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We study the problem of multi-user dynamic searchable symmetric encryption (DMUSSE) where a data owner stores its encrypted documents on an untrusted remote server and wishes to selectively allow multiple users to access them by issuing keyword search queries. Specifically, we consider the case where some of the users may be corrupted and colluding with the server to extract additional information about the dataset (beyond what they have access to). We provide the first formal security definition for the dynamic setting as well as forward and backward privacy definitions. We then propose SE, the first provably secure DMUSSE scheme and instantiate it in two versions, one based on oblivious data structures and one based on update queues, with different performance trade-offs. Furthermore, we extend SE to support verifiability of results. To achieve this, users need a secure digest initially computed by the data owner and changed after every update. We efficiently accommodate this, without relying on a trusted third party, by adopting a blockchain-based approach for the digests dissemination and deploy our schemes over the permissioned Hyperledger Fabric blockchain. We prototype both versions and experimentally evaluate their practical performance, both as stand-alone systems and running on top of Hyperledger Fabric.

Searchable Symmetric Encryption (SSE) is a key tool for secure data processing. To date, most of the SSEs were studied alone, while an SSE supporting update operations over encrypted data remained a challenging problem due to various statistical attacks and multi-user environments. In this paper, we propose SeUpdate, the first SSE scheme that simultaneously achieves keyword search and controlled update over encrypted data, with flexible read (search) and write (update) access control policies among multiple users. In SeUpdate, users do not need to share secret keys and a single query enables one to efficiently search all his authorized data. We formally define a security model, and prove our scheme have both forward and backward security. We note that the write permission of an SSE is realized for the first time. We further extend the basic scheme with dynamic access policy update and support of a large number of files. We also implement SeUpdate and some related work. The theoretical and experimental analyses demonstrate our scheme and its extension are practical and efficient.

With the rapid development of the industrial Internet of Things (IIoT) and cloud computing, an increasing number of companies outsource their data to cloud servers to save costs. To protect data privacy, sensitive industrial data must be encrypted before being outsourced to cloud servers. A multi-user searchable encryption (MUSE) scheme was introduced to ensure high efficiency of encrypted data retrieval. In an IIoT system with numerous users, the existing MUSE schemes suffer from certain key exposure problems owing to the limited key protection of smart devices and frequent queries by users. In this study, we propose a parallel key-insulated MUSE scheme for IIoT. This scheme utilizes broadcast encryption technology to implement MUSE. In addition, our scheme introduces a key-insulated primitive to improve the tolerance to key exposure. The security of our scheme is proved in the random oracle model. The experimental results show that our scheme achieves high computational efficiency.

Mobile edge computing (MEC) is a promising solution to enable resource-limited mobile devices to offload computation-intensive tasks to nearby edge servers. In this paper, dynamic computation offloading in a non-orthogonal multiple access (NOMA) based multi-user network is investigated by considering stochastic task arrivals and wireless channels. Specifically, a cooperative multi-agent deep reinforcement learning (CMADRL) based framework is proposed to learn decentralized offloading policies to allocate task offloading and local execution powers at each user, which minimizes long-term average network computation cost in terms of power consumption and buffering delay. By leveraging a centralized training and distributed execution strategy, the proposed framework can not only learn efficient decentralized policies, but also relieves users’ computational pressure and works well in coordinating the interference of NOMA-based networks. To reduce the training complexity, it is further improved to train one parameter shared policy network that can be exploited by all users with slightly sacrificed performance. Numerical simulations demonstrate that the proposed CMADRL based framework can learn efficient dynamic offloading policies at each user, which significantly outperforms the conventional independent Q-learning based framework as well as some greedy strategies with lower network computation cost.

With the exponential increase of interconnected communicating devices which make up the Internet of Things (IoT), securing the network transmission and the fifth generation (5G) systems which is the bedrock for IoT concept actualization is becoming more and more challenging. One of the major attacks which poses a great risk to data transmission is the eavesdropper (Eve) attack which occurs in both single input, single output (SISO), multiple input and multiple output (MIMO) systems. Thus, in this study, our focus is to establish a secured connection in a multiple-antenna transmission when the channel state information (CSI) of Eve is unknown to the network users. Our model comprises a secure wireless communication standard where Eve performs either optimal matched filtering (OMF) or a basic matched filtering (BMF) while the transmitting IoT node employs smart jamming strategy in order to compromise the activities of Eve. With respect to this and in attempt to realize maximum privacy, we examined the design of optimal jamming parameters. In the end, the numerical analysis of our investigation indicates that a substantial privacy advantage is achievable while utilizing only full-duplex jamming against using artificial noise from the transmitter only. However, a joint performance of both results shows a higher privacy improvement.

As cloud computing becomes the prevailing aspect of software engineering, paradigms such as Service-Based Systems (SBSs) or Software as a Service (SaaS) are coming into focus. They are based on cloud services responding to numerous client requests. Selecting the actual service instance for request can be an issue, if requirements for multiple Quality of Service (QoS) attributes need to be satisfied for many users simultaneously. The problem becomes more complex if we take into account the compositeness of users' applications, consisting of many tasks, where QoS properties are calculated over the whole composition. The existing approaches for this problem lack either efficiency or generality. In this paper, we propose a fast heuristic method for multi-criteria service selection, designed for multi-user composite workflows with the goal of satisfying all, or as many as possible, of the given QoS requirements. The proposed method reduces the problem to several independent transportation problems, using a global-aware utility cost based on expected compositional QoS, and iterative solution improvements. Apart from being more general than the existing approaches, the proposed method turns out to be more efficient than the alternatives (up to 5x faster), as shown by extensive experiments covering both special and more general cases.

