surface (RIS) in conjunction with ISAC to address this issue. The RIS is composed of a large number of low-cost elements that can adjust the amplitude and phase shift of impinging signals, thus providing a relatively high beamforming gain. To maximize the sum-rate of the communication system, we jointly optimize the beamformer at the base station (BS) and the phase shifts at the RIS, subject to a threshold on the interference power, the unit-norm constraint of the transmit power, and the unit modulus constraint of the RIS phase shifts. To efficiently tackle this NP-hard problem, we first reformulate the problem into a more tractable form using the fractional programming (FP) technique. Then, we exploit the geometrical properties of the constraints and adopt an alternating manifold-based optimization to compute the optimal active beamformer and the RIS phase shifts, respectively. Simulation results demonstrate that the proposed RIS-assisted design significantly reduces the mutual interference and improves the system sum-rate for the communication system.

Automatic modulation classification (AMC) using the Deep Neural Network (DNN) approach outperforms the traditional classification techniques, even in the presence of challenging wireless channel environments. However, the adversarial attacks cause the loss of accuracy for the DNN-based AMC by injecting a well-designed perturbation to the wireless channels. In this paper, we propose a novel generative adversarial network (GAN)-based countermeasure approach to safeguard the DNN-based AMC systems against adversarial attack examples. GAN-based aims to eliminate the adversarial attack examples before feeding to the DNN-based classifier. Specifically, we have shown the resiliency of our proposed defense GAN against the Fast-Gradient Sign method (FGSM) algorithm as one of the most potent kinds of attack algorithms to craft the perturbed signals. The existing defense-GAN has been designed for image classification and does not work in our case where the above-mentioned communication system is considered. Thus, our proposed countermeasure approach deploys GANs with a mixture of generators to overcome the mode collapsing problem in a typical GAN facing radio signal classification problem. Simulation results show the effectiveness of our proposed defense GAN so that it could enhance the accuracy of the DNN-based AMC under adversarial attacks to 81%, approximately.

The earlier works in the context of low-rank-sparse-decomposition (LRSD)-driven stationary synthetic aperture radar (SAR) imaging have shown significant improvement in the reconstruction–decomposition process. Neither of the proposed frameworks, however, can achieve satisfactory performance when facing a platform residual phase error (PRPE) arising from the instability of airborne platforms. More importantly, in spite of the significance of real-time processing requirements in remote sensing applications, these prior works have only focused on enhancing the quality of the formed image, not reducing the computational burden. To address these two concerns, this article presents a fast and unified joint SAR imaging framework where the dominant sparse objects and low-rank features of the image background are decomposed and enhanced through a robust LRSD. In particular, our unified algorithm circumvents the tedious task of computing the inverse of large matrices for image formation and takes advantage of the recent advances in constrained quadratic programming to handle the unimodular constraint imposed due to the PRPE. Furthermore, we extend our approach to ISAR autofocusing and imaging. Specifically, due to the intrinsic sparsity of ISAR images, the LRSD framework is essentially tasked with the recovery of a sparse image. Several experiments based on synthetic and real data are presented to validate the superiority of the proposed method in terms of imaging quality and computational cost compared to the state-of-the-art methods.

Next-generation wireless communication systems are believed to share the same spectrum previously allocated to radar applications. The coexisting communication system will cause harmful interference to the radar system. In this paper, we investigate the deployment of the Reconfigurable intelligent surface (RIS) to improve the performance of a Multiple-Input Multiple-Output (MIMO) Radar and Communication Coexis-tence (RCC) system. The RIS consists of a large number of nearly passive, and low-cost elements, which provides passive, and a relatively high beamforming gain by controlling the reflecting elements' reflection coefficients. Moreover, the RIS can eliminate the mutual interference between the radar and communication systems. To improve the sum-rate of the communication system subjected to the radar performance constraints, we design the transmit beamforming and the phase shifts for the RIS elements by using the local search approach. Numerical results verify the effectiveness of the utilization of the RIS.

