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Holographic Multiple Input Multiple Output (HMIMO), which integrates massive antenna elements into a compact space to achieve a spatially continuous aperture, plays an important role in future wireless networks. With numerous antenna elements, it is hard to implement the HMIMO via phased arrays due to unacceptable power consumption. To address this issue, reconfigurable refractive surface (RRS) is an energy efficient enabler of HMIMO since the surface is free of expensive phase shifters. Unlike traditional metasurfaces working as passive relays, the RRS is used as transmit antennas, where the far-field approximation does not hold anymore, urging a new performance analysis framework. In this letter, we first derive the data rate of an RRS-based single-user downlink system, and then compare its power consumption with the phased array. Simulation results verify our analysis and show that the RRS is an energy-efficient way to HMIMO., 5 pages, 4 figures

For many applications envisioned for the Internet of Things (IoT), it is expected that the sensors will have very low costs and zero power, which can be satisfied by meta-material sensor based IoT, i.e., meta-IoT. As their constituent meta-materials can reflect wireless signals with environment-sensitive reflection coefficients, meta-IoT sensors can achieve simultaneous sensing and transmission without any active modulation. However, to maximize the sensing accuracy, the structures of meta-IoT sensors need to be optimized considering their joint influence on sensing and transmission, which is challenging due to the high computational complexity in evaluating the influence, especially given a large number of sensors. In this paper, we propose a joint sensing and transmission design method for meta-IoT systems with a large number of meta-IoT sensors, which can efficiently optimize the sensing accuracy of the system. Specifically, a computationally efficient received signal model is established to evaluate the joint influence of meta-material structure on sensing and transmission. Then, a sensing algorithm based on deep unsupervised learning is designed to obtain accurate sensing results in a robust manner. Experiments with a prototype verify that the system has a higher sensitivity and a longer transmission range compared to existing designs, and can sense environmental anomalies correctly within 2 meters., Comment: 40 pages, 13 figures

Integrated sensing and communication (ISAC) is an essential technology in the upcoming 6G network, and its performance can be effectively enhanced by the reconfigurable intelligent surface (RIS). Among the various RIS-enabled ISAC techniques, RIS-enabled sensing-assisted communication has attracted growing attention because it can effectively improve communication performance by focusing the energy on the sensed locations of the users and is easy to be integrated into existing communication systems. However, existing RIS-enabled sensing-assisted communication systems rely on antenna arrays for the acquisition of angular information to localize the users, making the system more complex and expensive. To handle this problem, in this paper, we propose an RIS-enabled multi-user sensing-assisted communication framework where a single antenna access point (AP) first senses the locations of the single-antenna users with the help of the RIS, and then the energy of the communication signal is focused on the sensed locations of the users to provide them with a higher sum-rate by adjusting the phase shifts of the RIS. However, the selection of the RIS phase shifts heavily influences the resource block and power allocation at the AP, which makes the joint design of the RIS phase shifts and the resource allocation very challenging. In order to address this challenge, we formulate an RIS-enabled multi-user communication optimization problem and design a two-stage optimization algorithm based on the genetic and Lagrangian duality methods to jointly optimize the RIS phase shifts and the resource allocation. Simulation and experimental results show that compared with the scheme without RIS, the proposed RIS-enabled multi-user communication system can achieve a higher sum-rate and lower localization error.

Given the rapid development of advanced electromagnetic manipulation technologies, researchers have turned their attentions to the investigation of smart surfaces for enhancing the radio coverage. Simultaneously transmitting and reflecting intelligent omni-surfaces (STAR-IOSs) constitute one of the most promising categories. Although previous research contributions have demonstrated the benefits of STAR-IOSs in terms of its wireless communication performance gains, several important issues remain unresolved, including both their practical hardware implementations and their accurate physical models. In this paper, we address these issues by discussing four practical hardware implementations of STAR-IOSs, as well as three hardware modeling techniques and five channel modeling methods. We clarify the taxonomy of the smart surface technologies in support of further investigating the family of STAR-IOSs.

