Integrated sensing and communication (ISAC) technology is a key enabler for 6G Internet of Things (IoT) networks due to its efficient spectrum utilization and functional integration. By leveraging reconfigurable intelligent surfaces (RIS), ISAC systems can dynamically manipulate propagation environments, enhance signal quality and coverage in complicated scenarios, particularly for unmanned aerial vehicle (UAV) networks. This paper proposes a novel framework for jointly optimizing trajectory and beamforming in RIS-aided ISAC systems, where the downlink non-orthogonal multiple access (NOMA)-enabled UAV network is considered. The framework aims to maximize the average achievable rate (AAR) by jointly optimizing UAV trajectory, base station (BS) beamforming, and RIS phase shifts, under communication and sensing constraints. The maximization problem is decomposed into three subproblems, i.e., BS beamforming optimization, RIS phase shift and UAV trajectory design. Successive interference cancellation (SIC) is employed to transform the first two subproblems into semidefinite programming (SDP) problems, and the non-convex trajectory optimization is addressed by using the successive convex approximation (SCA) method. Numerical results demonstrate that the proposed RIS-UAV-assisted system significantly outperforms traditional ISAC schemes in terms of communication and sensing efficiency. Simulation results also reveal the superiorities of the joint optimization algorithm compared to its counterparts.

Fast underwater localization serves as a critical application of Internet-of-Underwater-Things (IoUT), such as underwater search and rescue. Narrowband sinusoidal pulses are very commonly used in locator beacons installed on flight recorders. A mobile anchor (e.g., autonomous underwater vehicle (AUV)) has to keep receiving the signal and measuring Doppler shift for a long time, so that enough information can be collected for reliable localization. The need of a long observation window is deeply rooted in the fact that the Doppler shift measurements are highly correlated when they are taken at closely located spots. In this paper, we will show that by replacing the narrowband beacon signal with a wideband one, high-accuracy positioning can be achieved within a short period of time. The basic idea is to simultaneously measure Doppler shift and time of arrival (ToA) from wideband signals, and the errors spaces corresponding to these two measurements are complementary even when they are taken at the same position. The Cramér-Rao bound (CRB) will be derived for such a system. In low-signal-to-noise ratio (SNR) regime, the observation window has to be prolonged for effective information extraction, and we will see that the wideband signals still have an edge over the narrowband signals in positioning accuracy. Efficient algorithms are designed for positioning and the closed-form positioning error is derived. We also show that the localization accuracy will experience a significant drop when ToA is replaced by time difference of arrival (TDoA), because the perfect complementation no longer holds. The performance of the proposed algorithm, along with corresponding comparisons, is verified through simulations over various parameters such as SNR, number of measurements, and length of observation window, etc.

Non-orthogonal multiple access (NOMA) is a promising technology garnering significant attention among the Internet of Things (IoT) community due to its superior spectral efficiency. This work addresses the rate region boundary enhancement of downlink NOMA systems under imperfect successive interference cancellation (SIC) with advanced improper Gaussian signaling (IGS), which provides additional degrees of freedom for system design. We investigate a universal scenario in which two users adopt improper signaling and their transmit powers are optimized. We first formulate the achievable rate of both users in terms of the impropriety degree of the IGS. First, the analytical expressions for the best improper transmission are characterized by jointly optimizing the users’ power and the impropriety degree for the perfect SIC case. Then, a deep Q network (DQN)-based approach is provided to find the rate region of the IGS-aided NOMA system under imperfect SIC. Simulations presented for the downlink NOMA system support the analysis, illustrating that IGS can efficiently enhance the rate region of the NOMA system compared to proper signaling.

Unmanned Aerial Vehicles (UAVs)-assisted networks play a pivotal role in both terrestrial base stations (BSs) and non-terrestrial networks (NTNs) due to their extensive coverage and collaborative decision-making capabilities. However, the presence of diverse node types, rapidly evolving requirements, and dynamic channel conditions poses substantial challenges for ground users (GUs) access, particularly in an unknown environment within BS-UAV-NTN integrated networks. To tackle these challenges, this paper introduces a novel approach-a deep Q-learning network (DQN)-based algorithm for UAVs deployment and an adaptive and load balancing (ALB) scheme for GUs access. This paper formulates the GUs access problem in BS-UAV-NTN networks as a maximization problem, transforming it into a Markov Decision Process (MDP) problem for UAVs deployment in unknown environment. The proposed solution includes a DQN-based UAVs deployment algorithm and an access scheme that prioritizes BSs and UAVs. Simulation results convincingly show that this access scheme outperforms traditional Q-learning and random schemes in terms of rewards and the number of accessed GUs.