Unmanned Aerial Vehicles (UAVs) have found compelling applications in intelligent logistics, search and rescue as well as in air-borne Base Station (BS). However, their communications are prone to both channel errors and eavesdropping. Hence, we investigate the max-min secrecy fairness of UAV-aided cellular networks, in which Cooperative Rate-Splitting (CRS) aided downlink transmissions are employed by each multi-antenna UAV Base Station (UAV-BS) to safeguard the downlink of a two-user Multi-Input Single-Output (MISO) system against an external multi-antenna Eavesdropper (Eve). Realistically, only Imperfect Channel State Information (ICSI) is assumed to be available at the transmitter. Additionally, we consider a realistic total power constraint and guarantee the specific Quality of Service (QoS) requirements of the legitimate users. To handle the worst-case channel uncertainty of the legitimate users and an external Eve, we conceive a robust secure resource allocation algorithm, which maximizes the minimum worst-case secrecy rate of the legitimate users. Based on the CRS principle, the transmitter splits and encodes the messages of legitimate users into common as well as private streams and the user having stronger CSI is asked to help the cell-edge user by opportunistically forwarding its decoded common message. In contrast to the existing schemes adopted in the literature for ensuring secure transmission of the first cooperative phase only, in our proposed solution the common message has a twin-fold mission. Explicitly, apart from serving as the desired message, it also acts as Artificial Noise (AN) for drowning out Eve without consuming extra power. This is in stark contrast to the conventional AN designs. In the second phase, the pure AN is directed towards the Eve, deploying a robust Maximum Ratio Transmitter (MRT) beamformer at the UAV-BS. To solve the resultant non-convex optimization problem we resort to the Sequential Parametric Convex Approximation (SPCA) method together with a bespoke initialization algorithm to avoid any failure due to infeasibility. Our simulation results confirm that the proposed secure transmission scheme outperforms the existing cooperative benchmarkers.

Future generations of cellular systems need to meet stringent requirements for bandwidth and latency. This entitles maximum utilization of the available wireless spectrum. Given that some of the spectrum bands are underutilized, e.g. radar band. In this paper, we study the utilization of the radar spectrum for commercial cellular usage for meeting future generations of cellular systems' spectral demands. We propose to avoid destructive high power interference from seaborne radar transmitters with high power that can saturate the cellular base station receivers by using a projection based approach. Additionally, in our design, the radar transmitters will be cooperating with cellular system by transmitting useful broadcast communication signal. Towards that, we study the channel between seaborne multiple input multiple output (MIMO) radar system and MIMO cellular systems. The high power radar signal is projected onto the small singular value subspace of the channel, and therefore, reaches the base station with low power. In our design, the small singular values are selected so that the received power at the cellular base stations is within acceptable cellular system power constraints. Simulation results show that the amount of power transmitted from the radar to the base station increases as the threshold increases, which allows the radar to be cooperatively capable of transmitting communication signals to cellular base stations. Moreover, as the threshold increases, the accuracy of localizing the radar target increases.

In this paper, we introduce an efficient application-aware resource allocation (RA) approach for wireless networks with hybrid traffic. In our model, users are running elastic or inelastic traffic. We use logarithmic and sigmoidal-like utility functions to represent delay tolerant applications and real time applications, respectively, running on the user equipment (UE). Our approach provides an efficient assignment of the spectral resources based on the quality of service (QoS) requirements of users' applications as well as the UEs' channel conditions. We proved that the proposed resource allocation optimization problem is convex and therefore can ensure optimal allocated resources while considering the QoS requirements of the UEs' applications and channels' conditions.

Spectrum auction has been considered a promising solution to release the under-utilized spectrum from primary spectrum licensee to potential secondary users. In this paper, we introduce an auction-based resource allocation scheme between a spectrum broker and base stations (BS)s that belong to a wireless service provider (WSP). Each BS submits the proportion of resources it wishes to lease from each auctioned spectrum band based on its true demand and the auctioneer's offered price. The spectrum broker leases the resource blocks (RB)s of its under-utilized spectrum bands to the WSP base stations using an iterative resource allocation algorithm. The proposed algorithm is based on a fair spectrum auction. The spectrum broker who manages the under-utilized spectrum resources plays the role of an auctioneer. First, we establish utility functions for the auctioneer and each participating BS. Then we maximize these utilities in iterations in order to achieve optimal price and allocated resources. Furthermore, we introduce a cheat-prevent auction mechanism based on ascending-bid auction to prevent greedy BSs from cheating activities to obtain lower prices for the auctioned resources. Finally we present simulation results on the performance of the proposed resource leasing mechanism.