A reconfigurable intelligent surface (RIS)-assisted multi-user downlink communication system over fading channels is investigated, where both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes are employed. In particular, the time overhead for configuring the RIS reflective elements at the beginning of each fading channel is considered. The optimization goal is maximizing the effective throughput of the entire transmission period by jointly optimizing the phase shift of the RIS and the power allocation of the AP for each channel block. In an effort to solve the formulated problem and fill the research vacancy of the performance comparison between different machine learning tools in wireless networks, a deep learning (DL) approach and a reinforcement learning (RL) approach are proposed and their representative superiority and inferiority are investigated. The DL approach can locate the optimal phase shifts with the deep neural network fitting as well as the corresponding power allocation for each user. From the perspective of long-term reward, the phase shift control with configuration overhead can be regarded as a Markov decision process and the RL algorithm is proficient in solving such problems with the assistance of the Bellman equation. The numerical results indicate that: 1) From the perspective of the wireless network, NOMA can achieve a throughput gain of about 42% compared with OMA; 2) The well-trained RL and DL agents are able to achieve the same performance in Rician channel, while RL is superior in the Rayleigh channel; 3) The DL approach has lower complexity and faster convergence, while the RL approach has preferable strategy flexibility.

Intelligent reflecting surfaces (IRSs), which are capable of adjusting the propagation conditions by controlling the phase shifts of the reflected waves that impinge on the surface, have been widely analyzed for enhancing the performance of wireless systems. However, the reflective properties of widely studied IRSs restrict the service coverage to only one side of the surface. In this paper, to extend the wireless coverage of communication systems, we introduce the concept of intelligent omni-surface (IOS)-assisted communication. More precisely, an IOS is an important instance of a reconfigurable intelligent surface (RIS) that can provide service coverage to the mobile users (MUs) in a reflective and a refractive manner. We consider a downlink IOS-assisted communication system, where a multi-antenna small base station (SBS) and an IOS jointly perform beamforming, for improving the received power of multiple MUs on both sides of the IOS, through different reflective/refractive channels. To maximize the sum-rate, we formulate a joint IOS phase shift design and SBS beamforming optimization problem, and propose an iterative algorithm to efficiently solve the resulting non-convex program. Both theoretical analysis and simulation results show that an IOS significantly extends the service coverage of the SBS when compared to an IRS.

Grant-free access, in which channels are accessed without undergoing assignment through a handshake process, is a promising solution to support massive connectivity needed for Internet-of-Things (IoT) networks. In this paper, we consider uplink grant-free massive access for an IoT network with multiple channels. To be specific, the IoT devices generate short packets and have grant-free non-orthogonal access to a channel to transmit the generated packets to a base station (BS). With the aim of keeping the information fresh at the BS, we first derive the age of information (AoI) for grant-free short-packet communications, and then formulate the AoI minimization problem. However, the problem is challenging as the number of users involved is large, and to tackle this problem efficiently, we propose a mean-field evolutionary game-based approach. In this approach, the average behavior of the IoT devices is considered rather than their individual behaviors, and the dynamics of the strategies of the IoT devices are modeled by an evolutionary process. Simulation results verify the effectiveness of the proposed mean-field evolutionary game-based approach.

In this paper, we consider a single-cell multi-user orthogonal frequency division multiple access (OFDMA) network with one unmanned aerial vehicle (UAV), which works as an amplify-and-forward relay to improve the quality-of-service (QoS) of the user equipments (UEs) in the cell edge. Aiming to improve the throughput while guaranteeing the user fairness, we jointly optimize the communication mode, subchannel allocation, power allocation, and UAV trajectory, which is an NP-hard problem. To design the UAV trajectory and resource allocation efficiently, we first decompose the problem into three subproblems, i.e., mode selection and subchannel allocation, trajectory optimization, and power allocation, and then solve these subproblems iteratively. Simulation results show that the proposed algorithm outperforms the random algorithm and the cellular scheme., 33 pages, 6 figures, to be published in IEEE Transactions on Wireless Communications