Non-orthogonal multiple access (NOMA) is a promising technology to increase system throughput and accommodate massive connectivity via non-orthogonal resource allocation. This paper studies the benefits of the improper Gaussian signaling (IGS) technique on the two-user downlink NOMA system with imperfect successive interference cancellation (SIC) over Rayleigh fading channels by analyzing the outage probabilities of both the strong user (SU) and the weak user (WU). The SU is assumed to use proper Gaussian signaling (PGS), while the WU employs IGS. Only statistical channel state information (CSI) is available at the base station, which is more practical than perfect knowledge of the instantaneous CSI. Both users’ outage probabilities are formulated in terms of the impropriety degree of the WU’s IGS, taking into account the residual interference introduced by the imperfect SIC. The optimization problem is formulated to minimize the outage probability of one user by adjusting the transmitted power and the impropriety degree of the WU while maintaining a certain quality-of-service (QoS) of the other user. Numerical results on the NOMA system verify that IGS provides an opportunity to further improve the outage performance over the conventional PGS.

Non-orthogonal multiple access (NOMA) exhibits superiority in spectrum efficiency which is particularly essential in the Internet of Things (IoT) system involving massive number of device connections. This paper addresses the achievable rate improvement for the downlink NOMA system, in the context of imperfect successive interference cancellation (SIC), by means of the improper Gaussian signaling (IGS) technique. We investigate a basic scenario where the strong user transmits the conventional proper data, while the weak user adopts an improper signaling scheme. The users’ data rates are first formulated in terms of the impropriety degree of the IGS, under residual interference introduced by the imperfect SIC. In this way, analytical expressions for the best improper transmission can be characterized by jointly optimizing the user’s power and the impropriety degree, where their sufficient and necessary conditions are provided. When the strong user transmits with its maximum power, the IGS scheme always increases the achievable rate of the strong user while the weak user may also benefit. When the weak user transmits with its maximum power, such a scheme enables us optimize the achievable rate of the strong user under various levels of channel-to-noise ratios (CNR) and imperfect SIC. Finally, when both the users are imposed by quality of service (QoS) constraints, a Q-learning based solution is proposed to maximize their sum rate. Simulations on the downlink NOMA system support the analysis.

Adverse impacts of in-phase and quadrature-phase (I/Q) imbalance in both the transmitter (Tx) and receiver (Rx) are quantified for the generalized frequency division multiplexing (GFDM) based transmission over frequency selective fading channels. To this end, we first equip the standard signal-to-interference-plus-noise (SINR) performance evaluation with the ability to consider second-order noncircular (improper) signals, and thus precisely evaluate performance deterioration caused by I/Q distortions over the in-phase (I) and quadrature-phase (Q) channels of a transmission system. Next, we propose a novel means to evaluate the individual SINR contributions from both the channels of GFDM, and hence, provide more meaningful insights into the underlying wireless transmission in the presence of complex non-circularity. This is accompanied by an account of complete augmented second-order statistics of I/Q imbalanced GFDM waveforms which caters for various sources of complex improperness. Simulations in the GFDM system setting support our analysis.

Generalized frequency division multiplexing (GFDM) has become one of the most important waveform candidates for beyond 5G (B5G) communications. However, physical distortions, such as in-phase and quadrature (I/Q) imbalance caused by the imperfections of radio frequency (RF) components within direct-conversion transceivers (DCTs), may cause severe performance degradation in GFDM-based wireless systems. To this end, we first conduct a rigorous sum rate analysis to quantify the impact of I/Q imbalance in both the transmitter and the receiver on the GFDM wireless transmission. An efficient I/Q imbalance compensation scheme is next proposed based on pilots; this is achieved through a nonlinear least squares analysis of the joint channel and I/Q imbalance estimation, and a simple symbol detection procedure. For rigor, the Cramer-Rao lower bounds for both the I/Q imbalance parameters and the channel coefficients are also derived. The simulation results illustrate that the mean square error performance of the proposed estimator closely approaches the corresponding CRLB over static frequency selective channels, thus significantly reducing the sensitivity of GFDM DCTs to physical I/Q impairments.