In the era of information technology, secure access to electronic systems is important for protecting private information. Among various authentication techniques, keystroke authentication methods based on users’ touch behavior have received increasing attention. In this paper, we present a technique for obtaining high multi-user authentication accuracy by utilizing users’ touch time and force information, which were obtained from a piezoelectric touch panel. After feeding a support vector machine (SVM) with users’ touch features, a high classification accuracy of 97% is achieved, validating the feasibility of the proposed technique for achieving highly secure multi-user authentication, hence advancing the development of security techniques.

In cognitive radio networks (CRNs), dynamic spectrum access allows (unlicensed) users to identify and access unused channels opportunistically, thus improves spectrum utilization. In this paper, we address the user-channel allocation problem in multi-user multi-channel CRNs without a prior knowledge of channel statistics. The result of channel access is stochastic with unknown distribution, and statistically different for each user. In deciding the channel for access, a user needs to either explore a channel to learn its statistics, or exploit the channel with the highest expected reward based on the information collected so far. Further, a channel should be accessed exclusively by one user at a time to avoid collision. Using multi-armed bandit framework, we develop two rate-optimal algorithms with low computational complexities of $O(N)$ O ( N ) and $O(NK)$ O ( N K ) , respectively, where $N$ N denotes the number of users and $K$ K denotes the number of channels. Further, we extend the results and develop an algorithm that is amenable to implement in a distributed fashion.

Reconfigurable intelligent surface (RIS) is a promising solution to build a programmable wireless environment with reconfigurable passive elements, which can achieve high spectral and energy efficiency. In this paper, we investigate the ergodic capacity of RIS-assisted multi-user multiple-input single-output (MISO) wireless systems in both uplink and downlink scenarios. Unlike most of prior works, where instantaneous channel state information (CSI) is assumed, we consider the realistic scenario with only statistical CSI. For both scenarios, we first present an analytical expressions for the ergodic sum capacity of the system. Based on which, the joint power control (or transmit beamforming) and phase shift design problem maximizing the ergodic sum capacity is formulated. Capitalizing on the alternating direction method of multipliers (ADMM), fractional programming (FP) and alternating optimization (AO) methods, efficient suboptimal solutions are obtained for the non-convex design problems. Simulation results are presented to validate the accuracy of the analytical ergodic sum capacity expressions and evaluate the impact of key system parameters such as CSI, Rician $K$ factor, number of RIS elements, and RIS location on the ergodic capacity performance. The findings suggest that the proposed statistical CSI design achieves decent performance compared with the instantaneous CSI based design. Moreover, a signal hot spot can be created when placing the RIS close to the users.

Ambient backscatter communication (BackCom) is faced with the challenge that a single BackCom device can occupy multiple orthogonal resource blocks unintentionally. As a result, in order to avoid co-channel interference, a conventional approach is to serve multiple BackCom devices in different time slots, which reduces both spectral efficiency and connectivity. This letter demonstrates that the use of non-orthogonal multiple access (NOMA) can efficiently improve the system throughput and support massive connectivity in ambient BackCom networks. In particular, two transceiver design approaches are developed in the letter to realize different tradeoffs between system performance and complexity.

Intelligent reflecting surface (IRS) has recently emerged as a promising technology for 6G wireless systems, due to its capability to reconfigure the wireless propagation environment. In this paper, we investigate a Rate-Splitting Multiple Access (RSMA) for IRS-assisted downlink system, where the base station (BS) communicates with single-antenna users with the help of an IRS. RSMA relies on rate-splitting (RS) at the BS and successive interference cancellation (SIC) at the users and provides a generalized multiple access framework. We derive a new architecture called IRS-RS that leverages the interplay between RS and IRS. For performance analysis, we utilize an \textit{on-off control technique} to control the passive beamforming vector of the IRS-RS and derive the closed-form expressions for outage probability of cell-edge users and near users. Moreover, we also analyze the outage behavior of cell-edge users for a sufficiently large number of reflecting elements. Additionally, we also analyze the outage performance of cooperative RS based decode-and-forward (DF)-assisted framework called DF-RS. Through simulation results, it is shown that the proposed framework outperforms the corresponding DF-RS, RS without IRS and IRS-assisted conventional non-orthogonal multiple access (NOMA) schemes. Furthermore, the impact of various system's parameters such as the number of IRS reflecting elements and the number of users on the system performance is revealed.