Recently, the ultra-dense low Earth orbit (LEO) satellite constellation over high-frequency band has served as a potential solution for high-capacity backhaul data services. In this paper, we consider an ultra-dense LEO-based terrestrial-satellite network where terrestrial users can access the network through the LEO-assisted backhaul. We aim to minimize the number of satellites in the constellation while satisfying the backhaul requirement of each user terminal (UT). We first derive the average total backhaul capacity of each UT, based on which a three-dimensional constellation optimization algorithm is proposed to minimize the number of satellites in the constellation. Simulation results verify our theoretical capacity analysis and show that for any given coverage ratio requirement, the corresponding optimized LEO satellite constellation can be obtained by the proposed three-dimensional constellation optimization algorithm. Given the same number of deployed LEO satellites, the average coverage ratio of the proposed LEO satellite constellation is at least 10 percentage points higher than that of Telesat constellation.

In recent years, unmanned aerial vehicles (UAVs) have found numerous sensing applications, which are expected to add billions of dollars to the world economy in the next decade. To further improve the Quality-of-Service (QoS) in such applications, the 3rd Generation Partnership Project (3GPP) has considered the adoption of terrestrial cellular networks to support UAV sensing services, also known as the cellular Internet of UAVs. In this paper, we consider a cellular Internet of UAVs, where the sensory data can be transmitted either to base station (BS) via cellular links, or to mobile devices by underlay UAV-to-Device (U2D) communications. To evaluate the freshness of data, the age of information (AoI) is adopted, in which a lower AoI implies fresher data. Since UAVs' AoIs are determined by their trajectories during sensing and transmission, we investigate the AoI minimization problem for UAVs by designing their trajectories. This problem is a Markov decision problem (MDP) with an infinite state-action space, and thus we utilize multi-agent deep reinforcement learning (DRL) to approximate the state-action space. Then, we propose a multi-UAV trajectory design algorithm to solve this problem. Simulation results show that our algorithm achieves a lower AoI than greedy algorithm and policy gradient algorithm., 30 pages, 7 figures

The 360-degree video streaming system delivers a monocular panoramic video surrounding the user, and the user can change the viewing direction of mobile devices to see different parts of the video through the “viewport”. Due to the limited network bandwidth, playbacks of high-resolution 360-degree videos often suffer from rebuffering, while too much bandwidth is wasted in delivering those out-of-viewport parts that the user never watches. In this article, we present an Ensemble Prediction and Allocation based Streaming System, named as EPASS360 , for delivering high Quality of Experience (QoE) 360-degree videos. The prediction model takes advantages of ensemble learning, providing high accuracy on the prediction of viewports. The allocation model divides a video into tiles, and allocates high resolution to tiles where a user’s viewpoint may appear in the future by solving the QoE-aware optimization problem. Trace-driven emulation on real-world datasets shows that EPASS360 enhances the QoE in various scenarios compared to state-of-the-art streaming approaches. Experiments on the head-mounted device and the hand-held device over real-world Internet confirm the high user experience of EPASS360.

In a free-space optical (FSO) communication system, a conventional transmit laser selection (CTLS) scheme selects the best transmit aperture to send the information bits and offers the best possible error performance. However, its performance is highly dependent on the feedback link, and even a slight error in the feedback information can cause a significant loss to the diversity order and bit error rate (BER). To mitigate this problem, in this research, a novel clustering-based TLS scheme, named two TLS (TTLS), is proposed and analyzed. Note that the word two in TTLS corresponds to the transmission corresponding to the two transmit apertures. TTLS scheme divides the arbitrary number of transmit apertures/lasers into two clusters and then selects the best transmit aperture from each cluster. It is observed by analysis that the TTLS scheme offers a lesser loss in the diversity order as compared to CTLS under imperfect feedback link. Therefore, a significant performance gain can be achieved by TTLS as compared to CTLS under a practical (imperfect/erroneous feedback link) scenario. Further, to retain the full diversity order under the imperfect feedback link, a modified error-tolerant weighting scheme (METWS) is also proposed. It is noticed from the asymptotic analysis that METWS offers full diversity order and outperforms CTLS and repetition coding schemes under both perfect and imperfect feedback links.

