Generalized frequency division multiplexing (GFDM) has become one of the most important waveform candidates for air interface in 5G communications. However, like the standard orthogonal frequency division multiplexing (OFDM), its direct-conversion receivers are vulnerable to radio frequency impairments, due to their cost and size constraints. In this paper, the performance deterioration resulting from inphase/quadrature (I/Q) imbalance is first analyzed in terms of signal-to-interference plus noise ratio (SINR) for typical GFDM receivers. Next, a blind adaptive I/Q imbalance compensator based on normalized complex least mean square (NCLMS), originally designed for OFDM receivers, is extended for GFDM ones. In order to provide more physical insight into its compensation capability, a full second-order performance assessment is established, via a joint consideration of the weight error variance and its complementary variance of the proposed compensator in both the transient and steady-state stages. Apart from an accurate evaluation on its overall self-image attenuation performance, the proposed full second-order analysis is also able to quantify the individual contributions from both the I and Q channels of the compensator, an important finding missing in the literature and not possible to discover by using the standard variance analysis only. This analysis also facilitates theoretical quantifications of SINR improvements by NCLMS for GFDM receivers. Simulations on GFDM waveforms support the 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 (Tx) and the receiver (Rx) 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 (NLS) analysis of the joint channel and I/Q imbalance estimation, and a simple symbol detection procedure. For rigor, the Cramer-Rao lower bounds (CRLBs) for both the I/Q imbalance parameters and the channel coefficients are also derived. Simulation results illustrate that the mean square error (MSE) 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.

Flying Ad Hoc Networks (FANETs) are essential for a wide range of military and civil applications as they significantly reduce mission duration and improve coverage compared to using a single Unmanned Aerial Vehicle (UAV). Nevertheless, the dynamic and mobile nature of FANETs, coupled with fluctuating data traffic patterns, pose significant challenges for adaptive routing and efficient packet delivery. At present, most of the existing routing protocols for FANETs are designed based on mobility or topology information without taking into account data traffic and the associated joint decision-making approach. This narrow focus can result in poor network performance, characterized by longer delivery delay and lower delivery ratio, which can significantly impair the overall Quality of Service (QoS) in FANETs. To address these challenges, this paper proposes the Joint Prediction and Entropy weight-based (JPE) routing protocol by considering both current and future network conditions. The protocol uses a Long Short-Term Memory (LSTM) model to predict and obtain the mobility, buffer available size, and Link Expiration Time (LET) of each Neighbouring UAV (NU) in order to avoid high-mobility, high-traffic, and weak-link UAVs and establish an appropriate path. The routing decision problem is then formulated as an optimization problem and solved using the proposed Entropy Weight-based Multi-Metric (EWMM) approach to make fast and joint routing decisions. The integrated prediction and decision process considers both current and future multi-metric factors that can lead to packet loss or delay. Simulation results demonstrate the effectiveness of the LSTM-based Joint Prediction (JP) model and show that the JPE protocol outperforms the PAP and SPA protocols, improving Packet Delivery Ratio (PDR) and delay performance by 30.13% and 31.24% respectively.

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.

Sparse code multiple access (SCMA) has exhibited superiority in spectrum efficiency, which is particularly essential in the Internet of Things (IoT) system where a steadily increasing number of device connections are accommodated. However, the computational complexity of the conventional message passing algorithm (MPA) for the multiuser SCMA detection increases exponentially with the degree of resource nodes (RNs). To address this issue, two low complexity MPA schemes are proposed by utilizing the sparse feature of codewords. First, a sorted MPA (SMPA) detector is introduced to reduce the message exchanging from RNs to variable nodes (VNs) by dropping the redundant superposed constellation points outside a belief interval. Next, in order to further speed up the sorting process of the Euclidean distances between the received signal and codeword combinations, a deep neural network aided MPA (DNNMPA) is proposed, in which, the DNN behaves as a function approximator to generate the belief interval and operates in parallel with the initialization procedure before iterative message passing. Simulation results illustrate that the proposed SMPA and DNNMPA detectors significantly reduce the computational complexity of the conventional MPA one, but with comparable decoding capabilities, for the uplink SCMA system.

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.