The Internet of Nanothings (IoNT) enables in-body communication, but transmitting signals to external devices remains a key challenge. Neural communication provides a promising interface, yet existing models often oversimplify membrane potentials as binary states, ignoring their subthreshold oscillatory dynamics. To address this, we propose a biologically realistic neural communication model that incorporates the resonate-and-fire (RF) neuron model, capturing the damped oscillations in membrane potential. Accordingly, we design two coding and modulation schemes: enhanced dual-pulse on-off keying (EDP-OOK), which aligns pulse intervals with the neuron’s oscillatory period for optimal excitation or suppression, and tunable dual-pulse on-off keying (TDP-OOK), which flexibly adjusts pulse intensity for energy-efficient suppression. The transmission efficiency is evaluated using the bit error rate (BER). Simulation results show the proposed schemes achieve reliable transmission with lower power consumption compared to conventional methods. This research opens up possibilities for efficiently connecting IoNT to external networks.

Reconfigurable intelligent surface (RIS) has emerged as a highly promising technology for future wireless sensing applications, primarily due to its capability to dynamically manipulate the incoming signals through meticulous configuration of the RIS profile. This paper delves into the optimal design strategy for the RIS profile, aiming at maximizing the localization accuracy of the non-line-of-sight user within typical downlink orthogonal frequency division multiplexing (OFDM) systems. Assuming prior knowledge of the user’s location, we first derive the measurement model and corresponding position error bound (PEB) for the considered OFDM systems. Subsequently, under the total power budget constraint, we establish a closed-form solution for the optimal covariance matrix of the RIS profile by leveraging the subspace structure information of the channel states. Building upon this, the design of the optimal RIS profile is executed using the time-sharing technique. Taking into account the hardware limitations, we further formulate a more practical RIS profile design problem by incorporating the unit-modulus constraint for each RIS element, which, however, is non-convex and thus hard to tackle. To address this issue, we employ the alternating minimization technique to compute a suboptimal solution for the constrained RIS profile design problem. Simulation results demonstrate the remarkable PEB performance achieved by both the proposed optimal and constrained RIS profile design strategies. It is also illustrated that the proposed constrained RIS profile design surpasses other state-of-the-art alternatives, exhibiting a comparable performance to our devised optimal benchmark.

Nowadays, unmanned aerial vehicles (UAVs) are deployed to perceive high-definition visuals of ground targets (GTs) for environment reconstruction of virtual reality (VR) by leveraging their high flexibility. Inspired by the classic scalable video coding method, we develop a novel multi-UAV collaborative layered visual perception and transmission scheme for VR named MUL-VR, wherein GTs are divided into multiple overlapped clusters and multiple UAVs are deployed to collaboratively perceive visuals from these clusters. Specifically, our proposed formulation entails maximizing user’s quality of experience (QoE) by optimizing cluster radii, UAV horizontal coordinates, and bandwidth allocation strategy subject to the constraints on visual quality, transmission delay and available bandwidth. To address this issue, we formulate the investigated MUL-VR scheme into an intractable optimization problem, which, however, is difficult to solve due to the non-convexity of the objective function and constraints, as well as the intricate coupling of the variables. To tackle this challenging problem, we first propose an efficient alternating algorithm, which decomposes the original optimization problem into three subproblems, and then derive the optimal closed-form solution to each subproblem. Consequently, the final solution can be obtained by iteratively optimizing the variables associated with each subproblem, while holding the variables in the other two subproblems fixed, until the convergence condition is satisfied. Simulation results demonstrate that the proposed scheme can effectively improve the user’s QoE and enhance the robustness of the system, yielding superior performance compared to other benchmarks. Specifically, compared to the classic K-Means based scheme, the proposed scheme offers a 25.9% enhancement in terms of QoE when the preference coefficient ε = 0.1 and such performance gain progressively expands as ε increases.

