Communication Pairs Research Articles

CollectNET: a web server for integrated inference of cell-cell communication network.

Cell-cell communication (CCC) through ligand-receptor (L-R) pairs forms the cornerstone for complex functionalities in multicellular organisms. Deciphering such intercellular signaling can contribute to unraveling disease mechanisms and enable targeted therapy. Nonetheless, notable biases and inconsistencies are evident among the inferential outcomes generated by current methods for inferring CCC network. To fill this gap, we developed collectNET (http://health.tsinghua.edu.cn/collectnet) as a comprehensive web platform for analyzing CCC network, with efficient calculation, hierarchical browsing, comprehensive statistics, advanced searching, and intuitive visualization. collectNET provides a reliable online inference service with prior knowledge of three public L-R databases and systematic integration of three mainstream inference methods. Additionally, collectNET has assembled a human CCC atlas, including 126 785 significant communication pairs based on 343 023 cells. We anticipate that collectNET will benefit researchers in gaining a more holistic understanding of cell development and differentiation mechanisms. Database URL: http://health.tsinghua.edu.cn/collectnet.

Conflictogenic Linguistic Markers in Communication with a Patient

The study of verbal means of conflict communication is necessary for understanding the mechanisms of preventing misunderstandings in the communication pairs "patient - doctor" and "patient - medical institution". The selection and analysis of lexical and grammatical markers that actualize the semantics of the conflict, and the definition of typical linguistic representations of the conflict in patients' appeals can become the basis for educational communication trainings.

On energy conservation for fixed-wing UAV assisted practical full-duplex relaying with service requirement and bank angle limit

This paper studies a full-duplex (FD) amplify-and-forward relaying, where a fixed-wing unmanned aerial vehicle (UAV) is dispatched as a mobile relay to serve a source-destination communication pair so as to satisfy their service requirement. Here, the UAV relay flies in a circular path and hence it must continuously change its heading of flight which leads to a bank angle of the aircraft. In order to ensure flight safety, therefore, a bank angle limit is imposed on the UAV relay. By considering a practical FD relaying, namely, there exists both residual loop interference (RLI) and processing time-delay at the relay side, novel signal to interference plus noise ratio (SINR) received at the destination is analyzed, giving a closed-form expression in addition to a concise lower-bound. Armed with the SINR analysis, the size of data received by the destination during a period of flight time of the UAV is calculated, leading to a closed-form lower-bound of the size of data. Taking bank angle limit into consideration and using the calculated lower-bound of the size of data, an optimization problem with the purpose of energy conservation is solved, leading to a novel method for joint adjustment of the UAV's flight parameters. Computer simulation experiments are conducted, and the results demonstrated that the proposed optimization method performs better in terms of energy conservation compared to the benchmark techniques regardless of the value of RLI and/or the service requirement of the source-destination communication pair.

Efficient resource allocation algorithms for multihop D2D approach in 5G network

SummaryNow the 5G network systems are expected to enhance their throughput. Device‐to‐device (D2D) communication is one such technique that offers an effective solution. The existing cellular user (CU) communication must be ensured that this solution had minimized its interference with it. Hence, designing methods for resource allocation for D2D transmission is also important. Such allocation will maximize network throughput without causing much interference with the present communication. Apart from these, it ensures delay minimization and achieves fairness among user data rates. But when distance is increased, an extension from direct D2D communication (single‐hop) to multi‐hop is necessary. At the same time, its objectives shall be retained. In the proposed work, initially, a comparative study is done on different algorithms for multi‐hop (two‐hop) D2D communication pairs. While studying different algorithms such as random allocation (RA), graph allocation (GA), particle swamp optimization (PSO), and technique for order preference by similarity to the ideal solution (TOPSIS), they are also analyzed, looking for the possibility of a particular combination of them to achieve specific objectives. The realized method has improved performance even in the situation of an increase in distance. The arrived two methods also support a better data rate. The first method allocates resources based on orthogonal sharing. The second method is for allocating resources to them that have cellular orientation. The simulated results exhibit the improved capability of the proposed methods. The method based on the graph to allocate the mentioned resources functions better. Finally, for throughput and data rates, PSO and TOPSIS techniques are implemented.