Mobile edge computation partitioning is an effective technique to improve the applications performance on the mobile devices by selectively offloading some computations from the devices to the nearby edge cloud. Most previous works focus on the computation partitioning for a single user. Recent works begin to study the computation partitioning in a multiple user environment in which a number of users compete for the constrained computation resources on the edge cloud. However, these works neglect the fact that the users normally also share the network resources to access the edge cloud, and thus the allocation of bandwidth to the users significantly affects the overall performance of the users. In this paper, we study network aware mobile edge computation partitioning in multi-user environments , i.e., to decide for each user which parts of the application should be offloaded onto the edge cloud, and which others should be executed locally, and meanwhile to allocate the access bandwidth among the users, such that the average application performance of the users is maximized. This problem is new in that we consider the competition among users for the network bandwidth as well as the computation resources in a multi-user environment. With a set of novel models and formulations, we transform the problem into the classic Multi-class Multi-dimensional Knapsack Problem, and develop an effective algorithm, namely Performance Function Matrix based Heuristic (PFM-H), to solve it. We further consider the user mobility and design effective online algorithms that could be easily deployed in practical systems. Comprehensive trace driven simulations show that our proposed algorithm outperforms the benchmark algorithms significantly in the average application performance.

Energy beamforming takes center stage in radio frequency (RF)-enabled wireless energy transfer (WET) systems. In this letter, we study multi-user energy beamforming to minimize the overall charging time for energy harvesting users (EHUs) with different energy requirements. In order to lessen the cost and power consumption by reducing the required RF chains, it is of consequence to figure out low-rank optimal energy beamforming. By analyzing an upper bound on the rank of a minimum-rank solution, we develop a method to figure out low-rank optimal energy beamforming from the formulated semi-definite programming problem. Our simulation results exhibit the proposed energy beamforming considerably reduces the charging time required until all the requests are met. We also reveal that the capacity achieving precoder for multicasting transmission is a special case of the proposed energy beamforming solution.

The acquisition of channel state information is an essential step in enabling intelligent reflecting surfaces (IRSs) for wireless communications. In this letter, we introduce a novel procedure to estimate the cascaded channel between the base station (BS), IRS, and users in a multi-user multiple-input single-output system. The common BS-IRS channel, over which all users transmit, is leveraged to decompose the cascaded channel into a series of rank-1 matrices. Low-rank matrix recovery methods are utilized to improve upon the linear minimum mean-squared error estimate of the cascaded channel. A theoretical upper bound for the mean-square error (MSE) of the proposed estimator is derived. Numerical results reveal that the proposed techniques outperform the existing counterparts in terms of the MSE and scale with the number of BS antennas.

Referring to the system equipped with single-bit converters, 1-bit mmWave communications is gaining increasing attention for its superb cost efficiency. However, the inherent non-linear distortion renders the detectors designed for classical transparent communications inapplicable, leading to an urgent need for novel detecting solutions dedicated to 1-bit systems. Although a few endeavours have been made towards learning-based (as opposed to the traditional model-based ) detectors for multi-user (MU) 1-bit systems, they are exclusively limited to narrowband channels and fail to cope with the multi-path effects inevitable to mmWave systems. In this paper, we first design a learning-based detector (LeaD) for general wideband multi-user (wMU) scenarios. Though stemming from block-based detection, the classic workhorse for transparent systems, LeaD faces either unaffordable complexity or unacceptable data rate in 1-bit systems. Given the impracticability of block-based detection, we resort to the serial detection mechanism and henceforth devise a so-termed model-enhanced (Me-)LeaD by utilizing the channel delay-domain information. Me-LeaD can be further augmented by exploiting the channel angular-domain information. Underpinned by a judiciously tailored method for extracting tbe model information, the proposed Me-LeaD demonstrates a decent overall performance in general 1-bit wMU scenarios.

Among the existing platforms, the mobile platform provides most of the augmented reality experiences for users. Augmented reality solutions have been applied in several contexts due to technological advances in this area. However, many of these advances are still out of reach of more massive access by regular users due to access limitations or the cost of these new technologies. After conducting a literature review considering the application of data visualization in augmented reality environments, we have identified an evolution of equipment used in this context with a decrease of low-cost proposals. Hence, the works related to these two areas showed high use of technologies not yet accessible to the vast majority of users. Thus, we propose a low-cost augmented reality application that can be used on mobile devices for data visualization. We developed a hybrid proposal of an augmented reality prototype to interacting with cards through the mobile device to support the process of creating data visualization. The tangible cards have a design that helps the user in the visualization creation process, indicating each card’s role in the interaction process. The proposed application supports a multi-user environment, allowing multiple users to interact simultaneously with data visualizations in the AR environment. The visualizations are generated on a dedicated server for this function, providing a set of seven different types of visualization. To validate the developed prototype we performed two tests, the first being a stress assessment on the server that generates data visualizations to analyze the proposed application’s scalability. The second was a remote usability test with twelve participants, to evaluate the interaction and design of the cards, and the viability of this solution for augmented reality applications.

Recently, people are paying more and more attention to privacy-enhanced computing, mostly because of the threat of network information leakage. In order to ensure the security of data in network communication, we have designed privacy-enhanced multi-user quantum private data query using partial quantum homomorphic encryption to facilitate the protection of the personal privacy of the database and users. Many QPQ protocols are based on QKD theory and lack the identity authentication of users. The protocols are vulnerable to external eavesdropping attacks. The identity authentication process of this scheme confirms the real identity of the user and avoids external eavesdropping attacks to a certain extent. The scheme allows the use of universal quantum circuits to perform quantum transformation on the user’s arbitrarily encrypted input data, which improves the accuracy of the data query process. In addition, the scheme applies partial quantum homomorphic encryption theory to multi-user quantum private data query, which can realize the function of n users querying data at the same time. We have implemented a security analysis on the scheme and compared with other QPQ protocols, we found that this scheme is safe and effective.

