Given the significant potential of orthogonal time frequency space (OTFS) signals in advancing integrated sensing and communication (ISAC) systems, this paper investigates the target sensing enhancements in ISAC-OTFS systems. Specifically, a customized inter-range-cell interference (IRCI)-free range reconstruction method is proposed for high-quality target sensing. The resultant mainlobe signal-to-noise ratio (SNR) is derived as a function of OTFS waveform parameters, and the achievable maximum SNR is deduced to provide a benchmark for subsequent simulations. The optimization of OTFS waveforms for maximizing the target sensing SNR is formulated with hardware realization constraints (i.e., peak to average power ratio (PAPR) and energy constraints). The resultant non-convex multi-ratio fractional programming problem is solved using a hybrid algorithm named multi-ratio fractional programming and partial successive convex approximation (MR-FP-PSCA). Finally, the numerical results, including the IRCI-free target sensing method and the proposed optimization algorithm, demonstrate the effectiveness of the developed schemes.

Integrated sensing and communication (ISAC) systems improve unmanned aerial vehicles (UAVs) efficiency and safety for low-altitude economy, but struggle with target identification due to reliance on primary radar. Thus, inspired by secondary surveillance radar (SSR) systems that can recognize target identities, this paper proposes an target identity recognition (TIR)-enabled ISAC technology to further improve UAV identification and tracking capabilities, optimizing airspace management and collision avoidance. Firstly, a novel integrated waveform design for TIR-enabled ISAC systems is proposed, where the waveform carries the interrogation/reply information of TIR through pulse modulation, and maintains a low-level amplitude (LLA) during pulse interval, allowing communication data to be modulated onto the carrier phase. Then, the proposed integrated waveform shows a trade-off between TIR bit error rate (BER) and communication symbol error rate (SER) due to the LLA, with an algorithm developed to optimize LLA for minimizing TIR BER while maintaining low communication SER.

Integrated sensing and communication (ISAC) has emerged as a key enabling technology for 6G networks. This paper addresses the joint beamforming design challenge in ISAC systems to prevent sensing information leakage to legitimate communication users. We propose a novel optimization framework that leverages semidefinite relaxation (SDR) and alternating optimization (AO) techniques to jointly design the base station beamforming vectors and the phase shift matrix of the RIS deployed at the radar target. The proposed approach ensures the satisfaction of communication SINR requirements while effectively suppressing the eavesdropping capability of sensing eavesdroppers. Simulation results demonstrate that our method achieves near-complete eavesdropping elimination compared to RIS-free and random-phase RIS configurations, enabling secure decoupling of sensing and communication functionalities.

Low Earth orbit (LEO) satellite communications are the key enabler for achieving 6G ubiquitous connectivity. With the rapid progress of small satellite technology and the surging demands on direct-to-satellite services, a global wave of building LEO satellite constellations has been arisen. LEO satellite communications are the typical high mobility scenarios and suffer from severe Doppler effects. To overcome this challenge, orthogonal time frequency space (OTFS)-based LEO satellite communications have recently been studied, which exploit high mobility to obtain delay-Doppler diversity. However, due to limited satellite transmit power and very long propagation distance, the satellite-to-ground (S2G) links are very weak, and also suffer from inter-beam and inter-satellite interference. In this paper, we study coordinated multi-satellite transmission for OTFS-based LEO satellite communications to significantly improve the performance of S2G transmission, through enabling multiple satellites to cooperatively serve ground users. Furthermore, considering different delay and Doppler offsets among cooperative LEO satellites, we propose simultaneous pilots-based aggregate channel estimation (SP-ACE) scheme to improve channel estimation, which aggregately estimates the channels in S2G joint transmission by regarding the channels of all cooperative links as a single channel. Besides integer Doppler, we also consider fractional Doppler and propose three-stage peak-searching correlation (PSC)-based fractional Doppler estimation. Finally, simulations are conducted and the results demonstrate the effectiveness of the proposed coordinated multi-satellite transmission scheme, SP-ACE and three-stage PSC fractional Doppler estimation schemes.

