This article investigates the performance of a reconfigurable intelligent surface (RIS)-assisted full-duplex (FD) integrated satellite-terrestrial network (ISTN). Particularly, the RIS structure is incorporated into the ISTN topology to curb the effects of the undesirable self-interference (SI) resulting from the FD operation, cross-tier interference (CTI), and intracell interference (ICI). To examine this network performance, an optimization problem is formulated to maximize the sum rate of users by jointly optimizing the active beamforming and RIS reflection coefficients. To address the non-convexity of the optimization problem, it is decomposed into two subproblems: one which involves applying the fractional programming (FP) technique to find the optimal solution for the active beamforming variable, while the other subproblem constitutes the use of successive convex approximation (SCA) to optimize the passive beamforming variable. These subproblems are solved alternately until convergence. Numerical results presented herein confirm that the proposed algorithm outperforms the benchmark schemes.

The integration of a wireless backhaul network between a macro-cell base station (MBS) and multiple small-cell base stations (SBSs) with full-duplex (FD) technology at the SBSs offers significant potential to enhance spectrum efficiency in millimeter-wave (mmWave) communication systems. However, the FD mode also introduces an inherent trade-off between better time utilization and increased network interference, necessitating careful optimization of duplex modes at each SBS. In this paper, we propose a novel framework that jointly optimizes both the duplex mode selection for each SBS and the design of hybrid beamforming (BF) at the MBS and SBSs to maximize network throughput. Our framework alternately tackles two key subproblems: hybrid BF design and SBS duplex mode selection. For hybrid BF design, we employ a two-stage approach that combines a codebook-based radio frequency beam selection via a greedy algorithm with baseband BF optimization through a generalized power iteration method. For SBS duplex mode selection, we introduce the worst-mode switching (WMS) algorithm to efficiently find a near-optimal solution in polynomial time. Simulation results demonstrate that the proposed joint algorithm not only has low computational complexity but is also robust to various interference scenarios, including different ADC resolutions and varying levels of residual self-interference suppression. This adaptability results in superior sum-rate performance compared to conventional hybrid BF schemes and configurations where all SBSs operate exclusively in either full-duplex or half-duplex mode.

We propose a novel graph neural network (GNN) architecture for jointly optimizing user association, base station (BS) beamforming, and reconfigurable intelligent surface (RIS) phase shift in a multi-RIS aided multi-cell network. The proposed architecture represents BSs and users as nodes in a bipartite graph where the same type of nodes shares the same neural networks for generating messages and updating its representations, allowing for distributed implementation. In addition, we utilize a composite reflected channel estimation integrated between layers of the GNN structure to significantly reduce the signaling overhead and complexity required for channel estimation in a multi-RIS network. To avoid BS overload, load balancing is regularized in the training of the GNN and we further develop a collision avoidance algorithm to ensure strict load balancing at every BS. Numerical results show that the proposed GNN architecture is significantly more efficient than existing approaches. The results further demonstrate its strong scalability with network size and achieving a throughput performance approaching that of a centralized traditional optimization algorithm, without requiring individual RIS-reflected channels estimation and without the need for re-training or fine-tuning.

Amidst overwhelming demands for spectrum in the licensed wireless networks caused by the explosive breakthrough of the Internet of Things (IoT), the unlicensed band has been a tremendous resource in enhancing massive connectivity. This has also accelerated the creation of cutting-edge radio access technologies (RATs), such as 5G new unlicensed (NR-U) to support IoT networks. However, these efforts have been frustrated by several performance challenges, including increased interference which has led to the deterioration in the Quality-of-Service (QoS). Moreover, wireless engineers are tasked to develop RATs whose specifications comply with several inter-RAT coexistence regulations. In this article, we propose a joint IoT user selection and resource assignment algorithm to improve the performance of the 5G NR-U-enabled IoT system under QoS constraints with regulated interference to the Wi-Fi-enabled IoT system. Specifically, we formulate a mixed-integer nonlinear programming problem (MINLP) and decompose it into two subproblems, including user selection and power allocation. We adopt the difference of concave functions (DC) approach to solve the resulting optimization problems. We also exploit low-complexity algorithms based on matching theory for IoT user selection and dual-decomposition for power allocation. Especially, we derive a closed form of the power allocation expression using the Lagrangian method. Through various simulations, we demonstrate that the proposed algorithm enables multiuser diversity and significantly improves the spectral efficiency compared to the conventional scheme.

