Reinforcement learning (RL) has emerged as a promising anti-jamming solution in frequency-hopping (FH) communication systems, owing to its dynamic anti-jamming capability independent of FH patterns. Nevertheless, conventional RL-based methods may require extensive retraining when previously encountered jamming signals reappear after a long period because of the obliviousness of RL. To address this issue, this paper proposes a memory-driven anti-jamming approach for FH communications. Inspired by reinforced continual learning (RCL), the proposed approach employs an adaptive expansion mechanism of sub-deep neural networks to efficiently capture and retain jamming patterns. Simulation results demonstrate that the proposed approach can make more rapid anti-jamming decisions compared to conventional methods when encountering previously observed jamming patterns.

Ambient backscatter communication (AmBC) is a crucial technology for addressing the energy consumption challenge in green Internet of Things through the reflection or absorption of surrounding radio frequency (RF) signals. Nevertheless, it grapples with the intricacies of ambient RF signal and the round-trip path loss. For the traditional detectors, the incorporation of pilot sequences results in the reduction in spectral efficiency. Furthermore, traditional energy-based detectors are inherently susceptible to a notable error floor issue, attributed to the co-channel direct link interference (DLI). Consequently, this paper proposes a blind symbol detector without the prior knowledge concerning the channel state information, signal variance, and noise variance. Leveraging the intra-symbol differential amplitude shift keying (IDASK) scheme, this detector effectively redirects the majority of DLI energy towards the largest eigenvalue of the received sample covariance matrix, thereby utilizing the second largest eigenvalue for efficient symbol detection. Simulation results demonstrate that the proposed blind detector exhibits a significant enhancement in symbol detection performance compared to traditional counterparts.

An increasing number of field robots would be used for mission-critical tasks in remote or post-disaster areas. Due to the limited individual abilities, these robots usually require an edge information hub (EIH), with not only communication but also sensing and computing functions. Such EIH could be deployed on a flexibly-dispatched unmanned aerial vehicle (UAV). Different from traditional aerial base stations or mobile edge computing (MEC), the EIH would direct the operations of robots via sensing-communication-computing-control ($\textbf {SC}^{3}$) closed-loop orchestration. This paper aims to optimize the closed-loop control performance of multiple$\textbf {SC}^{3}$loops, with constraints on satellite-backhaul rate, computing capability, and on-board energy. Specifically, the linear quadratic regulator (LQR) control cost is used to measure the closed-loop utility, and a sum LQR cost minimization problem is formulated to jointly optimize the splitting of sensor data and allocation of communication and computing resources. We first derive the optimal splitting ratio of sensor data, and then recast the problem to a more tractable form. An iterative algorithm is finally proposed to provide a sub-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm. We also uncover the influence of$\textbf {SC}^{3}$parameters on closed-loop controls, highlighting more systematic understanding.

Network-enabled robots have become important to support future machine-assisted and unmanned applications. To provide high-quality services for wide-area robots, hybrid satellite-terrestrial networks are a key technology. Via hybrid networks, computation-intensive and latency-sensitive tasks of robots can be offloaded to mobile edge computing (MEC) servers. However, due to the mobility of mobile robots and unreliable wireless network environments, excessive local computations and frequent service migrations may significantly increase the service delay. To address this issue, this paper aims to minimize the average task completion time for MEC-based offloading for satellite-terrestrial-network-enabled robots. Different from conventional mobility-aware schemes, the proposed scheme is to make the offloading decision by jointly considering the mobility control of robots. A joint optimization problem of task offloading and velocity control is formulated. Using Lyapunov optimization, the original optimization is decomposed into a velocity control subproblem and a task offloading subproblem. Then, based on the Markov decision process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed. Simulation results show that the proposed scheme can effectively reduce the service delay.

Benefiting from the fusion of communication and intelligent technologies, network-enabled robots have become important to support future machine-assisted and unmanned applications. To provide high-quality services for robots in wide areas, hybrid satellite-terrestrial networks are a key technology. Through hybrid networks, computation-intensive and latency-sensitive tasks can be offloaded to mobile edge computing (MEC) servers. However, due to the mobility of mobile robots and unreliable wireless network environments, excessive local computations and frequent service migrations may significantly increase the service delay. To address this issue, this paper aims to minimize the average task completion time for MEC-based offloading initiated by satellite-terrestrial-network-enabled robots. Different from conventional mobility-aware schemes, the proposed scheme makes the offloading decision by jointly considering the mobility control of robots. A joint optimization problem of task offloading and velocity control is formulated. Using Lyapunov optimization, the original optimization is decomposed into a velocity control subproblem and a task offloading subproblem. Then, based on the Markov decision process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed. The convergence and complexity of the improved RL algorithm are theoretically analyzed, and the simulation results show that the proposed scheme can effectively reduce the offloading delay.