With the commercial deployment of low earth orbit (LEO) satellites, the future integrated 6G-satellite system represents an excellent solution for ubiquitous connectivity and high-throughput data service to massive users. Due to the heterogeneity of users’ traffic profiles, uneven traffic distribution among beams or users often occurs in LEO satellite systems. Conventional satellite payloads with fixed beam radiation patterns may result in large gaps between requested and allocated capacity. The advances of flexible satellite payloads with dynamic beamforming capabilities enable spot beams to adjust their coverage and adaptively schedule users, thus offering spatial-temporal domain flexibility. Motivated by this, as an early attempt, we investigate how adaptive beam patterns with flexible user scheduling schemes can help alleviate mismatches of requested-transmitted data in uneven-traffic and full-frequency reuse LEO systems. We formulate an optimization problem to jointly determine beam patterns, power allocation, user-LEO association, and user-slot scheduling. The problem is identified as mixed-integer nonconvex programming. We propose an efficient iterative algorithm to solve the problem by first determining beam patterns and user associations at the frame scale, followed by optimizing power allocation and user scheduling at the timeslot scale. The four-decision components are iteratively updated to improve the overall performance. Numerical results demonstrate the benefits brought by adaptive beam patterns and their effectiveness in reducing the mismatch effect in uneven-traffic LEO systems.

The free energy principle and its corollary, the active inference framework, serve as theoretical foundations in the domain of neuroscience, explaining the genesis of intelligent behavior. This principle states that the processes of perception, learning, and decision making-within an agent-are all driven by the objective of "minimizing free energy," evincing the following behaviors: learning and employing a generative model of the environment to interpret observations, thereby achieving perception, and selecting actions to maintain a stable preferred state and minimize the uncertainty about the environment, thereby achieving decision making. This fundamental principle can be used to explain how the brain processes perceptual information, learns about the environment, and selects actions. Two pivotal tenets are that the agent employs a generative model for perception and planning and that interaction with the world (and other agents) enhances the performance of the generative model and augments perception. With the evolution of control theory and deep learning tools, agents based on the FEP have been instantiated in various ways across different domains, guiding the design of a multitude of generative models and decision-making algorithms. This letter first introduces the basic concepts of the FEP, followed by its historical development and connections with other theories of intelligence, and then delves into the specific application of the FEP to perception and decision making, encompassing both low-dimensional simple situations and high-dimensional complex situations. It compares the FEP with model-based reinforcement learning to show that the FEP provides a better objective function. We illustrate this using numerical studies of Dreamer3 by adding expected information gain into the standard objective function. In a complementary fashion, existing reinforcement learning, and deep learning algorithms can also help implement the FEP-based agents. Finally, we discuss the various capabilities that agents need to possess in complex environments and state that the FEP can aid agents in acquiring these capabilities.

In this paper, we develop a secure multi-layer mobile edge computing (MEC) system where an unmanned aerial vehicle (UAV) equipped with a reconfigurable intelligent surface (RIS) acts as an aerial edge server and assists the offloading from multiple ground users to a base station (BS), in the presence of a full-duplex active eavesdropper (AE). To enhance the computing performance, we consider a partially offloading scheme where the computational task at each user can be executed at itself and offloaded to the UAV edge server and the BS via the UAV-enabled RIS, respectively. To maximize the total number of secure computing tasks among all users, we design a low complexity iterative algorithm by jointly optimizing the RIS phase shift, UAV deployment, power and computing resource allocation subject to certain power constraints. Numerical results show that compared to benchmark offloading schemes, our proposed UAV-RIS aided multi-layer MEC design improves the computing performance by at least 12.91%. Numerical results also demonstrate the impact of the full-duplex AE and validate the robustness of our proposed solution.