To avoid the original private data uploading in cloud-edgecomputing, the federated learning (FL) scheme is recently proposed which enhances the privacy preservation. However, the attacks against the uploaded model updates in FL can still cause private data leakage which demotivates the privacy-sensitive participating edge devices. To address this issue, we aim to design a privacy-preserving incentive mechanism for the federated cloud-edge learning (PFCEL) system such that 1) the privacy-sensitive edge devices are motivated to contribute to the local training and model uploading, 2) a trade-off between the private data leakage and the model accuracy is achieved. We first model the data leakage quantitatively from an adversarial perspective, and then formulate the incentive design problem as a three-layer Stackelberg game, where the interaction between the edge servers and edge devices is further formulated as an optimal contract design problem. Extensive theoretical analysis and numerical evaluations demonstrate the effectiveness of our designed mechanism in terms of privacy preservation and system utility.

Cell-free systems can effectively eliminate the inter-cell interference by enabling multiple base stations (BSs) to cooperatively serve users without cell boundaries at the expense of high costs of hardware and power sources due to the large-scale deployment of BSs. To tackle this issue, the low-cost reconfigurable intelligent surface (RIS) can serve as a promising technique to improve the energy efficiency of cell-free systems. In this article, we consider an RIS aided cell-free MIMO system where multiple RISs are deployed around BSs and users to create favorable propagation conditions via reconfigurable reflections in a low-cost way, thereby enhancing cell-free MIMO communications. To maximize the energy efficiency, a hybrid beamforming (HBF) scheme consisting of the digital beamforming at BSs and the RIS-based analog beamforming is proposed. The energy efficiency maximization problem is formulated and an iterative algorithm is designed to solve this problem. The impact of the transmit power, the number of RIS, and the RIS size on energy efficiency are investigated. Both theoretical analysis and simulation results reveal that the optimal energy efficiency depends on the numbers of RISs and the RIS size. Numerical evaluations also show that the proposed system can achieve a higher energy efficiency than conventional ones.

The future sixth generation (6G) wireless communications look forward to constructing a ubiquitous intelligent information network with high data rates. To fulfill such challenging visions, the reconfigurable holographic surface (RHS) is developed as a promising solution due to its capability of accurate multi-beam steering with low power consumption and hardware cost. Different from the conventional phase-controlled antennas, the RHS can control the radiation amplitude of the reference wave propagating on the metasurface by leveraging the holographic technique. The desired object waves can then be generated without complex phase-shifting circuits, enabling the convenient implementation of the transceiver. Such amplitude-controlled holographic beamforming triggers new challenges since a new beamforming scheme needs to be developed to handle the complex-domain optimization problem subject to the unconventional real-domain amplitude constraints, which makes the superposition of the radiation waves from different antenna elements difficult to tackle. In this letter, we consider an RHS-aided multi-user communication system with a base station equipped with an RHS. We formulate a sum rate maximization problem and design a novel amplitude-controlled algorithm to solve the problem. Simulation results verify the effectiveness of the proposed scheme.

In this article, we consider a downlink ultra-dense LEO-based multi-terminal satellite system where multiple satellites fly in formation to serve a number of ground terminal stations for data transmission. Benefited from the dense satellite constellation, high channel capacity can be achieved via the massive virtual antenna array formed by multiple satellites. We aim at exploring how the satellite distribution and formation size influence the channel capacity in this case. We first derive the upper bound of the channel capacity in the general multi-antenna satellite networks. The single-antenna satellite case is considered where we present the specific forms of the derived capacity bounds to prove that the capacity first increases linearly then increases more and more slowly with the satellite formation size, and there exists an optimal LEO satellite distribution to achieve the maximum capacity of the system. Simulation results verify our theoretical analysis and show that such statements also hold for the general multi-antenna satellite case.

Cell-free system is an efficient solution to eliminate inter-cell interference by serving users without cell boundaries. To further improve the spectrum efficiency, in this letter, we consider a reconfigurable intelligent surface (RIS) aided cell-free MIMO system. Multiple RISs are employed to create favorable propagation conditions via configurable reflection from the BS to users. Benefited from multiple transmission paths the spatial resources can be better utilized. We develop a hybrid beamforming (HBF) scheme consisting of the digital beamforming at the BS and the RIS-based beamforming. A sum rate optimization (SRO) problem is formulated and an iterative algorithm is designed to solve this problem. Simulation results show that the proposed system can achieve a significant performance gain compared to conventional single cell systems without RISs and distributed antenna systems.