RIS-aided mmWave beam-forming for two-way communications of multiple pairs

Millimeter-wave (mmWave) communications is a key enabler towards realizing enhanced Mobile Broadband (eMBB) as a key promise of 5G and beyond, due to the abundance of bandwidth available at mmWave bands. An mmWave coverage map consists of blind spots due to shadowing and fading especially in dense urban environments. Beam-forming employing massive MIMO is primarily used to address high attenuation in the mmWave channel. Due to their ability in manipulating the impinging electromagnetic waves in an energy-efficient fashion, Reconfigurable Intelligent Surfaces (RISs) are considered a great match to complement the massive MIMO systems in realizing the beam-forming task and therefore effectively filling in the mmWave coverage gap. In this paper, we propose a novel RIS architecture, namely RIS-UPA where the RIS elements are arranged in a Uniform Planar Array (UPA). We show how RIS-UPA can be used in an RIS-aided MIMO system to fill the coverage gap in mmWave by forming beams of a custom footprint, with optimized main lobe gain, minimum leakage, and fairly sharp edges. Further, we propose a configuration for RIS-UPA that can support multiple two-way communication pairs, simultaneously. We theoretically obtain closed-form low-complexity solutions for our design and validate our theoretical findings by extensive numerical experiments.

Outage Probability and Throughput of Mobile Multiantenna UAV-Assisted FD-NOMA Relay System With Imperfect CSI

In this article, we analyze an unmanned aerial vehicle (UAV) equipped with multiple antennas and operating in full-duplex (FD) mode to assist the communication of two source–destination pairs in a power-domain nonorthogonal multiple access (NOMA) system. The UAV acts as a relay to forward data from sources to destinations. For practical purposes, the velocity of the UAV in a horizontal plane is taken into consideration. The impacts of imperfect successive interference cancelation, imperfect channel state information, residual self-interference, and the power allocation coefficient for the users to the system performance are also investigated. We evaluate the performance of the considered mobile multiantenna UAV-assisted FD-NOMA relay system by deriving the outage probability and the throughput’s closed-form expressions for each communication pair in different communication urban environments. Monte Carlo simulation results verify the accuracy of these derived expressions.Numerical results show that the best outage performance can be achieved when the location of the UAV and transmission power of the source and the UAV are properly chosen. Furthermore, increasing the number of antennas at the UAV helps to improve the system performance remarkably.

A Proactive Eavesdropping Game in MIMO Systems Based on Multiagent Deep Reinforcement Learning

This paper considers an adversarial scenario between a legitimate eavesdropper and a suspicious communication pair. All three nodes are equipped with multiple antennas. The eavesdropper, which operates in a full-duplex model, aims to wiretap the dubious communication pair via proactive jamming. On the other hand, the suspicious transmitter, which can send artificial noise (AN) to disturb the wiretap channel, aims to guarantee secrecy. More specifically, the eavesdropper adjusts jamming power to enhance the wiretap rate, while the suspicious transmitter jointly adapts the transmit power and noise power against the eavesdropping. Considering the partial observation and complicated interactions between the eavesdropper and the suspicious pair in unknown system dynamics, we model the problem as an imperfect-information stochastic game. To approach the Nash equilibrium solution of the eavesdropping game, we develop a multi-agent reinforcement learning (MARL) algorithm, termed neural fictitious self-play with soft actor-critic (NFSP-SAC), by combining the fictitious self-play (FSP) with a deep reinforcement learning algorithm, SAC. The introduction of SAC enables FSP to handle the problems with continuous and high dimension observation and action space. The simulation results demonstrate that the power allocation policies learned by our method empirically converge to a Nash equilibrium, while the compared reinforcement learning algorithms suffer from severe fluctuations during the learning process.

Joint alignment and steering to manage interference

In wireless communication networks, mobile users in overlapping areas may experience severe interference, therefore, designing effective Interference Management (IM) methods is crucial to improving network performance. However, when managing multiple disturbances from the same source, it may not be feasible to use existing IM methods such as Interference Alignment (IA) and Interference Steering (IS) exclusively. It is because with IA, the aligned interference becomes indistinguishable at its desired Receiver (Rx) under the cost constraint of Degrees-of-Freedom (DoF), while with IS, more transmit power will be consumed in the direct and repeated application of IS to each interference. To remedy these deficiencies, Interference Alignment Steering (IAS) is proposed by incorporating IA and IS and exploiting their advantages in IM. With IAS, the interfering Transmitter (Tx) first aligns one interference incurred by the transmission of one data stream to a one-dimensional subspace orthogonal to the desired transmission at the interfered Rx, and then the remaining interferences are treated as a whole and steered to the same subspace as the aligned interference. Moreover, two improved versions of IAS, i.e., IAS with Full Adjustment at the Interfering Tx (IAS-FAIT) and Interference Steering and Alignment (ISA), are presented. The former considers the influence of IA on the interfering user-pair's performance. The orthogonality between the desired signals at the interfered Rx can be maintained by adjusting the spatial characteristics of all interferences and the aligned interference components, thus ensuring the Spectral Efficiency (SE) of the interfering communication pairs. Under ISA, the power cost for IS at the interfered Tx is minimized, hence improving SE performance of the interfered communication-pairs. Since the proposed methods are realized at the interfering and interfered Txs cooperatively, the expenses of IM are shared by both communication-pairs. Our in-depth simulation results show that joint use of IA and IS can effectively manage multiple disturbances from the same source and improve the system's SE.