Orthogonal time-frequency space (OTFS) is a promising technique for high mobility wireless communications, as it approaches a sparse representation of doubly-selective fading channel and can obtain delay-Doppler (DD) diversity gain. However, in practical OTFS systems, fractional channel parameter is a great challenge and will seriously deteriorates the sparsity and the performance of channel estimation. In this paper, we propose a novel channel estimation scheme for doubly selective channel in the presence of both fractional Doppler and fractional delay. First, we analyze the input-output relationship of single-input single-output (SISO) OTFS system based on the integer sampling DD grid, then extend it to the fractional DD grid. Compared with the integer DD domain, the model in the fractional DD domain is sparser and more accurate. Based on our analysis, two kinds of off-grid models, i.e., doubly fractional model and mixed one- and two-dimensional (1&2D) fractional models are proposed and compared. The mixed 1&2D fractional model has an advantage of low complexity, but encounters a tandem off-grid distortion. To tackle this distortion problem, as well as to balance the accuracy and the computational workload, a novel two dimensional off-grid decomposition and combination scheme is proposed based on the doubly and mixed 1&2D fractional model. Simulation results demonstrate the effectiveness and superiority of the proposed channel estimation schemes, compared with the existing work for OTFS systems under doubly selective channels.

Multiple waveforms should be supported by the new generation wireless communication systems, in order to satisfy the requirements for various mobility scenarios. Orthogonal frequency division multiplexing (OFDM) has been extensively adopted by various wireless communication systems, but it faces great challenges in high mobility scenarios due to large Doppler shift and Doppler spread effects. To overcome this challenge, orthogonal time frequency space (OTFS) has been recently proposed to exploit delay and Doppler diversities, which leads to superior performance to OFDM in high mobility scenarios. This excellent property empowers OTFS to become a promising waveform for high-mobility wireless communication systems (HMWCS). However, in low mobility scenarios, OTFS does not exhibit obvious performance superiority, and suffers from some additional processing complexity. In this paper, considering multi-mobility scenarios, we study the co-existence of OTFS and OFDM waveforms to achieve good tradeoffs between performance and complexity. By regarding OTFS as precoded OFDM, we design multi-waveform downlink transmission systems. We also study two schemes for the co-existence of OTFS and OFDM waveforms. The time division multiplexing (TDM) scheme enables OTFS users with high mobility and OFDM users with low mobility to be multiplexed in the time domain, while the frequency division multiplexing (FDM) scheme multiplexes OTFS users and OFDM users in the frequency domain. The simulation results show that both the FDM and TDM schemes improve the bit error rate (BER) performance of high mobility users. The results also show that the TDM scheme achieves superior BER performance to the FDM scheme.

Interacting with out of reach or occluded VR objects can be cumbersome. Although users can change their position and orientation, such as via teleporting, to help observe and select, doing so frequently may cause loss of spatial orientation or motion sickness. We present vMirror, an interactive widget leveraging reflection of mirrors to observe and select distant or occluded objects. We first designed interaction techniques for placing mirrors and interacting with objects through mirrors. We then conducted a formative study to explore a semi-automated mirror placement method with manual adjustments. Next, we conducted a target-selection experiment to measure the effect of the mirror’s orientation on users’ performance. Results showed that vMirror can be as efficient as direct target selection for most mirror orientations. We further compared vMirror with teleport technique in a virtual treasure hunt game and measured participants’ task performance and subjective experiences. Finally, we discuss vMirorr user experience and present future directions.

In this paper, we consider a downlink non-orthogonal multiple access (NOMA) enabled heterogeneous small cells network (HSCN), where the macro base station simultaneously communicates with multiple small cell base stations (SBSs) through wireless backhaul. In each small cell, users are grouped by NOMA bases and then served by their respective SBS. The proposed framework considers the realistic imperfect channel state information and quality of service requirements of users. The goal is to investigate an energy-efficient joint power, and bandwidth allocation scheme, which aims to maximize the energy efficiency (EE) of the small cells in downlink NOMA-HSCN constrained by the maximum transmit power and the minimum required data rate simultaneously. The optimization problem is non-convex due to the fractional objective function and non-convex constraint and thus challenging to obtain an exact solution efficiently. To this end, the joint optimization is first decomposed into two subproblems. Then, an iterative algorithm to solve the power optimization subproblem is proposed with guaranteed convergence. Furthermore, we derive a closed-form solution for the bandwidth allocation subproblem. Simulation results reveal that the effectiveness of the proposed schemes in terms of EE compared to the existing NOMA and the orthogonal multiple access schemes.