Machine-to-Machine (M2M) networks being connected to the internet at large, inherit all the cyber-vulnerabilities of the standard Information Technology (IT) systems. Since perfect cyber-security and robustness is an idealistic construct, it is worthwhile to design intrusion detection schemes to quickly detect and mitigate the harmful consequences of cyber-attacks. Volumetric anomaly detection have been popularized due to their low-complexity, but they cannot detect low-volume sophisticated attacks and also suffer from high false-alarm rate. To overcome these limitations, feature-based detection schemes have been studied for IT networks. However these schemes cannot be easily adapted to M2M systems due to the fundamental architectural and functional differences between the M2M and IT systems. In this paper, we propose novel feature-based detection schemes for a general M2M uplink to detect Distributed Denial-of-Service (DDoS) attacks, emergency scenarios and terminal device failures. The detection for DDoS attack and emergency scenarios involves building up a database of legitimate M2M connections during a training phase and then flagging the new M2M connections as anomalies during the evaluation phase. To distinguish between DDoS attack and emergency scenarios that yield similar signatures for anomaly detection schemes, we propose a modified Canberra distance metric. It basically measures the similarity or differences in the characteristics of inter-arrival time epochs for any two anomalous streams. We detect device failures by inspecting for the decrease in active M2M connections over a reasonably large time interval. Lastly using Monte-Carlo simulations, we show that the proposed anomaly detection schemes have high detection performance and low-false alarm rate.

In this paper, we introduce a secure energy trading auction approach to schedule the power plant limited resources during peak hours time slots. In the proposed auction model, the power plant serving a power grid shares with the smart meters its available amount of resources that is expected during the next future peak time slot; smart meters expecting a demand for additional power participate in the power auction by submitting bids of their offered price for their requested amount of power. In order to secure the power auction and protect smart meters' privacy, homomorphic encryption through Paillier cryptosystem is used to secure the bidding values and ensure avoiding possible insincere behaviors of smart meters or the grid operator (i.e. the auctioneer) to manipulate the auction for their own benefits. In addition, we use a payment rule that maximizes the power plant's revenue. We propose an efficient power scheduling mechanism to distribute the operator's limited resources among smart meters participating in the power auction. Finally, we present simulation results for the performance of our secure power scheduling auction mechanism.

In this paper, we present our spectrum sharing algorithm between a multi-input multi-output (MIMO) radar and Long Term Evolution (LTE) cellular system with multiple base stations (BS)s. We analyze the performance of MIMO radars in detecting the angle of arrival, propagation delay and Doppler angular frequency by projecting orthogonal waveforms onto the null-space of interference channel matrix. We compare and analyze the radar's detectable target parameters in the case of the original radar waveform and the case of null-projected radar waveform. Our proposed spectrum-sharing algorithm causes minimum loss in radar performance by selecting the best interference channel that does not cause interference to the ithLTE base station due to the radar signal. We show through our analytical and simulation results that the loss in the radar performance in detecting the target parameters is minimal when our proposed spectrum sharing algorithm is used to select the best channel onto which radar signals are projected.

This paper explores a collocated overlapped-multiple-input multiple-output (MIMO) antenna architecture and a spectrum sharing algorithm via null space projection (NSP) for radar-communications coexistence. In the overlapped-MIMO architecture, the transmit array of a collocated MIMO radar is partitioned into a number of subarrays that are allowed to overlap. Each of the antenna elements has signals orthogonal to others in the same subarray and to the other subarrays. The proposed architecture not only improves sidelobe suppression to reduce interference to communications system, but also enjoys the advantages of MIMO radar without sacrificing the desirable characteristics such as beampattern and SNR gain. The radar-centric spectrum sharing then projects the radar signal onto the null space of the communications system's interference channel to avoid interference from the radar. Numerical results are presented that show the performance of the proposed design in terms of overall beampattern and sidelobe levels of the radar waveform and finally shows a comparison of the proposed system with existing collocated MIMO radar architectures.