Scheduling the transmission of time-sensitive information from a source node to multiple users over error-prone communication channels is studied with the goal of minimizing the long-term average age of information (AoI) at the users. A long-term average resource constraint is imposed on the source, which limits the average number of transmissions. The source can transmit only to a single user at each time slot, and after each transmission, it receives an instantaneous ACK/NACK feedback from the intended receiver, and decides when and to which user to transmit the next update. Assuming the channel statistics are known, the optimal scheduling policy is studied for both the standard automatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols. Then, a reinforcement learning (RL) approach is introduced to find a near-optimal policy, which does not assume any a priori information on the random processes governing the channel states. Different RL methods including average-cost SARSA with linear function approximation (LFA), upper confidence reinforcement learning (UCRL2), and deep Q-network (DQN) are applied and compared through numerical simulations.

This paper studies joint source and relay beamforming optimization and time allocation for a wireless powered multi-relay multi-user network, where a multi-antenna base station (BS) transmits information to users with the assistance of multiple single-antenna relays. Considering that the relays are energy-constrained, which are powered by the BS wirelessly, we aim to maximize the minimum rate among the users by jointly optimizing the information and energy beamforming at the BS, the distributed beamforming at the relays, and the time allocation under the power constraints and the energy causality constraints at the BS and relays, respectively. In order to tackle the formulated optimization problem, we propose an iterative algorithm by alternatively solving two subproblems. Numerical results show that the proposed joint optimization scheme obviously outperforms other schemes.

To support massive access for future wireless networks, we propose a novel reconfigurable intelligent surface (RIS)-enhanced downlink multi-user multi-input single-output (MU-MISO) symbiotic radio (SR) system. In the proposed system, each RIS not only enhances the primary transmission from the primary transmitter (PT) to the associated primary receiver (PR) nearby, but also acts as an Internet-of-Things (IoT) device to enable IoT transmissions to the same PR. Therefore, each PR needs to jointly decode the information from both the PT and its corresponding RIS. We are interested in maximizing the weighted sum-rate of both primary and IoT transmissions by jointly designing the active transmit beamforming at PT and the passive beamforming at each RIS under the maximum transmit power constraint at the PT and various constraints on the reflection coefficients (RCs), which include the ideal, continuous-phase and the discrete-phase cases. The formulated problem is non-convex, which cannot be solved directly. Thus, fractional programming (FP) method and alternating optimization (AO) technique are adopted to tackle the problem. In particular, three low-complexity algorithms are proposed to trade off between computational complexity and convergence rate. Compared to different benchmark schemes, simulation results demonstrate that with the aid of the RISs, the PRs can benefit from the enhanced primary transmission from the PT, and receive information from the associated RISs via IoT transmission.

In massive IoT (Internet of Things) environment, which constitutes a goal of the 5G standard, the communication channel of an MTC (machine-type-communication) device with narrowband transmission has problems that are deep fading and interference with the transmission performance. For improved communications in IoT environment, researchers aimed to increase the transmission throughput by using larger contention windows between channel hops when the probability of successful data communication drops during the previous time step to prevent the rapid increase in the number of connected devices. Hence, researchers propose a model to change the interframe spacing according to the number of contention windows for hopping and data prioritization by quickly determining network traffic variations in a massive network environment. Specifically, researchers propose CSMA/CA (carrier-sense multiple access with collision avoidance) channel hopping to prevent deep fading and obtain multiuser diversity in multichannel environments as well as improve the channel quality. Researchers confirmed that the proposed CSMA/CA channel hopping provides a data transfer rate about twice that of conventional TDMA (time-division multiple access) channel hopping for 60 connected devices. As a result of analyzing data transfer rates in channel hopping, it was shown that the collision probability is not high when the number of nodes is small, so the number of channels and contention window size are not significantly affected, but as the number of nodes increases, the number of channels and contention window size are greatly affected.

WiFi based gesture recognition has received significant attention over the past few years. However, the key limitation of prior WiFi based gesture recognition systems is that they cannot recognize the gestures of multiple users performing them simultaneously. In this article, we address this limitation and propose WiMU, a Wi Fi based M ulti- U ser gesture recognition system. The key idea behind WiMU is that when it detects that some users have performed some gestures simultaneously, it first automatically determines the number of simultaneously performed gestures ( $N_a$ N a ) and then, using the training samples collected from a single user, generates virtual samples for various plausible combinations of $N_a$ N a gestures. The key property of these virtual samples is that the virtual samples for any given combination of gestures are identical to the real samples that would result from real users performing that combination of gestures. WiMU compares the detected sample against these virtual samples and recognizes the simultaneously performed gestures. We implemented and extensively evaluated WiMU using commodity WiFi devices. Our results show that WiMU recognizes 2, 3, 4, 5, 6, 7, and 8 simultaneously performed gestures with accuracies of 95.6, 94.9, 93.9, 92.7, 91.6, 91.0, and 90.1 percent, respectively.