We study a mobility prediction based proactive wireless caching scheme for two-tier cellular networks consisting of a base station (BS) tier and a device-to-device (D2D) tier. Two scenarios are considered: popular contents cached only at BSs, and popular contents cached at both BSs and MTs. We model user mobility as a Markov renewal process to predict user moving paths and residence time. Then we analyse the hit-rate performance to evaluate the presented schemes. By formulating content placement to maximize the hit-rate as optimization problems, we provide the optimal solution for the first scenario and develop a greedy mobility prediction based proactive wireless caching (MPPC) scheme for the second. Through analysis we show that the hit-rate achieved by MPPC is at least exp(1)-1/exp(1) of the optimal hit-rate. The numeric results show that the MPPC can dramatically improve the hit-rate performance, compared with random caching and most popular caching (MPC) schemes. We show that the hit-rate achieved by MPPC outperformances MPC by 26% at most when MTs are not able to cache. Besides we present the impact of the moving speed on the hit-rate performance of MPPC for MTs.

Ultra-dense networks (UDNs) are the promising technology for the fifth-generation wireless networks and beyond to significantly boost network capacity and improve network coverage by exploiting spatial spectrum reuse through the deployment of massive base stations (BSs). In this paper, the fundamental tradeoffs of non-orthogonal multicast, multicast, and unicast in the UDN are studied, to understand the impact of network densitification on them and provide some insights on UDN deployment. Non-orthogonal multicast with imperfect channel estimation and successive interference cancellation is also investigated. To evaluate the performance, a tractable model for performance analysis is developed by using stochastic geometry, and then the analytical expressions for downlink signal-to-interference-plus-noise ratio coverage probability, spectrum efficiency, area traffic capacity, and energy efficiency are derived. The numerical results together with the Monte Carlo simulations are also provided. The results demonstrate that non-orthogonal multicast can further improve the performance of multicast and achieve higher spectrum efficiency, area traffic capacity, and energy efficiency than unicast from the low-to-high BS density regions, but suffers from inferior performance to unicast in the very high BS density region. The results also show that the non-orthogonal multicast and multicast exhibit different performance trends from unicast.

Wireless caching enables popular files to be stored at the base stations (BSs) in advance, which has been considered as an efficient way to reduce the service delay and alleviate heavy burdens on the backhaul links. In this paper, we study the BS clustering scheme for the cooperative wireless caching networks (CWCNs) with heterogeneous file preference among users and BSs, and propose the minimum cost (MC) based clustering scheme. We first introduce the weight-based cost function, which characterizes the trade-off between service delay and transmission cost, and then formulate the clustering as the optimization problem. Furthermore, two clustering algorithms are developed to solve the optimization problem. The results show that the proposed scheme can achieve lower cost compared with random clustering scheme.

Power domain non-orthogonal multiple access (NOMA), which multiplexes multiple users in power domain to achieve high spectrum efficiency, is a promising technology for the fifth-generation (5G) wireless networks and beyond. In this work, we study time and power allocation for two-hop NOMA relaying with decode-and-forward (DF) protocol, to satisfy two NOMA users' different quality of service (QoS) requirements. The performance metrics of outage probability and ergodic capacity are used to evaluate two NOMA users, respectively. The closedform expression for outage probability is also derived. Furthermore, to obtain optimal time allocation factor to achieve the minimum outage probability, we formulate it as one optimization problem and develop a golden section search (GSS)-based algorithm to solve it. Also, the tradeoff between time and power allocation is further analysed to improve the system performance. Numerical and simulation results show that the proposed time and power allocation DF-NOMA behaves better than the traditional equal-time slot cooperative NOMA.