As next generation cellular networks become denser, associating users with the optimal base stations at each time while ensuring no base station is overloaded becomes critical for achieving stable and high network performance. We propose multi-agent online Q-learning (QL) algorithms for performing real-time load balancing user association and handover in dense cellular networks. The load balancing constraints at all base stations couple the actions of user agents, and we propose two multi-agent action selection policies, one centralized and one distributed, to satisfy load balancing at every learning step. In the centralized policy, the actions of UEs are determined by a central load balancer (CLB) running an algorithm based on swapping the worst connection to maximize the total learning reward. In the distributed policy, each UE takes an action based on its local information by participating in a distributed matching game with the BSs to maximize the local reward. We then integrate these action selection policies into an online QL algorithm that adapts in real-time to network dynamics including channel variations and user mobility, using a reward function that considers a handover cost to reduce handover frequency. The proposed multi-agent QL algorithm features low-complexity and fast convergence, outperforming 3GPP max-SINR association. Both policies adapt well to network dynamics at various UE speed profiles from walking, running, to biking and suburban driving, illustrating their robustness and real-time adaptability.

The integration of millimeter wave and higher frequencies into 5G and 6G networks raises concerns about potential conflicts with existing satellite services that operate in the same or adjacent frequency bands. This paper analyzes the co-channel interference and out-of-band (OOB) leakage power from terrestrial networks to satellites, and offers design criteria for terrestrial networks to protect existing satellite services. Specifically, we establish the power spectral density of a multicarrier transmitted signal, enabling us to derive the in-band and OOB emission powers for arbitrary pulse shaping and interpolation filters employed in a terrestrial transmitter. We then establish the cumulative distribution function (CDF) of the aggregated interference from terrestrial networks to a satellite receiver using stochastic geometry tools. Based on this CDF, we derive closed-form expressions that impose limits on terrestrial node density and spectrum emission masks, ensuring a near-zero satellite outage probability. By defining an interference threshold based on satellite protection criteria, our analysis enables the identification of an optimal trade-off between terrestrial node density and transmit power, providing robust theoretical guidance for formulating regulations pertaining to terrestrial networks. Extensive simulations validate our analysis results, demonstrating as an example the feasibility of cellular coexistence with LEO satellites in the 47.2-50.2 GHz band. Our approach effectively controls both in-band interference from a terrestrial network to fixed satellite services and OOB interference to passive Earth exploration satellite services in the adjacent band, while satisfying the required satellite interference thresholds.

This paper investigates the framework of cross-entropy (CE) based clustering and beamforming for mmWave-non-orthogonal multiple access (NOMA) system taking into consideration the impact of imperfect successive interference cancellation (SIC). For the design of clustering and beamforming, we adopt CE based machine learning algorithm that has the objective to obtain the statistical parameters by minimizing the cross-entropy between optimal and sampling distributions. By using CE based clustering, the number of clusters can be adjusted to strike a balance between the inter-cluster interference and intra-cluster interference introduced by imperfect SIC. Furthermore, the inter-cluster interference induced by spatial beamforming is further reduced using CE based beamforming, which can significantly enhance the system performance of mmWave-NOMA. Based on the result, we compute the power allocation by dividing it into the intra-cluster and inter-cluster power allocation problems. In particular, we derive the optimal intra-cluster power allocation in a closed form and obtain the condition to guarantee the minimum rate requirements of all the users. We next solve the inter-cluster power allocation using convex optimization technique. Data-intensive simulation results illustrate that our proposed algorithm outperforms the conventional algorithm such as$K$-mean based clustering and the number of clusters can be controlled using CE based clustering algorithm.

We study the joint optimization of beamforming, RIS phase shift, and association for the links of BS-user and RIS-user communications in a multi-cell wireless network aided by multiple RISs. Consider a network setting with many RISs, we can optimize the reflection of each RIS for a single associated user, even though the RIS will reflect the signals of all users. We first design the optimal BS transmit beamforming together with the phase shift of RIS in a closed form to maximize the effective channel gain (ECG). Then, we design two different BS-RIS-user association algorithms satisfying the load balancing constraint. The first algorithm uses worst connection swapping on both the BS-user and RIS-user links, whereas the second algorithm uses a simpler max-ECG rule for the RIS-user link because of no load balancing at the RIS. A joint algorithm alternates between BS-RIS-user association and beamforming/reflection design until convergence. The proposed algorithms not only have substantially lower complexity than existing algorithms, but also outperforms the conventional max SINR association and effectively exploits multiple RISs to boost the network sum rate.