Estimating air-to-ground (A2G) channel for a cellular-connected unmanned aerial vehicle (UAV) requires frequent pilot transmission due to its high mobility. To reduce the pilot overhead, researchers have attempted to predict future channels based on the historical ones by leveraging learning techniques. However, most existing works are limited to sequentially forecasting subsequent channels, suffering from the error accumulation problem that consequently hampers the prediction accuracy. To address this issue, this paper proposes a novel learning-based estimate-then-predict scheme for A2G channel tracking. In this scheme, the UAV transmits limited pilots, and the base station (BS) first performs channel estimation by exploiting the received pilots and then predicts a series of subsequent channels concurrently. Specifically, in the estimation phase, we propose a least-squares feedforward neural network (LS-FNN) to fuse the benefits of LS in high signal-to-noise ratio (SNR) regime and FNN in low SNR regime. In the prediction phase, a multi-time-interval long-short-term-memory (MTI-LSTM) network is proposed for concurrent channel prediction. A distinctive difference from prior works is that layer normalization is employed to greatly increase the prediction accuracy at no cost of additional neurons. Simulation results corroborate the superior performance of our proposed scheme over the state-of-the-art benchmarks.

The Internet of Nan-othings (IoNTs) have been extensively explored as potential communication technologies for in-body applications. The transmission of information from within the body to the external environment has become a popular and pressing issue that needs to be addressed. Neural communication has been proposed as a promising method, utilizing an action potential (AP), transient changes in membrane potential, as a fundamental unit for communication. Current research conceptualizes the membrane potential into two states: an excited state that generates an AP upon stimulation and a resting state absent of stimulation. However, this assumption overlooks the inherent oscillatory characteristics of membrane potential, which exhibit a discernible sensitivity to specific input frequencies. In this paper, we incorporate the oscillations characterized by the Izhikevich model into the channel model. Here, APs are generated when the inter-spike interval closely aligns with, or is a multiple of, the oscillatory period. Following this, we introduce an adaptive coding and modulation strategy. To represent “1”, a pair of pulses, separated by one period, are used to stimulate an AP. In contrast, to represent”0”, two consecutive pulses, distanced by half a period, are employed to inhibit the membrane potential. The transmission efficiency is evaluated by bit error rate (BER) and mutual information (MI). Numerical simulation results demonstrate that the proposed neural communication system is biologically plausible and exhibits higher resistance to interference. This research opens up possibilities for connecting IoNTs to external networks.

Neuro-spike communication is a promising communication technique in future nano-scale applications. For example, it can be used as the communication means between biocompatible nanomachines, which sheds light on new solutions to achieve new medical diagnosis and treatment for neurological diseases. The information transmission in the neuro-spike communication channel is regulated by the ability of neurons to change synaptic strength over time, i.e., synaptic plasticity. In this paper, we consider one kind of typical synaptic plasticity, i.e., spike-time dependent plasticity (STDP), where the synaptic weights can be enhanced or decreased depending on temporal correlations between presynaptic spike arrival and postsynaptic firing. The STDP is integrated in the modeling of neuro-spike communication channel, and the mutual information of the system is derived theoretically. The mutual information is also evaluated through simulations under the synaptic noise and different vesicle release probabilities. Through simulations, it is observed that STDP can increase the single-input single-output (SISO) communication rates between synapses.

Integrated sensing and communication (ISAC) will become an important feature in the future wireless communication networks. To achieve the high-rate communication and high-precision sensing, joint design of beamforming and power allocation is essential in the ISAC-assisted vehicular networks. This paper studies the downlink ISAC-assisted beamforming design and power allocation in the vehicle-to-infrastructure (V2I) communication networks to maximize the achievable sum-rate while guaranteeing the targeted sensing accuracy. The Cramer-Rao lower bounds (CRLBs) are introduced to characterize the estimation performance of the angle and distance between the target vehicle and the roadside unit (RSU). Due to the non-convex CRLBs sensing constraints, we propose an intelligent scheme based on deep reinforcement learning (DRL) to design the beamforming and allocate the power. In this scheme, the reward function is related to the communication sum-rate and CRLBs sensing constraints. Simulation results show that the proposed scheme significantly improves both communication and sensing performance compared to the benchmark schemes.