Improving Quality of Service in 5G Resilient Communication with the Cellular Structure of Smartphones

Recent studies in information computation technology (ICT) are focusing on Next-generation networks, SDN (Software-defined networking), 5G, and 6G. Optimal working mode for device-to-device (D2D) communication is aimed at improving the quality of service with the frequency spectrum structure is of research areas in 5G. D2D communication working modes are selected to meet both the predefined system conditions and provide maximum throughput for the network. Due to the complexity of the direct solutions, we formulated the problem as an optimization problem and found the optimal working modes under different parameters of the system through extensive simulations. After determining the links’ optimal modes, we calculated the network throughput; because of selecting the best working modes, we obtained the highest throughput. A major finding from this research is that D2D communication pairs are more inclined to use full-duplex (FD) mode in short distances to meet system requirements, and so most communications take place in FD mode at these distances. According to these results, using FD communication at short distances offers better conditions and Quality of service (QoS) than QoS-D2D method.

Adaptive Discrete Motion Control for Mobile Relay Networks

We consider the problem of joint beamforming and discrete motion control for mobile relaying networks in dynamic channel environments. We assume a single source-destination communication pair. We adopt a general time slotted approach where, during each slot, every relay implements optimal beamforming and estimates its optimal position for the subsequent slot. We assume that the relays move in a 2D compact square region that has been discretized into a fine grid. The goal is to derive discrete motion policies for the relays, in an adaptive fashion, so that they accommodate the dynamic changes of the channel and, therefore, maximize the Signal-to-Interference + Noise Ratio (SINR) at the destination. We present two different approaches for constructing the motion policies. The first approach assumes that the channel evolves as a Gaussian process and exhibits correlation with respect to both time and space. A stochastic programming method is proposed for estimating the relay positions (and the beamforming weights) based on causal information. The stochastic program is equivalent to a set of simple subproblems and the exact evaluation of the objective of each subproblem is impossible. To tackle this we propose a surrogate of the original subproblem that pertains to the Sample Average Approximation method. We denote this approach as model-based because it adopts the assumption that the underlying correlation structure of the channels is completely known. The second method is denoted as model-free, because it adopts no assumption for the channel statistics. For the scope of this approach, we set the problem of discrete relay motion control in a dynamic programming framework. Finally we employ deep Q learning to derive the motion policies. We provide implementation details that are crucial for achieving good performance in terms of the collective SINR at the destination.

GBLinks: GNN-Based Beam Selection and Link Activation for Ultra-Dense D2D mmWave Networks

In this paper, we consider the problem of joint beam selection and link activation across a set of communication pairs to effectively control the interference between communication pairs via inactivating part communication pairs in ultra-dense device-to-device (D2D) mmWave communication networks. The resulting optimization problem is formulated as an integer programming problem that is nonconvex and NP-hard. Consequently, the global optimal solution, even the local optimal solution, cannot be generally obtained. To overcome this challenge, this paper resorts to design a deep learning architecture based on graph neural network to finish the joint beam selection and link activation, with taking the network topology information into account. Meanwhile, we present an unsupervised Lagrangian dual learning framework to train the parameters of the GBLinks model. Numerical results show that the proposed GBLinks model can converge to a stable point with the number of iterations increases, in terms of the weighted sum rate. Furthermore, the GBLinks model can reach near-optimal solutions through comparing with the exhaustive scheme in small-scale ultra-dense D2D mmWave communication networks and outperforms GreedyNoSched and the SCA-based method. It also shows that the GBLinks model can generalize to varying network densities and network coverage regions of ultra-dense D2D mmWave communication networks.

Coverage analyses on directional transmissions ultra-dense networks with imperfect beam alignment

Blockage and imperfect beam alignment are two principal difficulties in high-frequency bands directional transmissions. In this paper, the coverage performance of downlink directional transmissions in ultra-dense networks is analyzed, with the consideration of beam alignment error and link blockage through stochastic geometry. Numerical experiments demonstrate that narrower beam leads to higher coverage probability with perfect beam alignment, but it is not the case with imperfect beam alignment. Therefore, the optimal beamwidth that maximize the coverage probability is characterized and a closed-form approximation of the optimal beamwidth is derived under imperfect beam alignment, accordingly. Furthermore, the optimal beamwidth is a monotonically increasing function of the standard deviation of the beam alignment error, and a monotonically decreasing function of the beamwidth of correspondent communication end, indicating that the beamwidth of the communication pairs ought to be jointly designed.