Multimedia broadcast/multicast service (MBMS) transmission, which distributes the media content to multiple users on the same radio resources by using point-to-multipoint communications, is a highly spectrum efficient mechanism for multimedia communications. In this paper, we study the application of power domain non-orthogonal transmission to MBMS enhancements in a K-tier heterogeneous network, in order to satisfy the ever-increasing demands for emerging applications and performance requirements. Then, we present non-orthogonal multi-rate MBMS transmission (NOMRMT) and non-orthogonal multi-service MBMS transmission schemes and investigate their performance by using stochastic geometry. A tractable mode is developed to analyze the performance of asynchronous and synchronous non-orthogonal MBMS transmission. Based on this model, analytical expressions for the signal-to-interference-plus-noise ratio coverage probability, average number of served users, and sum rate are derived. The results demonstrate that non-orthogonal MBMS transmission can achieve better performance than the orthogonal one, while synchronous non-orthogonal MBMS transmission is superior to the asynchronous one.

Millimeter wave (mmWave) wireless networks can operate in single-cell point-to-multipoint mode to provide local multicast services efficiently. In this paper, the performance of multicast mmWave wireless networks is studied, through stochastic geometry. Then, the use of power domain non-orthogonal multiple access (NOMA) for enhancing mmWave multicasting is also investigated. Furthermore, we study multicasting in two-tier mmWave heterogeneous networks, and propose a novel cooperative NOMA multicast scheme. Analytical expressions for the signal-to-interference-plus-noise ratio coverage probability, the average number of served users, and the sum multicast rate are derived, in order to assess the performance of these schemes. Finally, we discuss the maximum sum multicast rates, by formulating them as optimization problems, and also develop efficient golden section search algorithms to solve them. The offered solutions reveal the impact of data transmission rate and power allocation on the sum multicast rate. Both analytical and numerical results demonstrate that NOMA can significantly improve the mmWave multicasting, while the proposed cooperative NOMA mmWave multicast scheme can further improve the NOMA mmWave multicasting.

In high speed rail wireless networks, handover between evolved Node Bs(eNBs) occurs frequently, and the probability of handover failure is dramatically increased, thus seriously degrading the users' experience. To tackle this issue, this paper proposes a network-assisted mobile relay (MR)- controlled fast handover scheme. Under this scheme, the serving eNB provides, in advance, the network assistance information about the available resources of the target eNB, to MR. The MR then makes the handover decision based on continual measurements. Different from the conventional handover scheme, in the proposed scheme, the handover decision is made by the MR instead of eNB. Simulation results show that this scheme can effectively improve handover performance, in terms of handover failure and communication interruption probabilities.

We propose a novel two-timeslot two-way full-duplex (FD) relaying scheme, in which the access link and the backhaul link are divided in the time domain, and we study the average end-to-end rate and the outage performance. According to the user equipment capability and services, we investigate two scenarios: three-node I- and four-node Y-relaying channels. Among various relaying protocols, the well-known amplify-and-forward and decode-and-forward are considered. Closed-form expressions for the average end-to-end rate and the outage probability, under the effect of residual self-interference and inter-user interference, are presented. The results show that the proposed two-timeslot two-way FD relaying scheme can achieve higher rate and better outage performance than the half-duplex one, when residual self-interference is below a certain level. Therefore, this relaying scheme presents a reasonable tradeoff between performance and complexity, and so, it could be efficiently used in the fifth-generation wireless networks.

In this paper, we systematically study the average rate and outage probability tradeoffs of full-duplex two-way and one-way relaying under residual self-interference. Among various relaying protocols, two common of them are considered: amplify-and-forward (AF) and decode-and-forward (DF). Furthermore, we consider the application of physical-layer network coding (PNC) and analog network coding (ANC) to full-duplex two-way relaying. Novel closed-form expressions for the average rate and outage probability, are presented. The results show that full-duplex two-way relaying can achieve higher rate than one-way relaying in the medium to high signal-to-noise ratio (SNR) region, at the cost of a certain loss in the outage performance. Moreover, DF protocol can achieve better outage performance than the AF one, but it suffers from a certain loss in the rate in the high SNR region. It is also shown that PNC can further improve the rate and outage performance. In addition, the results clearly reveal the effects of time multiplexing, forward protocol, and network coding on relaying systems, which would shed light on designing practical full-duplex relaying schemes.