Orthogonal time frequency space (OTFS) system constitutes an effective structure conceived for efficiently utilizing the channel information, which is capable of achieving a promising transmission performance in high-mobility environment. To extract enough channel diversity, a two-dimensional Fourier transformation combined with a pulse shape is designed at the OTFS transmitter. Consequently, the amplitude of OTFS signals may fluctuate drastically, owing to the combined dependency of the OTFS transformation and the pulse shape. To quantify the amplitude fluctuation, we investigate the peak-to-average power ratio (PAPR) of OTFS signals, for a large amount of data in the delay-Doppler domain. We first reveal that when the number of data points approaches to infinity, based on central limit theorems for dependent variables, the complex-valued OTFS signals weakly converge to a Gaussian distribution. Then, according to the extremal theory of the Chi-squared process for stationary OTFS signals, an accurate expression of the PAPR distribution is derived, depending on the transmit pulse and the number of data points. It is also demonstrated that upon modifying the exponential factor, the analytical PAPR expression is applicable for the non-stationary Gaussian distribution caused by the bandlimited pulse with a large roll-off factor. Simulation results confirm the accuracy of the analytical PAPR probability for practical conditions.

An N-continuous generalized frequency division multiplexing (GFDM) transceiver architecture is studied with the objective of striking a balanced trade-off between the bit error rate (BER) and the sidelobe suppression performance. More specifically, in the proposed N-continuous GFDM signaling, the basis signals constructed allow one to make the GFDM signal N-continuous and attain a compact spectrum as an explicit benefit of sidelobe suppression. We further reveal that compared to conventional N-continuous orthogonal frequency division multiplexing (NC-OFDM), N-continuous GFDM introduces relatively low interference through evaluating the signal-to-interference ratio (SIR). Secondly, a signal recovery algorithm is presented by constructing a recovery matrix to eliminate the interference. Finally, it is demonstrated that the proposed N-continuous GFDM scheme outperforms its N-continuous OFDM counterpart in terms of sidelobe suppression, while achieving moderate BER performance degradation as opposed to original OFDM.

N-continuous (NC) precoder is a class of efficient sidelobe suppression techniques by smoothing the consecutive multicarrier symbols at the cost of additional interference to the transmit signals. In this paper, we firstly investigate the N-continuous precoder in recently developed Subcarrier-Index Modulation Orthogonal Frequency Division Multiplexing (SIM-OFDM) systems, when the channel is modeled by frequency-selective fading. On the one hand, the Signal to Interference Plus Noise Ratio (SINR) is derived through theoretical analysis. Furthermore, with the quantified SINR, the achievable rate is first acquired for evaluating the performance of NC precoded SIM-OFDM. We also show that the above-mentioned performance analysis matches well with the simulation results.

In this paper, N-continuous (NC) precoding is first combined with spatial modulation (SM) - orthogonal frequency division multiplexing (OFDM) system for sidelobe suppression, and the bit-error rate (BER) performance of the considered system is analyzed over fading channels. On the one hand, the introduced interference of the NC precoder is modeled and analyzed for SM-OFDM, so as to achieve the theoretical BER performance. On the other hand, we show that the simulation results support the theoretical analysis in different parameter configurations of the considered NC precoded SM-OFDM system.

Generalized frequency division multiplexing (GFDM) is a recent multicarrier 5G waveform candidate with the flexibility of pulse shaping filters. However, the flexibility of choosing a pulse shaping filter may result in intercarrier interference (ICI) and intersymbol interference (ISI), which becomes more severe in a broadband channel. In order to eliminate the ISI and ICI, based on discrete Gabor transform (DGT), in this paper, a transmit GFDM signal is first treated as an inverse DGT, and then a frequency-domain DGT is formulated to recover (as a receiver) the GFDM signal. Furthermore, to reduce the complexity, a suboptimal frequency-domain DGT called local DGT is developed. Some analyses are also given for the proposed DGT-based receivers.

N-continuous orthogonal frequency division multiplexing (OFDM) is a promising sidelobe suppression technique by enhancing the continuity between adjacent symbols with higher-order derivatives. However, it has high computational complexity due to the large-scale matrix operations in frequency domain. In this paper, a low-complexity time-domain signal processing (TDSP) algorithm for N-continuous OFDM is proposed. Based on linear combination of basis vectors, it utilizes small-scale matrix operations to generate the coordinates to construct N-continuous OFDM signal. Simulation results approve that the proposed algorithm achieves dramatic complexity reduction with identical sidelobe suppression and BER performance compared to conventional frequency-domain processed N-continuous OFDM.