Orthogonal time frequency space (OTFS) modulation has been considered as one of the most promising candidates to support reliable data transmission especially in high-mobility networks, wherein the performance of communications strongly relies on timely and accurate tracking of the relevant user state parameters. In this context, the problem of integrated sensing and communication (ISAC) assisted user state refinement is addressed in the framework of OTFS systems. In particular, by exploiting the initial yet coarse angle estimate provided by the typical codebook-based user state sensing algorithm, we judiciously design a hybrid digital-analog architecture to output the nested array structured low dimensional observations. In this way, the corresponding nested array based technique is employed to perform angle refinement by fully utilizing the degrees of freedom provided by the measurements. Next, based on the refined angle estimate, we develop a two-stage joint delay and Doppler shifts estimation scheme to update the corresponding coarse estimates. Numerical results validate the effectiveness of the proposed algorithm in various scenarios, showing that our well designed user state refinement scheme is able to improve the performance of the considered ISAC-assisted OTFS systems in term of both radar and communication metrics.

Nowadays, unmanned aerial vehicles (UAVs), empowered with the capability of high-definition image transmission, are used to capture the rapidly changing physical environment by leveraging its high flexibility to reconstruct an immersive realistic virtual environment for metaverse users. In this paper, we consider a novel UAV-assisted image acquisition system where a UAV is dispatched to take off from an initial location to capture real-time images of multiple ground targets and then transfer the captured images back to the ground user for virtual environment reconstruction. We aim to minimize the time for the UAV to complete the image acquisition task by optimizing the three-dimensional UAV trajectory under the constraints of image quality, information causality and energy consumption. To this end, we first formulate the investigated scenario into a mixed integer optimization problem, which, however, is difficult to solve due to the infinite time-varying variables closely coupled with each other. Then, a three-stage progressive algorithm is proposed to obtain an efficient solution to the formulated mixed integer optimization problem, where the constraints of image quality, information causality and energy consumption can be sequentially satisfied. Finally, comprehensive performance evaluation is conducted to verify the effectiveness of the proposed three-stage progressive trajectory design algorithm, and the results show that the proposed algorithm significantly outperforms the benchmark schemes.

Developing green radio networks is desirable to improve energy efficiency under quality of service (QoS) constraints. In this paper, we propose a QoS aware energy efficient resource allocation scheme in multiuser high speed downlink packet access (HSDPA) systems. First, we derive a new metric, namely effective energy efficiency (EEE), to represent the delivered service bits at the media access control (MAC) layer per joule subject to given QoS constraints. Then, by using this new metric, a EEE optimization problem in the mixed traffic scenario is formulated, where we can exploit the multi-traffic diversity. With the help of primal decomposition technique, we solve the formulated problem and propose a cross-layer resource allocation scheme, in which determines the EEE optimal transmit power level for each user and schedules the set of EEE near-optimal users. Numerical results are presented to quantify the superiority of our proposed scheme over the conventional resource allocation schemes.

In this paper, we investigate the optimal energy efficient coordinated beamforming in multi-cell multiple-input single-output (MISO) systems with K multiple-antenna base stations (BS) and K single-antenna mobile stations (MS), where each BS sends information to its own intended MS with cooperatively designed transmit beamforming. We assume single user detection at the MS by treating the interference as noise. By taking into account a realistic power model at the BS, we characterize the Pareto boundary of the achievable energy efficiency (EE) region of the K links, where the EE of each link is defined as the achievable data rate at the MS divided by the total power consumption at the BS. Since the EE of each link is non-cancave (which is a non-concave function over an affine function), characterizing this boundary is difficult. To meet this challenge, we relate this multi-cell MISO system to cognitive radio (CR) MISO channels by applying the concept of interference temperature (IT), and accordingly transform the EE boundary characterization problem into a set of fractional concave programming problems. Then, we apply the fractional concave programming technique to solve these fractional concave problems, and correspondingly give a parametrization for the EE boundary in terms of IT levels. Based on this characterization, we further present a decentralized algorithm to implement the multi-cell coordinated beamforming, which is shown by simulations to achieve the EE Pareto boundary.