Wireless network virtualization is basically the abstraction, isolation, and sharing of wireless resources among different entities. Consequently, virtualization provides great flexibility and higher network efficiency, and enables easier migration to new technologies in wireless networks. Traditionally, a wireless network virtualization controller manages the virtual resources (including radio resources and infrastructure resources) known as slices which are available to the Service Providers (SPs). The SPs then allocate their purchased resources to serve their subscribed mobile users. Such a centralized allocation decouples the Quality-of-Service (QoS) management by the SPs from the virtual resource management by the controller. In this paper, we propose a matching based wireless network virtualization resource allocation mechanism: a distributed three-sided (3D) matching between radio resources, physical infrastructure and mobile users. The Restricted Three-sided Matching with Size and Cyclic preference model (R-TMSC) is implemented to obtain a stable solution. Simulation results show that our proposed spectrum-oriented and user-oriented algorithms outperform the traditional resource allocation schemes. The spectrum-oriented algorithm enhances the user throughput and the system performance, within a lesser run time. Furthermore, for an increasing number of users, the proposed algorithms serve more users than traditional methods.

We consider an uplink multi-user scenario and investigate the use of reconfigurable intelligent surfaces (RIS) to optimize spatial multiplexing performance when a linear receiver is used. We study two different formulations of the problem, namely maximizing the effective rank and maximizing the minimum singular value of the RIS-augmented channel. We employ gradient-based optimization to solve the two problems and compare the solutions in terms of the sum-rate achievable when a linear receiver is used. Our results show that the proposed criteria can be used to optimize the RIS to obtain effective channels with favorable properties and drastically improve performance even when the propagation through the RIS contributes a small fraction of the received power., Comment: 5 pages, 4 figures, accepted for publication in IEEE Wireless Communications Letters

Driven by the increasingly serious air pollution problem, nowadays different systems can be used to achieve the monitoring task of air quality index (AQI) in urban areas. In this article, we design a novel unmanned aerial vehicle-aided (UAV-aided) AQI monitoring system, called AQ 360, which detects the air quality level from the 360-degree aerial panoramic images taken by the onboard camera. Specifically, we first present our own AQI recognition approach based on the physical form of the haze pictures, where the AQI is jointly decided by the images captured along six directions over the target location. Then, we study the UAV placement problem of selecting UAV’s flight altitude and 2-D coordinates during the monitoring process. The objective is to save the system energy consumption while maintaining the accuracy of estimating AQI distribution. For practical considerations, we implement and evaluate the proposed system in real-world scenarios. The results show that our system can provide a lower AQI recognition error compared with existing vision-based monitoring approaches, and energy consumption is also reduced when applying for large-area tasks.

Multi-access edge computing (MEC) has been proposed to reduce latency inherent in traditional cloud computing. One of the services offered in an MEC network (MECN) is computation offloading in which computing nodes, with limited capabilities and performance, can offload computation-intensive tasks to other computing nodes in the network. Recently, mean-field-type game (MFTG) has been applied in engineering applications in which the number of decision makers is finite and where a decision maker can be distinguishable from other decision makers and have a non-negligible effect on the total utility of the network. Since MECNs are implemented through finite number of computing nodes and the computing capability of a computing node can affect the state (i.e., the number of computation tasks) of the network, we propose non-cooperative and cooperative MFTG approaches to formulate computation offloading problems. In these scenarios, the goal of each computing node is to offload a portion of the aggregate computation tasks from the network that minimizes a specific cost. Then, we utilize a direct approach to calculate the optimal solution of these MFTG problems that minimizes the corresponding cost. Finally, we conclude the paper with simulations to show the significance of the approach.

