Integrated sensing and communication (ISAC) allows the same hardware platform and resources to function sensing and communication simultaneously, which reduces the hardware size and addresses the spectrum congestion concerns. However, the sharing of the hardware and resources of the sensing and communication functions raises resource managements. Traditional optimization approaches are developed based on rigid mathematical models in ISAC systems. However, they face computation complexity and may not achieve the desired performance under the dynamics of the ISAC system environments. Machine learning with ability in learning features/patterns of data as well as approximating mathematical models has recently proposed to effectively solve the complicated ISAC problems. In this survey, we thus provide a comprehensive literature review on applications of learning algorithms for ISAC systems. Particularly, we review learning approaches proposed for emerging issues in ISAC systems, including beamforming designing/tracking, waveform design, spectrum allocation, time allocation, and power allocation, angle of arrival (AoA)/angle of departure (AoD) estimation, signal classification, and security issues. Moreover, we present applications of advanced learning methods for wireless sensing, which is considered to be an emerging sensing service of the next-generation networks. We conclude the survey with highlighting technical issues of learning algorithms and discussing future research directions.

Wireless backhaul is a cost-effective and flexible alternative to wired backhaul, yet it suffers from unreliability. This paper studies the secrecy performance of a relay network with such unreliable wireless backhaul. Bernoulli process is adopted to model the wireless backhaul reliability. In the network, a relay employing time-switching-based radio frequency harvesting technique aids in forwarding the signal from the best transmitter. An eavesdropper, which is able to wiretap signals from both the transmitter and the relay, uses selection combining to maximize its received signal-to-noise ratio. Analytical expressions for secrecy outage probability, ergodic secrecy rate and non-zero achievable secrecy rate are derived, which can reveal the effect of the number of transmitters and backhaul reliability on the system performance. Furthermore, for the first time the impact of energy harvesting time fraction upon secrecy performances under unreliable backhaul is presented.

This paper considers a cognitive communication network, which consists of a flying base station deployed by an unmanned aerial vehicle (UAV) to serve its multiple downlink ground terminals (GTs), and multiple underlaid device-to-device (D2D) users. To support the GTs’ throughput while guaranteeing the quality-of-service for the D2D users, the paper proposes the joint design of D2D assignment, bandwidth, and power allocation. This design task poses a computationally challenging mixed-binary optimization problem, for which a new computational method for its solution is developed. Multiple binary (discrete) constraints for the D2D assignment are equivalently expressed by continuous constraints to leverage systematic processes of continuous optimization. As a result, this problem of mixed-binary optimization is reformulated by an exactly penalized continuous optimization problem, for which an alternating descent algorithm is proposed. Each round of the algorithm invokes two simple convex optimization problems of low computational complexity. The theoretical convergence of the algorithm can be easily proved and the provided numerical results demonstrate its rapid convergence to an optimal solution. Such a cognitive network is even more desirable as it outperforms a non-cognitive network, which uses a partial bandwidth for D2D users only.

Federated Learning (FL) as a promising technique is able to address the privacy issues in machine learning. However, due to the broadcast nature of wireless channel, one of the key challenges of FL is its vulnerability to wireless security threats. Thus, in this paper, we consider the model update security in FL. In particular, we propose to adopt a covert communication technique with which a friendly jammer transmits jamming signals to prevent a warden from detecting local model update transmissions of mobile devices in FL. The use of jamming signals reduces the transmission rate of the devices. Thus, we formulate an optimization problem that jointly determines the jamming power, local model transmission power, and local training accuracy to minimize the FL latency, given a security performance requirement. The problem is non-convex, and we propose an alternating descent algorithm to solve it. Extensive simulations are conducted and the results demonstrate the effectiveness and network performance improvement of the proposed algorithm.

This paper studies the spectrum allocation problem between spectrum service providers (SSPs) and terminals equipped with orthogonal frequency division multiplexing (OFDM) integrated radar and communication (IRC) systems. In particular, IRC-equipped terminals such as autonomous vehicles need to buy spectrum for their radar functions, e.g., sensing and detecting distant vehicles, and communication functions, e.g., transmitting sensing data to road-side units. The terminals determine their spectrum demands from the SSPs subject to their IRC performance requirements, while the SSPs compete with each other on the service prices to attract terminals. Taking into account the complicated interactions, a hierarchical Stackelberg game is proposed to reconcile the spectrum demand and service price, where the SSPs are the leaders and the terminals are the followers. Due to the spectrum constraints of the SSPs, we model the lower-layer subgame among the terminals as a generalized Nash equilibrium problem. An iterative searching algorithm is then developed that guarantees the convergence to the Stackelberg equilibrium. Numerical results demonstrate the effectiveness of our proposed scheme in terms of social welfare compared to baseline schemes

In this paper, the impacts of unreliable backhaul links on the secrecy performance of cooperative single carrier heterogeneous networks in the presence of eavesdroppers are investigated. A two-phase transmitter/relay selection scheme is proposed, where the best transmitter is selected to maximize signal-to-noise ratio at the relays in the first phase and the best relay is chosen in the second phase to minimize the signal-to-interference-plus-noise ratio of the eavesdroppers with the aid of a friendly jammer. Closed-form expressions are derived for the secrecy outage probability, probability of non-zero achievable secrecy rate, and ergodic secrecy rate. The asymptotic performance analysis is furthermore performed to explicitly reveal the impacts of unreliable backhaul links on the secrecy performance. Our results show that the diversity gain cannot be achieved in the presence of imperfect backhaul links.