Protecting Location Privacy in IoT Wireless Sensor Networks through Addresses Anonymity

Location privacy is very important for event-triggered type of Wireless Sensor Networks (WSNs) applications such as tracking and monitoring of wild animals. Most of the security schemes for WSNs are designed to provide protection for content privacy. Contextual privacy such as node identity anonymity has received much less attention. The adversary can fully explore such contextual information to disclose the location of critical components such as source nodes or base station. Most existing schemes provide location privacy at network layer. As no measures are taken to provide node identity anonymity at data link layer, the adversary can launch traffic analysis attacks to jeopardize location privacy. In this paper, a scheme named HASHA is proposed to defend against traffic analysis attacks through hashed one-time addresses. Hashed results of payload are used to create dynamic one-time MAC addresses between the communication pairs. Because of inevitable wireless frame errors, it is impossible for adversaries to track dynamic addresses. Therefore, HASHA can provide strong node identity anonymity, which makes traffic analysis attacks much more difficult and provides better location privacy. Simulations and analysis results show that HASHA can provide better location privacy with limited communication overheads, which is particularly suitable for resource-limited WSNs.

Dissecting the heterogeneity of the microenvironment in primary and recurrent nasopharyngeal carcinomas using single-cell RNA sequencing

ABSTRACT Nasopharyngeal carcinoma (NPC) has a 10–15% recurrence rate, while no long term or durable treatment options are currently available. Single-cell profiling in recurrent NPC (rNPC) may aid in designing effective anticancer therapies, including immunotherapies. For the first time, we profiled the transcriptomes of ∼60,000 cells from four primary NPC and two rNPC cases to provide deeper insights into the dynamic changes in rNPC within radiation fields. Heterogeneity of both immune cells (T, natural killer, B, and myeloid cells) and tumor cells was characterized. Recurrent samples showed increased infiltration of regulatory T cells in a highly immunosuppressive state and CD8+ T cells in a highly cytotoxic and dysfunctional state. Enrichment of M2-polarized macrophages and LAMP3+ dendritic cells conferred enhanced immune suppression to rNPC. Furthermore, malignant cells showed enhanced immune-related features, such as antigen presentation. Elevated regulatory T cell levels were associated with a worse prognosis, with certain receptor-ligand communication pairs identified in rNPC. Even with relatively limited samples, our study provides important clues to complement the exploitation of rNPC immune environment and will help advance targeted immunotherapy of rNPC.

Toward UAV-based communication: improving throughput by optimum trajectory and power allocation

It is predicted that the use of unmanned aerial vehicles (UAVs) in communication systems will be more extensive in future generations of wireless telecommunication networks, due to their facilitating advantages. In this paper, a UAV-based wireless communication system is considered in which a UAV is employed as a relay to connect two ground users. These two disconnected users make a communication pair. Our aim is to maximize the minimum achievable information rate for the communication link between the transmitter and receiver, by cooperatively optimizing UAV trajectory and transmitter and source power allocation. Motivated by the above, we formulate the optimization problem. The solving process is complicated because of the non-convexity of the formulated problem. To overcome this difficulty, we convert the main problem to some sub-problems by fixing some constraints and solving them with iterative algorithms such as successive convex optimization and reaching the solution for the main problem. Simulation results show the capability of the proposed algorithm.

Energy-Efficient UAV-Relaying 5G/6G Spectrum Sharing Networks: Interference Coordination With Power Management and Trajectory Design

As massive ground users access wireless networks with limited available spectrum, coexisting users that share the same spectrum band severely interfere with each other. Moreover, communication pairs without direct communication links may suffer from severe outage issues. In this paper, unmanned aerial vehicle (UAV) relaying-assisted interference coordination is proposed to enhance data transmission reliability by 1) relaying transmission, and 2) alleviating mutual interference among ground users. To efficiently utilize confined UAV energy such that the UAV relaying service life can be significantly prolonged, the full-duplex (FD) technique is exploited. An exact energy efficiency (EE) maximization problem is formulated, which jointly optimizes throughput and UAV energy consumption. Different from the traditional offline method, online power management and trajectory designs are performed under the information causality and rate constraints. Considering that power management and trajectory are coupled, we design a block coordinate descent method to efficiently solve the optimization problem. Numerical results demonstrate that the proposed scheme outperforms existing schemes, and the EE is about 30% and 46% higher than those of the genetic algorithm and half-duplex scheme, respectively. Moreover, impacts of different positions and the maximum power of ground users on the EE have been demonstrated.