Non-Terrestrial Networks (NTNs) composed of space-borne (e.g., satellites) and airborne vehicles (e.g., drones and blimps) have recently been proposed by 3GPP as a new paradigm of infrastructures to enhance the capacity and coverage of existing terrestrial wireless networks. The mobility of non-terrestrial base stations (NT-BSs) however leads to a dynamic environment, which imposes unique challenges for handover and throughput optimization particularly in multi-user access control for NTNs. To achieve performance optimization, each terrestrial user equipment (UE) should autonomously estimate the dynamics of moving NT-BSs, which is different from the existing user access control schemes in terrestrial wireless networks. Consequently, new learning schemes for optimum multi-user access control are desired. In this article, we therefore propose a UE-driven deep reinforcement learning (DRL) based scheme, in which a centralized agent deployed at the backhaul side of NT-BSs is responsible for training the parameter of a deep Q-network (DQN), and each UE independently makes its own access decisions based on the parameter from the trained DQN. With the proposed scheme, each UE is able to access a proper NT-BS intelligently to enhance the long-term system throughput and avoid frequent handovers among NT-BSs. Through comprehensive simulation studies, we justify the performance of the proposed scheme, and show its effectiveness in addressing the fundamental issues in the NTNs deployment.

In this paper, we investigate the adaptive video delivery for multiple users over time-varying and mutually interfering multi-cell wireless networks. The key research challenge is to jointly design the physical-layer resource allocation scheme and application-layer rate adaptation logic, such that the users’ long-term fair quality of experience (QoE) can be maximized. Due to the timescale mismatch between these two layers and the asynchrony of user requests, however, it is difficult to directly model the cross-layer stochastic control problem by using a reinforcement learning framework. To address this difficulty, we propose a novel two-level decision framework where an optimization-based beamforming scheme (performed at the base stations) and a deep reinforcement learning (DRL)-based rate adaptation scheme (performed at the user terminals) are, respectively, developed, such that a highly complex long-term multi-user QoE fairness problem is decomposed into some relatively simple problems and solved effectively. Our strategy represents a significant departure from the existing schemes with consideration of either a short-term multi-user QoE maximization or a long-term single-user point-to-point QoE maximization. Extensive simulations demonstrate that the proposed cross-layer design is effective and promising.

Interleaver centric systems have a significant role to play in fifth generation (5G) real-time communication. Flip Left–Right approach based Inverse Tree Interleavers (FLRITIs) are among one of the recently developed interleavers for these interleaver centric systems. During the real-time communication, interleaver assignment requests from multiple users often arrive at next generation NodeB (gNB) simultaneously. Therefore, a mechanism is essentially needed to handle these simultaneous interleaver assignment requests at the gNB. In this paper, an efficient mechanism to handle the simultaneous interleaver i.e. FLRITI assignment requests originated from different wireless transmit receive units is proposed. This mechanism helps the network in taking a correct logical decision based on pre-configured policy/policies while assigning correct sequence number and hence, the correct FLRITI to each 5G user from the inverse tree structure. The method is equally effective in all types of time-critical and non-time critical 5G communication scenarios e.g. Ultra reliable low latency communication 5G new radio, enhanced mobile broadband etc.

In view of reducing the complexity of signal detection in massive multiple-input multiple-output (MIMO) receivers, the use of non-coherent detection is favored over usual coherent techniques that require complex channel estimation. In this paper, a deep-learning approach to implement non-coherent multiple-symbol detection for differential phase-shift keying (DPSK) multi-user massive MIMO systems is proposed. The proposed deep-learning implementation reduces the high computational complexity required in conventional DPSK detection techniques. Two deep-learning-based multiple-symbol differential detection receiver designs are proposed and compared with decision-feedback differential detection (DFDD), and conventional multiple-symbol differential detection (MSDD) for the same system parameters. Multiple-symbol differential sphere detection (MSDSD) is used to implement conventional MSDD. The results show that the proposed deep-learning-based multiple-symbol differential detection implementation outperforms decision-feedback differential detection and achieves an optimal performance compared to conventional multiple-symbol differential detection implemented by multiple-symbol differential sphere detection for single-user and multi-user scenarios.

In this work, we experimentally achieved a real-time video optical wireless transmission which can broadcast two users in different positions, simultaneously. Firstly, a single-pixel 50-μm micro-LED is designed and fabricated on a GaN-based wafer and then two micro-LED chips are connected as parallel form in a single bulb. The transceiver based on two FPGA boards is used to generate quadrature amplitude modulation-orthogonal frequency division multiplexed (QAM-OFDM) signals and measure the communication performance of the VLC system. The low-power micro-LEDs bulb emits 0.8 mW optical power through two optical paths at 20 mA driving current, and the modulation bandwidths for two users’ access are 90 MHz and 73 MHz, respectively. In this two-user real-time video transmission system, the total data rate exceeds 105.54 Mbps without naked eye discernable distortion over 2-m free-space distance. Different from traditional point-to-point links, this work demonstrates the possibility of flexible high-speed multi-user real-time video optical wireless transmission based on a parallel micro-LEDs bulb.

