Direct smartphone-to-satellite connectivity is currently under investigation in both 5G Advanced and 6G to extend the support of basic communication services to the most remote geographical areas. While current satellite connectivity systems are focused on offering a global service for emergency and text messaging, their support for other extended data services, such as multimedia services, remains limited due to the challenging wireless conditions of non-terrestrial networks (NTN). These limitations stem from the significantly longer propagation distances between the device and the satellite resulting in poor link budgets and high latencies. To address these challenges and improve the reliability of data transmission in satellite networks, leveraging repeated transmissions of transport blocks with both low-rate modulation schemes and incremental redundancy can be key. In this paper, the focus is on voice services. Particularly the problem of optimally selecting the modulation coding scheme and the number of scheduled repetitions to maximize the probability of successful voice packet reception within a fixed end-to-end latency. The feasibility of supporting voice calls over 5G-NTN under different practical conditions is then evaluated for the optimized scheduler. Link-level simulations are performed based on a 3GPP 5G-NTN model, integrating 5G NR waveforms, LDPC coding, and HARQ with incremental redundancy. Numerical results suggest that high-quality voice calls can be supported in 5G-NTN for a wide range of altitudes and elevation angles, even at 2,000 km satellite altitude, which is the highest altitude considered for LEO satellites. Moreover, numerical results indicate that HARQ retransmissions are unnecessary for supporting voice transmission, as proactively scheduling a large number of repetitions is sufficient. However, using both scheduled repetitions and HARQ retransmissions is more efficient in terms of radio resources and power consumption.

Nonlinear system design is often a multiobjective optimization problem involving the search for a design that satisfies a number of predefined constraints. The design space is typically very large since it includes all possible system architectures with different combinations of components composing each architecture. In this article, we address nonlinear system design space exploration through a two-step approach encapsulated in a framework called fast design space exploration of nonlinear systems (ASSENT). In the first step, we use a genetic algorithm to search for system architectures that allow discrete choices for component values or else only component values for a fixed architecture. This step yields a coarse design since the system may or may not meet the target specifications. In contrast to prior works on design space exploration that rely on forward design, we use an inverse design in step 2 to search over a continuous space and fine-tune the component values with the goal of improving the value of the objective function. We use a neural network (NN) to model the system response. The NN is converted into a mixed-integer linear program for active learning to sample component values efficiently. We illustrate the efficacy of ASSENT on problems ranging from nonlinear system design to the design of electrical circuits. Experimental results show that ASSENT achieves the same or better value of the objective function compared to various other optimization techniques for nonlinear system design by up to 53%. We improve sample efficiency by 6–$12\times $compared to reinforcement learning-based synthesis of electrical circuits.

In this paper, the problems of user offloading and resource optimization are jointly addressed to support ultrareliable and low latency communications (URLLC) in HetNets. In particular, a multi-tier network with a single macro base station (MBS) and multiple overlaid small cell base stations (SBSs) is considered that includes users with different latency and reliability constraints. Modeling the latency and reliability constraints of users with probabilistic guarantees, the joint problem of user offloading and resource allocation (JUR) in a URLLC setting is formulated as an optimization problem to minimize the cost of serving users for the MBS. In the considered scheme, SBSs bid to serve URLLC users under their coverage at a given price, and the MBS decides whether to serve each user locally or to offload it to one of the overlaid SBSs. Since the JUR optimization is NP-hard, we propose a low complexity learningbased heuristic method (LHM) which includes a support vector machine-based user association model and a convex resource optimization (CRO) algorithm. To further reduce the delay, we propose an alternating direction method of multipliers (ADMM)- based solution to the CRO problem. Simulation results show that using LHM, the MBS significantly decreases the spectrum access delay for users (by ↑ 93%) as compared to JUR, while also reducing its bandwidth and power costs in serving users (by ↑ 33%) as compared to directly serving users without offloading.

In this paper, a novel economic approach, based on the framework of contract theory, is proposed for providing incentives for LTE over unlicensed channels (LTE-U) in cellular networks. In this model, a mobile network operator (MNO) designs and offers a set of contracts to the users to motivate them to accept being served over the unlicensed bands. A practical model in which the information about the quality-of-service (QoS) required by every user is not known to the MNO and other users is considered. For this contractual model, the closed-form expression of the price charged by the MNO for every user is derived and the problem of spectrum allocation is formulated as a matching game with incomplete information. For the matching problem, a distributed algorithm is proposed to assign the users to the licensed and unlicensed spectra. The simulation results show that the proposed pricing mechanism can increase the fraction of users that achieve their QoS requirements by up to 45% compared to classical algorithms that do not account for users requirements. Moreover, the performance of the proposed algorithm in the case of incomplete information is shown to approach the performance of the same mechanism with complete information.

