The quest for higher wireless carrier frequencies spanning the millimeter-wave (mmWave) and Terahertz (THz) bands heralds substantial enhancements in data throughput and spectral efficiency for next-generation wireless networks. However, these gains come at the cost of severe path loss and a heightened risk of beam misalignment due to user mobility, especially pronounced in near-field communication. Traditional solutions rely on extremely directional beamforming and frequent beam updates via beam management, but such techniques impose formidable computational and signaling overhead. In response, we propose a novel approach termed trajectory-adaptive beam shaping (TABS) that eliminates the need for real-time beam management by shaping the electromagnetic wavefront to follow the user's predefined trajectory. Drawing inspiration from self-accelerating beams in optics, TABS concentrates energy along pre-defined curved paths corresponding to the user's motion without requiring real-time beam reconfiguration. We further introduce a dedicated quantitative metric to characterize performance under the TABS framework. Comprehensive simulations substantiate the superiority of TABS in terms of link performance, overhead reduction, and implementation complexity.

Reconfigurable intelligent surfaces (RISs) are an emerging technology for improving spectral efficiency and reducing power consumption in future wireless systems. This paper investigates the joint design of the transmit precoding matrices and the RIS phase shift vector in a multi-user RIS-aided multiple-input multiple-output (MIMO) communication system. We formulate a max-min optimization problem to maximize the minimum achievable rate while considering transmit power and reradiation mask constraints. The achievable rate is simplified using the Arimoto-Blahut algorithm, and the problem is broken into quadratic programs with quadratic constraints (QPQC) sub-problems using an alternating optimization approach. To improve efficiency, we develop a model-based neural network optimization that utilizes the one-hot encoding for the angles of incidence and reflection. We address practical RIS limitations by using a greedy search algorithm to solve the optimization problem for discrete phase shifts. Simulation results demonstrate that the proposed methods effectively shape the multi-beam radiation pattern towards desired directions while satisfying reradiation mask constraints. The neural network design reduces the execution time, and the discrete phase shift scheme performs well with a small reduction of the beamforming gain by using only four phase shift levels.

For all of the tumultuous revolutions in wireless technology over the past several decades, there have been a couple of constants. One is the overcrowding of radio bands, and the other are attempts to escape that congestion by exploiting higher and higher frequencies. And today, as engineers roll out 5G and plan for 6G wireless, they find themselves at a crossroads.

Multiuser cooperative communications represent a research area of great interest constituting a key technology for future wireless networks. In this study, the authors consider a scenario of multi‐user, single‐relay network, where each user employs a part of the relay's transmission power taking into account the constraint of the total transmission power of the relay. The problem of relay power allocation to individual users with a view to optimising the long‐term network performance is investigated. An adequate utility function is defined, in terms of outage probability, evaluating the cooperative network's performance due to the relay's cooperation. Taking into consideration the distributed nature of cooperative networks along with the coupled constraint of relay's total transmission power, the specific problem is transformed from a network‐level to a user‐level optimisation problem using a modern game‐theoretic tool. More specifically, the initial power allocation problem is formulated as a state‐based potential game for which the corresponding Nash equilibrium exists. Finally, a gradient learning mechanism is proposed to achieve the stationary state Nash equilibrium of the game that constitutes also the global optimal solution of the initial problem. The performance of the new proposed scheme is investigated through numerical simulations for various network parameters.

Secure communications constitute a major concern in wireless networks' design. Toward this direction, physical layer security can achieve confidential data transmission from an information-theoretic viewpoint. This paper focuses on investigating the secure transmission of information over Nakagami-m fading channels in the presence of an eavesdropper. Specifically, we propose a distributed scheduler that opportunistically exploits the time-varying fading channel characteristics with a view to maximizing the expected secrecy throughput of a wireless link. The proposed multi-threshold scheduling policy postpones the communication up to an acceptable deadline until the secure-optimal time instant is found, based on the optimal stopping theory. The performance of the proposed scheduler is evaluated through simulations for various link parameters, while its achieved secrecy gain is examined and compared with other heuristic schemes.

In this paper, we analyze the problem of spectrum leasing in cognitive radio networks using principles from matching theory. More specifically, we deal with the pairing problem among the primary and the secondary users and we propose two different matching schemes that take into account the preferences of each user in terms of performance. The first scheme is a one-to-one matching scheme that is based on the deferred acceptance algorithm whereas the second one refers to a one-to-many matching algorithm where each primary user may cooperate with more than one secondary user. The performance of both proposed schemes is finally investigated for various network parameters and some useful conclusions are drawn.

Spectrum leasing in cognitive radio networks has recently started to draw the attention of the researchers. According to this concept, the primary users own specific spectral resources and they decide how to lease them to the secondary users so as to optimize their performance. In this paper, we consider an overlay cognitive radio network where the secondary users are allowed to transmit simultaneously with the primary user in exchange for devoting a part of their power to relay the primary signal. Focusing on the distributed nature of cognitive radio networks, we propose an auction-based mechanism for the leasing process. The secondary users, who represent the bidders, decide the power that are willing to employ for relaying PU's data whereas the primary user has the role of the auctioneer deciding the duration of the leasing process as well as the leasing time allocated to each secondary user. Furthermore, three different scenarios are studied, depending on the channel state information available to the secondary users. The performance of the proposed mechanism is investigated through the numerical analysis and the advantages of complete knowledge of channel state information are highlighted.

In this paper, we focus on proactive radio resource management schemes that retain the quality of the individual connections by pre-reserving the needed resources in a cellular network. We propose a new scheme, which improves older proactive solutions. Typically, in such solutions an improvement in call dropping probability negatively impacts new call blocking probability. We improve this framework by careful, fine-grained time scheduling of the proactive resource management. We adopt optimal stopping theory for our solution. Our findings are quite promising for the broader framework of proactive resource management and mobile computing.