We consider a distributed secret sharing system that consists of a dealer, n storage nodes, and m users. Each user is given access to a certain subset of storage nodes, where it can download the stored data. The dealer wants to securely convey a specific secret $\text {s}_{\text {j}}$ to user j via storage nodes, for $\text {j}=1,2, {\dots },\text {m}$ . More specifically, two secrecy conditions are considered in this multi-user context. The weak secrecy condition is that each user does not get any information about the individual secrets of other users, while the perfect secrecy condition implies that a user does not get any information about the collection of all other users’ secrets. In this system, the dealer encodes secrets into several secret shares and loads them into the storage nodes. Given a certain number of storage nodes we find the maximum number of users that can be served in such a system and construct schemes that achieve this with perfect secrecy. We further define two major properties for such distributed secret sharing systems; communication complexity is defined as the total amount of data that users need to download in order to reconstruct their secrets; and storage overhead is defined as the total size of data loaded by the dealer into the storage nodes normalized by the total size of secrets. Lower bounds on the minimum communication complexity and the storage overhead are characterized given any n and m. We construct distributed secret sharing protocols, under certain conditions on the system parameters, that attain the lower bound on the communication complexity while providing perfect secrecy. Furthermore, we construct protocols, again under certain conditions, that simultaneously attain the lower bounds on the communication complexity and the storage overhead while providing weak secrecy, thereby demonstrating schemes that are optimal in terms of both parameters. It is shown how to modify the proposed protocols in order to construct schemes with balanced storage load and communication complexity.

In this article, we investigate a multi-user multiple-input-single-output full-duplex system with simultaneous wireless information and power transfer under the non-linear energy harvesting model, where channel state information is imperfect. Since the channel estimation error is considered in our proposed system, the self-interference cannot be canceled perfectly. For such a system, a robust optimization problem that maximizes the minimum average total harvested power by jointly optimizing the uplink and downlink beamforming matrices is formulated. As the problem is non-linear and non-convex under channel estimation errors, a novel Newton method based iterative optimization algorithm is proposed to solve it efficiently. In each iteration, the semidefinite relaxation technique and S-Procedure method are applied to obtain the optimal beamforming vectors, which are proved to be optimal solutions with rank-one. Simulation results reveal that the proposed robust design can achieve better system performance than the non-robust scheme, both of which are based on the non-linear energy harvesting model.

Mobile edge computing (MEC) is a new paradigm to alleviate resource limitations of mobile IoT networks through computation offloading with low latency. This article presents an efficient and secure multi-user multi-task computation offloading model with guaranteed performance in latency, energy, and security for mobile-edge computing. It does not only investigate offloading strategy but also considers resource allocation, compression and security issues. Firstly, to guarantee efficient utilization of the shared resource in multi-user scenarios, radio and computation resources are jointly addressed. In addition, JPEG and MPEG4 compression algorithms are used to reduce the transfer overhead. To fulfill security requirements, a security layer is introduced to protect the transmitted data from cyber-attacks. Furthermore, an integrated model of resource allocation, compression, and security is formulated as an integer nonlinear problem with the objective of minimizing the weighted sum of energy under a latency constraint. As this problem is considered as NP-hard, linearization and relaxation approaches are applied to transform the problem into a convex one. Finally, an efficient offloading algorithm is designed with detailed processes to make the computation offloading decision for computation tasks of mobile users. Simulation results show that our model not only saves about 46% of system overhead consumption in comparison with local execution but also scale well for large-scale IoT networks.

This study investigates the impact of co-channel interference caused by external terminals on multi-user bi-directional amplify-and-forward based wireless relaying network. The investigation considers multi-user pair and a single bi-directional relay terminal and a finite number of co-channel interference in the system model. A lower bound on the end-to-end outage probability is derived for the simultaneous transmission and the opportunistic source-pair selection strategy. According to analytical, asymptotic, and Monte-Carlo based computer simulation results, the opportunistic source-pair selection strategy achieves a near-optimal performance for the inter-cell interference caused by other user-pairs within the system. Results also show that the opportunistic source-pair selection strategy also achieves optimal diversity order, which is equal to N, in high signal-to-noise ratio regimes. Results also show that the co-channel interference degrades the achievable diversity order from N to 0 while also affecting the system coding gain.

To effectively improve the throughput of the IEEE contention-based wireless local area network (WLAN) system, many uplink medium access control layer (MAC) protocols based on multi-user multiple-input multiple-output (MU-MIMO) technology have been proposed. However, a common problem of these approaches is that many physical layer (PHY) operations, e.g., synchronization, are not considered. As a result, existing methods may find difficulties in real-world implementation. In this article, we propose a simple yet practical uplink scheme for systems with multiple antennas. The main idea is to conduct multi-user beamforming (MU-BF) at an access point such that it can resolve collided signals from multiple competitive users; the proposed scheme can effectively reduce the upstream collision probability and improve system throughput. The other distinct advantage of the proposed scheme is that the MAC protocol of the original IEEE 802.11 standard, i.e., distributed coordination function (DCF), can remain unchanged. The mathematical analysis of system performance is also presented and verified. Simulations show that the proposed scheme significantly outperforms conventional WLAN systems.

The technology of static user-centric virtual cell (VC) has been designed in the fifth-generation (5G) ultra-dense networks (UDNs) for alleviating both frequent handover and inter-cell interference. However, to provide the user-centric services, the fairness of resource management must be involved when the common vehicular-to-X (V2X) messages are multicast to the vehicle groups. In this paper, a dynamic user-centric virtual cell (DUVC) scheme is proposed for updating adaptively the VC through the mobile tracking of the vehicles. Furthermore, an approximation algorithm is proposed for solving the max-min-fair problem of resource management in V2X communication in order to better support V2X services throughout the service VC. Numerical results are provided for demonstrating that the proposed DUVC scheme is suitable for implementing in the V2X communication. Finally, the proposed algorithm is shown to outperform three other existing algorithms in terms of fairness of resource management.