In this paper, the problem of cell association is studied in an Internet of things (IoT) system in which a set of devices deployed in a given area report physical events to a set of small base stations (SBSs) via uplink communication links. In this model, the key goal is to minimize the number of devices that report the same information to a given SBS by taking into account the spatial correlation between the IoT devices. In particular, the problem of correlation-aware cell association is formulated as a popular matching game in which the IoT devices are assigned to the SBSs to maximize the amount of information that is reported to the SBSs. To this end, the number of devices matched to every SBS must be maximized. For the formulated problem, a distributed two- level matching algorithm is proposed and the algorithm is proved to converge to a popular outcome. In that state, all the SBSs and devices prefer the matching that results from the proposed algorithm to any other possible matching. Simulation results show that the proposed algorithm allows the SBSs to collect up to 40\% more useful information compared to max sum-rate association algorithm.

To reap the benefits of cache-enabled small cell networks, new backaul management mechanisms are needed to prevent the predicted files that are downloaded at the small base stations (SBSs) for caching purposes from jeopardizing the urgent requests that need to be served via the backhaul. Such mechanisms must account for the heterogeneity of the backhaul that will encompass both wireless backhaul links (at various frequency bands) and a wired backhaul component. In this paper, the heterogeneous backhaul management problem is formulated as a minority game in which each SBS has to define the number of predicted files to download, without affecting the required transmission rate of the current requests. For the formulated game, it is shown that a unique fair proper mixed Nash equilibrium (PMNE) exists. A self-organizing reinforcement learning algorithm is then proposed and shown to converge to a unique Boltzmann-Gibbs equilibrium, which approximates the desired PMNE. Simulation results show that the performance of the proposed approach can be close to that of the ideal optimal algorithm while it outperforms a centralized greedy approach in terms of the amount of data that is cached without jeopardizing the quality-of-service of current requests.

In this paper, a novel approach for providing incentives for caching in small cell networks (SCNs) is proposed based on the economics framework of contract theory. In this model, a mobile network operator (MNO) designs contracts that will be offered to a number of content providers (CPs) to motivate them to cache their content at the MNO's small base stations (SBSs). A practical model in which information about the traffic generated by the CPs' users is not known to the MNO is considered. Under such asymmetric information, the incentive contract between the MNO and each CP is properly designed so as to determine the amount of allocated storage to the CP and the charged price by the MNO. The contracts are derived by the MNO in a way to maximize the global benefit of the CPs and prevent them from using their private information to manipulate the outcome of the caching process. For this interdependent contract model, the closed-form expressions of the price and the allocated storage space to each CP are derived. This proposed mechanism is shown to satisfy the sufficient and necessary conditions for the feasibility of a contract. Moreover, it is shown that the proposed pricing model is budget balanced, enabling the MNO to cover all the caching expenses via the prices charged to the CPs. Simulation results show that none of the CPs will have an incentive to choose a contract designed for CPs with different traffic loads.

In this paper, an incentive proactive cache mechanism in cache-enabled small cell networks (SCNs) is proposed, in order to motivate the content providers (CPs) to participate in the caching procedure. A network composed of a single mobile network operator (MNO) and multiple CPs is considered. The MNO aims to define the price it charges the CPs to maximize its revenue while the CPs compete to determine the number of files they cache at the MNO's small base stations (SBSs) to improve the quality of service (QoS) of their users. This problem is formulated as a Stackelberg game where a single MNO is considered as the leader and the multiple CPs willing to cache files are the followers. The followers game is modeled as a non-cooperative game and both the existence and uniqueness of a Nash equilibrium (NE) are proved. The closed-form expression of the NE which corresponds to the amount of storage each CP requests from the MNO is derived. An optimization problem is formulated at the MNO side to determine the optimal price that the MNO should charge the CPs. Simulation results show that at the equilibrium, the MNO and CPs can all achieve a utility that is up to 50% higher than the cases in which the prices and storage quantities are requested arbitrarily.

In this paper, we address the caching problem in small cell networks from a game theoretic point of view. In particular, we formulate the caching problem as a many-to-many matching game between small base stations and service providers' servers. The servers store a set of videos and aim to cache these videos at the small base stations in order to reduce the experienced delay by the end-users. On the other hand, small base stations cache the videos according to their local popularity, so as to reduce the load on the backhaul links. We propose a new matching algorithm for the many-to-many problem and prove that it reaches a pairwise stable outcome. Simulation results show that the number of satisfied requests by the small base stations in the proposed caching algorithm can reach up to three times the satisfaction of a random caching policy. Moreover, the expected download time of all the videos can be reduced significantly.