In this letter, we study a buffer-aided Internet of unmanned aerial vehicles (UAVs) in which a UAV performs data sensing, stores the data, and sends it to the base station (BS) in cellular networks. To minimize the overall completion time for all the sensing tasks, we formulate a joint trajectory, sensing location, and sensing time optimization problem. To solve this NP-hard problem efficiently, we propose an iterative trajectory, sensing location and sensing time optimization (ITLTO) algorithm, and discuss the trade-off between sensing time and flying time. Simulation results show that the proposed algorithm can effectively reduce the completion time for the sensing tasks., Accepted by IEEE Communications Letters

Using radio-frequency (RF) sensing techniques for human posture recognition has attracted growing interest due to its advantages of pervasiveness, contact-free observation, and privacy protection. Conventional RF sensing techniques are constrained by their radio environments, which limit the number of transmission channels to carry multi-dimensional information about human postures. Instead of passively adapting to the environment, in this paper, we design an RF sensing system for posture recognition based on reconfigurable intelligent surfaces (RISs). The proposed system can actively customize the environments to provide desirable propagation properties and diverse transmission channels. However, achieving high recognition accuracy requires the optimization of RIS configuration, which is a challenging problem. To tackle this challenge, we formulate the optimization problem, decompose it into two subproblems, and propose algorithms to solve them. Based on the developed algorithms, we implement the system and carry out practical experiments. Both simulation and experimental results verify the effectiveness of the designed algorithms and system. Compared to the random configuration and non-configurable environment cases, the designed system can greatly improve the recognition accuracy.

In this letter, we study the multi-user multiple-input single-output (MU-MISO) transmission assisted by a reconfigurable intelligent surface (RIS). Specifically, for an arbitrary precoder employed at the base station (BS), by exploiting the concept of constructive interference (CI), we aim to design the phase shifts at the RIS such that the direct-link signals and the reflected signals can add constructively at each user side. An optimization problem is formulated to minimize the symbol error rate, where we also allow the on-off switch for the reflecting elements at the RIS. By relaxing the constant-envelope (CE) constraints, we arrive at a convex problem that can readily be solved, and a direct normalization is subsequently applied to satisfy the CE constraints. Moreover, we propose a greedy algorithm to further improve the system performance. Numerical results validate the effectiveness of the proposed scheme and demonstrate its superior performance compared to conventional schemes.

In this paper, we consider the radio access network (RAN) slicing in heterogeneous communication systems. Typically, the infrastructure provider (InP) allocates the radio resources to the mobile virtual network operators (MVNOs) as RAN slices, and then the MVNOs use the allocated resources to provide services for their users. As the RAN being heterogeneous, the users are generally with different quality of service (QoS) requirements. Besides, the MVNOs also have different intrinsic interests in terms of collecting revenue and satisfying users QoS demand and thus require different amount of resources. In this regard, it is challenging for the InP to determine the resource allocation strategy. To deal with this issue, we propose a three-layer Stackelberg game (TLSG) to jointly solve the frequency and power allocation for the InP. We derive the unique equilibrium for the special case where each MVNO uniform users and propose a bi-level differential evolution algorithm for more general cases. Simulation results reveal that both users and the InP can be better off when the RAN is sliced for different types of users, and that the MVNOs focusing more on revenue collecting result in the InP and the users obtaining less payoff.

Recently, the ultra-dense low earth orbit (LEO) satellite constellation over high-frequency band has served as a potential solution for terrestrial data offloading owing to its seamless coverage and high-capacity backhaul. In this paper, we consider an integrated ultra-dense LEO-based satellite-terrestrial network where the terrestrial operator (TO) can offload its subscribed users to the LEO satellite network owned by the satellite operator (SO) for satellite-backhauled network access. However, data offloading consumes extra resources of the SO and degrades the quality-of-service of the SO’s original users. Therefore, we aim to design a pricing mechanism based on the Stackelberg game to motivate both operators for data offloading, and the Stackelberg equilibrium is achieved by jointly optimizing the C-band user association, Ka-band spectrum allocation, and data service pricing. Simulation results show that our proposed pricing mechanism can motivate two operators for offloading efficiently. The influence of available frequency resources, data service prices, and the number of LEO satellites on the system performance are also discussed.