Multimedia cloud storage which saves the huge storage overhead of local devices has attracted considerable attention. However, due to the lack of physical control of data, the privacy protection of data on the multimedia cloud has become one of the main concerns of users. Public-key encryption with keywords search (PEKS) is a technique that can keep the privacy and searchability of data in the cloud. In this paper, we present a PEKS with time-controlled proxy re-encryption model which allows the data owner to delegate the access right of the encrypted multimedia database to other users, to achieve the time-controlled multi-user search. Furthermore, it is designed to resist keywords guessing attack and support conjunctive keywords search. Compared with previous works which require a time server to generate a time seal for the generation of the search token, this model embeds the time information of accessing the encrypted database into public and secret key pairs of data users, which saves the managing overhead and reduces the security risks resulting from an extra server. In addition, most existing PEKS schemes were constructed based on the hardness of classical mathematical problems which can be broken by quantum computers. To address this issue, a lattice-based PEKS scheme based on the above model is proposed, which can be considered as the candidate for protecting multimedia data security in the quantum era.

In a wireless self-organizing and the network-assisted Device-to-Device (D2D) network, proximal Device Discovery (DD) plays a noteworthy role. It is the foundation for self-governing, coverage expansion and performance improvement for example, cooperative and multi-hop transmission. Energy efficiency and discovery accuracy are critical performance measures for proximal DD. A novel DD procedure using Non-orthogonal Multiple Access based Multi-user Shared Access (MUSA) is proposed in this article for network-assisted D2D communication. The proposed shared access scheme uses a complex spreading pattern to accommodate dense device discovery and enhance discovery distance in the cellular systems. A discovery signal shared access method with supplementary schemes for multi-user detection is also designed. With MUSA and its spreading pattern demodulation, 12% enhanced energy efficiency, and 98% overall improved accuracy is achieved contrasted with the customary DD methods.

In this paper, we investigate the performance of various multi-user scheduling algorithms over independent and non-identically distributed generalized fading channel and 3GPP long term evolution (LTE) networks. Multi-user scheduling is an important task to be performed in wireless communication networks to share the bandwidth resource effectively between large number of users. In this paper, novel expressions for average BER, average channel capacity and fairness among users are derived for various scenarios such as (1) independent identical fading parameter, independent non identical average SNR channel conditions and (2) both fading parameter and average SNR are independent and non-identically distributed under absolute SNR (AS) scheduling and threshold SNR scheduling. More over the performance of threshold SNR scheduling is compared with AS scheduling and other existing scheduling techniques to identify a suitable scheduling algorithm for LTE networks. Analytical expressions are simulated and validated through MATLAB, and insightful discussions are provided from the numerical results.

The security of public-key encryption (PKE) schemes in a multi-user setting is aimed at capturing real-world scenarios in which an adversary could attack multiple users and multiple ciphertexts of its choice. However, the fact that a real-world adversary can also mount key-exposure attacks for a set of multiple public keys requires us to consider a more realistic notion of security in multi-user settings. In this study, we establish the security notion of PKE in a multi-user setting with corruptions, where an adversary is able to issue (adaptive) encryption, decryption, and corruption (i.e., private key) queries. We then propose the first practical PKE scheme whose security is proven in a multi-user setting with corruptions. The security of our scheme is based on the computational Diffie–Hellman (CDH) assumption and is proven to be tightly chosen-ciphertext secure in a random oracle model. Our scheme essentially follows the recently proposed modular approach of combining KEM and augmented DEM in a multi-user setting, but we show that this modular approach works well in a multi-user setting with corruptions.

Dynamic adaptive video streaming over hypertext transfer protocol (DASH) plays a key role in video transmission over the Internet. The conventional DASH adaptation approaches mainly focus on optimizing the overall quality of experience (QoE) for all client sides, neglecting the QoE diversity of different users. In this paper, we propose a meta-learning framework for multi-user preferences (MLMP) as a new DASH adaptation approach, which is able to optimize the diverse QoE of different users. Specifically, we first design a subjective experiment to analyze the difference of QoE preferences across users, in which QoE refers to the metrics of visual quality, fluctuation, and rebuffering events. Based on our findings, we formulate the QoE optimization of multi-user preferences as a multi-task deep reinforcement learning (DRL) problem. In our formulation, the QoE preference of each user is modeled in the overall QoE calculation via assigning the weights to the three QoE metrics. Then, the MLMP framework is developed to solve the proposed multi-task DRL problem, such that the preferences regarding visual quality, fluctuation, and rebuffering events can be optimized for different users in DASH adaptation. Finally, the simulation results show that the proposed approach outperforms state-of-the-art DASH adaptation approaches in satisfying the different users' QoE preferences regarding visual quality, fluctuation, and rebuffering events.

Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-limited mobile devices from computation-intensive tasks, which enables devices to offload workloads to nearby MEC servers and improve the quality of computation experience. In this paper, an MEC enabled multi-user multi-input multi-output (MIMO) system with stochastic wireless channels and task arrivals is considered. In order to minimize long-term average computation cost in terms of power consumption and buffering delay at each user, a deep reinforcement learning (DRL)-based dynamic computation offloading strategy is investigated to build a scalable system with limited feedback. Specifically, a continuous action space-based DRL approach named deep deterministic policy gradient (DDPG) is adopted to learn decentralized computation offloading policies at all users respectively, where local execution and task offloading powers will be adaptively allocated according to each user’s local observation. Numerical results demonstrate that the proposed DDPG-based strategy can help each user learn an efficient dynamic offloading policy and also verify the superiority of its continuous power allocation capability to policies learned by conventional discrete action space-based reinforcement learning approaches like deep Q-network (DQN) as well as some other greedy strategies with reduced computation cost. Besides, power-delay tradeoff for computation offloading is also analyzed for both the DDPG-based and DQN-based strategies.

In this paper, we propose an Interleave-Division Multiple Access (IDMA) based uplink multi-user system fornext generation WLAN. By minimizing the latency throughaccurate detection per iteration, we were able to design a receiver architecture that meets the latency demands of current IEEE802.11 WLAN. To do this, the proposed system utilizes a novel algorithm for simplified LLR calculation of the soft input soft output demapper needed in the IDMA first stage detection.The proposed system has a maximum of 34.8 bits/s/Hz spectral efficiency for a single spatial stream and can support up to 8 users in a single 20MHz channel. We compare the proposed system to a reference OFDMA system and show its advantages in terms of diversity, flexibility and BER performance. Keywords: 802.11ax; IDMA; uplink multi-user access

Reconfigurable intelligent surface (RIS) has drawn considerable attention from the research society recently, which creates favorable propagation conditions by controlling the phase shifts of the reflected waves at the surface, thereby enhancing wireless transmissions. In this paper, we study a downlink multi-user system where the transmission from a multi-antenna base station (BS) to various users is achieved by the RIS reflecting the incident signals of the BS towards the users. Unlike most existing works, we consider the practical case where only a limited number of discrete phase shifts can be realized by the finite-sized RIS. Based on the reflection-dominated one-hop propagation model between the BS and users via the RIS, a hybrid beamforming scheme is proposed and the sum-rate maximization problem is formulated. Specifically, the continuous digital beamforming and discrete RIS-based analog beamforming are performed at the BS and the RIS, respectively, and an iterative algorithm is designed to solve this problem. Both theoretical analysis and numerical validations show that the RIS-based system can achieve a good sum-rate performance by setting a reasonable size of RIS and a small number of discrete phase shifts.

Ultra-Reliable Low-Latency Communications (URLLC) are considered as one of the key services of the upcoming fifth generation (5G) of wireless communications systems. Enabling URLLC is especially challenging due to the strict requirements in terms of latency and reliability. Multi-connectivity is a powerful approach to increase reliability. However, most of the current research is restricted to single-user scenarios, neglecting the challenges of multi-cellular, multi-user systems, i.e., interference and the competition for limited resources. In this article, we develop analytic comparisons of different connectivity approaches, showing that multi-connectivity may not always be optimal in the considered scenario. Moreover, we propose and evaluate novel resource allocation approaches based on stable matching theory to enable wireless URLLC. We extend the pure many-to-one stable matching procedure by utilizing the optimal connectivity approach for each user, optimizing the maximum number of matched resources, and providing a resource reservation mechanism for users suffering from bad channel conditions. System-level simulations demonstrate that the proposed algorithm outperforms baseline resource allocation approaches in outage probability by up to three orders of magnitude. Even in a highly loaded system, an outage probability in the range of ${10^{-5}}$ is achieved.

Multi-user file-sharing plays an important role in the cloud storage. It allows different users to share files via the cloud. However, it usually faces many security issues because the cloud is usually public and semi-trusted. To overcome these issues, we propose multi-user file-sharing systems (MFS) in this paper. According to the date sources, we propose two frameworks of MFS based on the framework of proxy re-encryption (PRE), unique source (uni-source) MFS and multi-source MFS. We formalize the security models, which contain ciphertext security, secret key security and master secret security. With the frameworks, we propose two secure and efficient MFS schemes based on LWE, the uni-source MFS UM and the multi-source MFS MM. We prove that UM is secret key and ciphertext security, and MM is secret key, master secret and ciphertext security with the hardness assumptions of LWE and SIS. Furtherly, we prove they have some useful properties, such as non-interactive and anonymous.

Millimeter wave (mmWave) in the unlicensed 60 GHz band provides extremely large spectrum utilization, which is a promising technique due to the rapid growth of wireless communication services. With wireless devices explosively increasing, the limited number of association and beamforming training (A-BFT) opportunities in conventional protocol leads to high packet collisions. In this paper, we design a novel non-slotted A-BFT frame structure for multi-users in order to alleviate packet collisions providing flexible training slots. To further reduce collision occurrence, we propose two association mechanisms including the time-based distributed coordination function (TDCF) and time-based beam collision avoidance (TBCA) schemes. The theoretical closed-forms of both TDCF and TBCA methods are also derived, and proved to outperform the random association. Furthermore, the adaptive beam search (ABS) and dynamic A-BFT adjustment (DAA) schemes are proposed to consider historical beam training information by adjusting the number of training beams and A-BFT length, respectively. Simulations are conducted to show that our proposed ABS, DAA, and TDCF/TBCA mechanisms outperform existing methods; whereas higher spectrum efficiency can be achieved by proposed non-slotted frame structure compared to conventional slotted structure.