In spectrum pooling scenario, the spectral leakage of DFT-based OFDM signal can be divided into in-band-out-of-subband (IBOSB) radiation and out-of-band (OOB) radiation. A precoding scheme is proposed to suppress the IBOSB sidelobes. The precoding design is based on the generalized eigenvalue problem. Simulation results demonstrate that the proposed scheme can suppress the sidelobes significantly in contrast to the uncoded schemes. At the same time, the bit-error-rate (BER) performance of the precoded system over multipath fading channel is improved due to the frequency diversity.

Since the outbreak of COVID-19 pandemic in 2020, a dramatic loss of human life has occurred and this trend presents an unprecedented challenge to public health, economic systems and social operations. Hence, it is urgent for us to take some countermeasures to restrain and dispel epidemic diffusion to the uttermost. Data freshness plays an inevitable role in timely infestor determination during this process. However, existing works pay little attention to optimizing this indicator in health monitoring. To make up this research gap, in this paper, we propose a mixed game-based Age of Information (AoI) optimization scheme, where the edge-based wireless technologies and AI-empowered diagnostic bots are adopted. Firstly, we establish the system model for Epidemic Prevention and Control Center (EPCC)-based health state monitoring network, where ultimate biosensing data is transmitted from AI bots via edge servers. Then, upon deriving AoI expression with a closed form, the minimization goal between edge servers and bots is specified. Simultaneously, we reformulate the AoI optimization problem from the mixed game viewpoint (i.e., coalition formation game and ordinary potential game), and then propose two algorithms for cooperative order-based bot deployment and stochastic learning-based channel selection. Finally, compared with the typical baselines, the experiment result shows our scheme can reach the lower AoI value for biosensing data transmission under different parameter settings.

Efficient grant-free uplink transmission is critical in minimizing Age of Information (AoI) in multichannel Internet of Things (IoT) networks. But less attention has been paid to this topic especially when dynamic channel access attacks (DCAAs) exist. To bridge this gap, this article formulates the distributed channel access problem in AoI-oriented IoT networks, and then a reinforcement learning-based solution is put forward based on the theoretical results of the game theory. First, a utility maximization problem is formulated for each sensor node based on its average AoI under DCAAs with probabilistic ACK feedback. Second, the problem is transformed into two ordinary potential game (OPG) models, which are both proved to have at least one nash equilibrium (NE); and a distributed learning algorithm is proposed to reach the NE. Finally, extensive simulations are conducted to evaluate the proposal’s performance. Simulation results verify the effectiveness of the proposed algorithm in various parameters settings.

Mobile crowdsensing (MCS)-enabled data collection can be implemented in a cost-effective, scalable, and flexible manner. However, joint sensing data freshness and security assurance have not been fully investigated in the current research. To address these two concerns, the potential game and homomorphic encryption-based joint Age of Information (AoI) optimization and privacy-preservation scheme for MCS is put forward in this paper. At first, the AoI minimization and privacy preservation-oriented MCS system framework is established. Then, the AoI-based spectrum access strategies are derived by a potential game in detail, where the stochastic learning algorithm is used to reach the Nash Equilibrium (NE) solution. Next, based on the somewhat homomorphic encryption method, the encrypted sensing data can be submitted to the service provider (SP) for further processing, where the data content can only be known to mobile workers (MWs) and service requester (SR) with permission. Finally, the numerical results show that our proposed MCS system can simultaneously guarantee data freshness and system security at an acceptable cost.

Using the frequency‐division duplex long‐term evolution (LTE)‐based multistatic passive radar system for unmanned aerial vehicle (UAV) detection is considered. The typical outdoor terrestrial LTE eNodeBs and the broadband direct air‐to‐ground communications network eNodeBs are used to cover the UAVs at the low altitude and at the medium‐high altitude, respectively. The bistatic range measurements are obtained from multiple eNodeBs, and the hyperbolic positioning technique is used for target localisation. Since the existing LTE downlink signals are more suitable for the transmission of the communication data rather than target detection, a new LTE downlink signal feature termed as target localisation reference signals (TLRS) is designed exclusively for the passive radar application using the Costas frequency coding and the P4 phase coding. Compared with the existing LTE downlink signal features, the TLRS have better waveform properties and could potentially provide much higher target detection accuracy. Meanwhile, the TLRS occupy only 0.266% LTE downlink time–frequency resources within a time frame of 1 s when the bandwidth is 10 MHz and have no negative impact on the normal LTE downlink data transmission.

As one of the most important representatives for the edge intelligence, Unmanned Aerial Vehicle (UAV) can be used to provide the intelligent edge service, such as performing data collection, computation and transmission tasks in the air-to-ground networks. In the considered Sensed Data Collection Networks (SDCNs), the UAV is in charge of collecting and computing the sensed data from mobile ground nodes, and multiple performance metrics need to be jointly optimized, including the collected data’s Age of Information (AoI) and the Signal to Interference plus Noise Ratio (SINR) at the UAV from mobile sensor nodes, the data collection areas’ completeness, and the mobile sensor nodes’ energy consumption. Due to the absence of such a joint optimization framework, this paper formulates the mobile sensing nodes deployment problem in the UAV-aided SDCNs. Afterwards, the formulated problem is transformed into a utility maximization problem from the perspective of Coalition Formation Game (CFG), where the orders, actions, rule, and stability in the coalitions are analyzed and proved. Then, two algorithms are put forward to reach the NE (Nash Equilibrium) of the CFG by determining the mobile sensor nodes’ coalitions and positions. Finally, extensive simulations are conducted with different parameters settings to evaluate the correctness and effectiveness of the proposed algorithms. Results have shown that the mobile sensor nodes, which adopt the proposed cooperative order to formulate the coalitions, could reach lower AoI, larger SINR, higher data completeness, and less energy consumption than the typical Pareto order.