A Centralized and Scalable Uplink Power Control Algorithm in Low SINR Scenarios

Power control is becoming increasingly essential for the fifth-generation (5G) and beyond systems. An example use-case, among others, is the unmanned-aerial-vehicle (UAV) communications where the nearly line-of-sight (LoS) radio channels may result in very low signal-to-interference-plus-noise ratios (SINRs). The authors in (Chiang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2007) proposed to efficiently and reliably solve this kind of non-convex problem via a series of geometrical programmings (GPs) using condensation approximation. However, it is only applicable for a small-scale network with several communication pairs and practically infeasible with more (e.g., tens of) nodes to be jointly optimized. We therefore in this paper aim to provide new insights into this problem. By properly introducing auxiliary variables, the problem is transformed to an equivalent form which is simpler and more intuitive for condensation. A novel condensation method with linear complexity is also proposed based on the form. The enhancements make the GP-based power control feasible for both small- and especially large-scale networks that are common in 5G and beyond. The algorithm is verified via simulations. A preliminary case study of uplink UAV communications also shows the potential of the algorithm.

Incorporating Interpersonal Synchronization Features for Automatic Emotion Recognition from Visual and Audio Data during Communication.

During social interaction, humans recognize others’ emotions via individual features and interpersonal features. However, most previous automatic emotion recognition techniques only used individual features—they have not tested the importance of interpersonal features. In the present study, we asked whether interpersonal features, especially time-lagged synchronization features, are beneficial to the performance of automatic emotion recognition techniques. We explored this question in the main experiment (speaker-dependent emotion recognition) and supplementary experiment (speaker-independent emotion recognition) by building an individual framework and interpersonal framework in visual, audio, and cross-modality, respectively. Our main experiment results showed that the interpersonal framework outperformed the individual framework in every modality. Our supplementary experiment showed—even for unknown communication pairs—that the interpersonal framework led to a better performance. Therefore, we concluded that interpersonal features are useful to boost the performance of automatic emotion recognition tasks. We hope to raise attention to interpersonal features in this study.

Delay minimization based uplink resource allocation for device-to-device communications considering mmWave propagation.

This paper addresses the resource allocation problem in multi-sharing uplink for device-to-device (D2D) communication, one aspect of 5G communication networks. The main advantage and motivation in relation to the use of D2D communication is the significant improvement in the spectral efficiency of the system when exploiting the proximity of communication pairs and reusing idle resources of the network, mainly in the uplink mode, where there are more idle available resources. An approach is proposed for allocating resources to D2D and cellular user equipments (CUE) users in the uplink of a 5G based network which considers the estimation of delay bound value. The proposed algorithm considers minimization of total delay for users in the uplink and solves the problem by forming conflict graph and by finding the maximal weight independent set. For the user delay estimation, an approach is proposed that considers the multifractal traffic envelope process and service curve for the uplink. The performance of the algorithm is evaluated through computer simulations in comparison with those of other algorithms in the literature in terms of throughput, delay, fairness and computational complexity in a scenario with channel modeling that describes the propagation of millimeter waves at frequencies above 6 GHz. Simulation results show that the proposed allocation algorithm outperforms other algorithms in the literature, being highly efficient to 5G systems.

Joint uplink and downlink resource allocation for device to device communications

With the ever-increasing number of intelligent devices, extensive research efforts have been spent towards developing efficient resource allocation in underlying device to device communications. Centralised and distributed approaches have been proposed to improve the network performances through uplink and downlink schemes. However, the full-duplex resource allocation remains challenging. Here, a joint uplink–downlink resource block assignment for device to device communications is proposed. The device's geometric distribution via the Poisson point process is formulated and the optimization problem as a mixed strategy non-cooperative game is modelled. The proposed resource allocation scheme allows many device to device communication pairs to share the same cellular resource block but does not restrict their number in advance; it is determined dynamically according to the channel's capacity and interference condition. Inversely, a device to device communication transmitter can simultaneously be allocated many resource blocks according to its bandwidth requirement and the number of its antennas. Furthermore, the optimal amount of data and power transmitted by each device is calculated. The efficiency of this algorithm is evaluated through computer simulations, and the results demonstrate good performances in the spectral and energy efficiency with relatively low complexity and convergence time.