In this paper, we consider a resource allocation with carrier aggregation optimization problem in long term evolution (LTE) cellular networks. In our proposed model, users are running elastic or inelastic traffic. Each user equipment (UE) is assigned an application utility function based on the type of its application. Our objective is to allocate multiple carriers resources optimally among users in their coverage area while giving the user the ability to select one of the carriers to be its primary carrier and the others to be its secondary carriers. The UE's decision is based on the carrier price per unit bandwidth. We present a price selective centralized resource allocation with carrier aggregation algorithm to allocate multiple carriers resources optimally among users while providing a minimum price for the allocated resources. In addition, we analyze the convergence of the algorithm with different carriers rates. Finally, we present simulation results for the performance of the proposed algorithm.

In this paper, we consider resource allocation optimization problem in cellular networks for different types of users running multiple applications simultaneously. In our proposed model, each user application is assigned a utility function that represents the application type running on the user equipment (UE). The network operators assign a subscription weight to each UE based on its subscription. Each UE assigns an application weight to each of its applications based on the instantaneous usage percentage of the application. Additionally, UEs with higher priority assign applications target rates to their applications. Our objective is to allocate the resources optimally among the UEs and their applications from a single evolved node B (eNodeB) based on a utility proportional fairness policy with priority to realtime application users. A minimum quality of service (QoS) is guaranteed to each UE application based on the UE subscription weight, the UE application weight and the UE application target rate. We propose a two-stage rate allocation algorithm to allocate the eNodeB resources among users and their applications. Finally, we present simulation results for the performance of our rate allocation algorithm.

In this paper, we consider resource allocation optimization problem in the fourth generation long-term evolution (4G-LTE) with elastic and inelastic real-time traffic. Mobile users are running either delay-tolerant or real-time applications. The users applications are approximated by logarithmic or sigmoidal-like utility functions. Our objective is to allocate resources according to the utility proportional fairness policy. Prior utility proportional fairness resource allocation algorithms fail to converge for high-traffic situations. We present a robust algorithm that solves the drawbacks in prior algorithms for the utility proportional fairness policy. Our robust optimal algorithm allocates the optimal rates for both high-traffic and low-traffic situations. It prevents fluctuation in the resource allocation process. In addition, we show that our algorithm provides traffic-dependent pricing for network providers. This pricing could be used to flatten the network traffic and decrease the cost per bandwidth for the users. Finally, numerical results are presented on the performance of the proposed algorithm.

Mobile cyberphysical systems have received considerable attention over the last decade, as communication, computing and control come together on a common platform. Understanding the complex interactions that govern the behavior of large complex cyberphysical systems is not an easy task. The goal of this paper is to address this challenge in the particular context of multimedia delivery over an autonomous aerial vehicle (AAV) network. Bandwidth requirements and stringent delay constraints of real-time video streaming, paired with limitations on computational complexity and power consumptions imposed by the underlying implementation platform, make cross-layer and cross-domain co-design approaches a necessity. In this paper, we propose a novel, low-complexity rate-distortion optimized (RDO) protocol specifically targeted at video streaming over mobile embedded networks. We test the performance of our RDO algorithm on simulation models developed for aerial mobility of multiple wirelessly communicating AAVs. Results show that our optimized streaming leads to 47% and 39% less video distortion with very little computational overhead compared to regular ACKed and non-ACKed transmission, respectively.

Analogous to the beneficial impact that mobility has on the throughput of unicast networks, this paper establishes that mobility can provide a similar gain in the order-wise growth-rate of the throughput for multicast networks. This paper considers an all-mobile multicast network, and characterizes its multicast capacity scaling. The scaling result shows that the growth-rate of the throughput in the all-mobile multicast network is order-wise higher compared to the all-static multicast network. Further, the paper considers a static-mobile hybrid multicast network, and establishes that, if there are sufficient number of mobile nodes (that is order-wise smaller than the total number of nodes) in the network, then mobile nodes can enhance the order behavior of the multicast throughput.