In this paper, we deal with the performance analysis of a cumulative distribution function (CDF)-based multiuser scheduling scheme for downlink non-orthogonal multiple access (NOMA). With this scheme, the multiple users having relatively better channel gain are selected. We first statistically analyze the probability of user selection in terms of the weight of each user determining the time fraction. We confirm that equal weight ensures fair resource allocation. Under this equal weight condition for fairness, we derive the distribution of signal-to-interference-plus-noise ratio (SINR) when the specific user is selected. Simulation results show that with this scheme, the time resources can be allocated more fairly compared to the proportional fair scheduling scheme, especially under NOMA conditions. Further, by adjusting the weight of desired user, the average rate and selection probability can be controlled.

This paper presents novel ultrareliable and low-latency communication (URLLC) techniques for URLLC services, such as Tactile Internet services. Among typical use cases of URLLC services are teleoperation, immersive virtual reality, cooperative automated driving, and so on. In such URLLC services, new kinds of traffic such as haptic information including kinesthetic information and tactile information need to be delivered in addition to high-quality video and audio traffic in traditional multimedia services. Furthermore, such a variety of traffic has various characteristics in terms of packet sizes and data rates with a variety of requirements of latency and reliability. Furthermore, some traffic may occur in a sporadic manner but requires reliable delivery of packets of medium to large sizes within a low latency, which is not supported by current state-of-the-art wireless communication systems and is very challenging for future wireless communication systems. Thus, to meet such a variety of tight traffic requirements in a wireless communication system, novel technologies from the physical layer to the network layer need to be devised. In this paper, some novel physical layer technologies such as waveform multiplexing, multiple-access scheme, channel code design, synchronization, and full-duplex transmission for spectrally efficient URLLC are introduced. In addition, a novel performance evaluation approach, which combines a ray-tracing tool and system-level simulation, is suggested for evaluating the performance of the proposed schemes. Simulation results show the feasibility of the proposed schemes providing realistic URLLC services in realistic geographical environments, which encourages further efforts to substantiate the proposed work.

In uplink multiuser systems, different timing offset (TO) and/or carrier frequency offset (CFO) of multiple users cause multiuser interference (MUI) to a desired user. In this paper, we propose two different MUI cancellation methods, namely weighted parallel interference cancellation (WPIC) and adaptive interference cancellation filter (AICF), for uplink transmission with generalized frequency division multiplexing (GFDM) waveform over Rayleigh fading channel given the existence of TO and CFO of multiple users. The proposed WPIC performs MUI estimation using the detected data symbol of other users and obtains the optimal weight to maximize the signal-to-interference ratio (SIR) of the desired user. We also propose the AICF that aims to maximize the SIR of the desired user. Based on our SIR expression derived in closed-form in the presence of TO and CFO, the AICF is obtained by solving the Rayleigh quotient problem. Lastly, the impact of multiple antennas at the receiver on MUI cancellation is also analyzed to improve reliability. The simulation results show that the proposed WPIC and AICF effectively eliminate MUI and that the AICF can outperform the WPIC in terms of the SIR and spectral efficiency. In addition, the multiple receiver antennas effectively cancel MUI as the number of antennas goes to infinity.

In uplink multiuser systems, different timing offset (TO) and carrier frequency offset (CFO) of different users cause multiuser interference (MUI) to desired user. In this paper, we propose an optimal receiver filter for uplink transmission with generalized frequency division multiplexing (GFDM) when TO and CFO of different users exist. Proposed optimal receiver filter maximizes signal-to-interference ratio (SIR) of desired user with TO and CFO in Rayleigh channel. Based on SIR expression derived in closed form in the presence of TO and CFO, we obtain the optimal receiver filter by solving Rayleigh quotient problem. We show from the simulation that the proposed receiver filter effectively eliminates MUI caused by TO within cyclic prefix range and CFO compared to the conventional zero-forcing filter.

In this paper, we analyze the impacts of timing offset (TO), carrier frequency offset (CFO), and phase noise in orthogonal frequency division multiplexing (OFDM) and generalized frequency division multiplexing (GFDM) waveforms. As TO can be classified into four cases depending on the direction of offset, we provide the analysis of signal-to-interference ratio (SIR) for each of the cases when phase noise and synchronization errors of the desired user occur in frequency selective channel. We also propose the receiver filter for GFDM systems that is optimized to maximize SIR with CFO in additive white Gaussian noise channel. Simulation results show that GFDM is more sensitive to CFO than OFDM. We also confirm that GFDM systems using proposed receiver filter are robust against CFO compared with conventional systems.