AI-driven mobility improvements are attracting growing attention in 5G and future mobile communication networks. To achieve a practically viable solution, it is important to determine the appropriate level of AI control in mobility management under different prediction accuracies. To address this, we propose three handover strategies that represent different levels of AI involvement in mobility control. Each strategy uses reference signal received power (RSRP) prediction based on a compact on-device long short-term memory (LSTM) model tailored for mobility scenarios. We evaluate their performance through system-level simulations under varying prediction accuracies in frequency range 2 (FR2) mobility scenarios. The results show that even assistive use of AI leads to noticeable improvements in handover performance. As prediction accuracy improves, highly AI-dependent strategies outperform assistive approaches. These findings indicate that compact AI models can provide practical benefits, and further improvements in prediction accuracy can enable fully AI-driven handover in future 6G systems.

Power amplifiers (PA) are the essential part of the wireless communication systems but generally exhibit non-linearity at the high voltage input. Non-linearity in the orthogonal frequency division multiplexing (OFDM) based transceiver can cause severe distortions for the both in-band and out-band signal because of the high peak to average power ratio (PAPR). As the system bandwidth goes higher, PA exhibit memory effect. Non-linearity and memory effect is usually handled efficiently with digital pre-distorter (DPD). DPD is modeled with Volterra series kind of polynomials specially generalized memory poly-nomial (GMP). Estimation of the coefficients of the GMP is non-trivial and involves lot of complexity when the order and the memory of the GMP are significantly high. In this paper, we propose AI-methods to select the top-P features of the GMP based DPD out of total$N$features (P$P$features during the training of the AI model. We also propose a perturbation based method, which perturbs the input and based on its effect on the output obtained from the trained model, finds the most suitable top-$P$features. At last, we show the performance of our methods by calculating the performance metrics such as error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR). We show that for the GMP with N = 602 features, AI based selected top-50 GMP features performs similar to the GMP with all 602 features, and thus the complexity is reduced by more than 99%.

The non-linear distortion caused by non-ideal radio frequency (RF) components especially the power amplifier (PA) limits the applications of higher order modulation and degrades power utilization efficiency. To improve the achievable rate in modern systems, it becomes critical to overcome the non-linear distortion so that we can maximize the opportunity of using higher order modulation such as 256QAM, 1024QAM and even 4096QAM at high transmission power. In this paper, we introduce an artificial intelligence (AI)-enabled non-linearity compensation scheme (AI-NC) to avoid the "model deficit" problem. The introduced AI-NC adapts the Echo State Network (ESN) to enable fast online training without additional training overhead. Furthermore, it is general enough to be used for any types of power amplifiers (PAs) with different non-linearity characteristics and different channel environments. It can also be used for the communication system using multiple antennas and supporting multiple users simultaneously. Simulation results and hardware-based tests show that the proposed AI-NC can drastically improve the link performance and/or coverage of higher order modulations in practice.

To provide coverage to the network devices distributed all over the globe, non-terrestrial networks (NTNs) have been recognized to complement and extend the terrestrial network to remote areas. The Low Earth Orbit (LEO) NTNs face several unprecedented challenges over 5G-NR protocol due to high differential delay and Doppler shifts which drastically impacts the performance of NR-NTN users. In this work, we investigate the impact of large differential delay and Doppler shifts within a NTN cell on the 5G-NR resource allocation and medium access control (MAC) protocol. We then propose an uplink zone-based scheduling technique to address the high differential delay and Doppler shifts for LEO satellites for LEO satellite-based NTNs. Finally, we validate the performance of the proposed strategy by comparing it with the conventional 5G-NR protocol through numerical simulations over the recently developed Samsung’s NR-NTN System Level Simulator.

Along with the surge of data traffic amount, Long Term Evolution (LTE)-based Device-to-Device (D2D) communication is emerging as a key data traffic offloading technology. However, current LTE-based D2D communication has limitations such as the lack of feedback mechanism, causing difficulty for efficient radio resource use. In this paper, we propose a feedback mechanism as well as a feedback-based rate adaptation scheme to increase the spectral efficiency of LTE-based D2D communication. In particular, the proposed feedback mechanism between Transmitter (Tx) and Receivers (Rxs) is designed to minimize signaling overhead. Thanks to the feedback mechanism, the proposed rate adaptation scheme makes D2D Tx use the highest Modulation and Coding Scheme (MCS) level while guaranteeing reliable transmission to all Rxs. Through extensive simulations, we verify that the proposed solution achieves solid goodput performance under various channel environments.

Device-to-Device (D2D) communication underlaying cellular network has received much attention as a means to utilize cellular resources in a more efficient manner. Since D2D communication range is expected to be shorter than the normal cellular communication range, there are potential advantages such as reduced delay, reduced number of hops, improved spectral efficiency, and offloaded cellular traffic. In this paper, a semi- distributed resource selection scheme for D2D communication is proposed to efficiently reuse cellular resources. In addition, we propose interference avoidance scheme and D2D power control scheme to enhance D2D performance and minimize the degradation of cellular network performance, respectively. We demonstrate that the proposed semi-distributed algorithm outperforms the existing scheme by as much as 64% in terms of sum throughput of D2D users.

Recently, Device-to-Device (D2D) communication has been under discussion with great attention. In particular, D2D discovery, as the first step of D2D communication, is a matter in dispute. In this paper, we propose a novel D2D discovery protocol to significantly reduce the participation delay, i.e., the time duration from the user's entrance into the network to the first beacon transmission, compared with the existing protocols incurring long participation delay. For the verification of our work, we evaluate our work via both mathematical analysis and simulation. Through our thorough investigation with a few resource selection algorithms, we observe that our proposed scheme reduces participation delay by 66% and that it barely degrades the performance in guaranteed distance among users which share the same resource.

Device-to-Device (D2D) communications allow devices to communicate directly without going through infrastructure. It is considered a promising solution to improve communication performance and network capacity of LTE-Advanced system. From the perspective of User Equipment (UE), additional energy consumption is required to support D2D communications. In addition, recent device supports multiple Radio Access Technologies (RATs) with high energy consumption. For an energy efficient D2D communication, the most significant problem is how to detect proximal devices and to establish a D2D link in a timely manner. In this paper, we propose a D2D discovery and link setup procedure, and analyze its performance in terms of energy consumption and delay by utilizing the measurement results with real LTE smartphones. Based on the analysis results, we conclude that there is a trade-off relation between energy consumption and delay performance.