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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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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

This paper investigates the relay energy allocation scheme based on the time switching (TS) operation strategy in multi-user simultaneous wireless information and power transfer (SWIPT) relaying system, where the relay is energy-constrained and utilizes the energy harvested in the energy harvesting mode to amplify and forward the information of the users. In the multi-user relaying system, the distances of the receivers of the users to the relay may be different. Thus, the total information rate maximization model is proposed and the corresponding energy allocation scheme is derived. After comparing the information rates of users, it is found that the information rates of users who are far from the relay are significantly lower than those of users who are close to the relay, i.e., “far-near” phenomenon. For the “far-near” problem, we propose the common information rate maximization model, and derive the corresponding energy allocation scheme. In this model, the information rates of all users are equal, which ensures the fairness of the information rate in multi-user relaying communication system. The simulation results show that the energy allocation scheme based on the common information rate maximization model can effectively solve the “far-near” problem in multi-user relaying communication system.

A multi-user downlink system is considered, in which a base station (BS) serves multiple user equipments (UEs). It is assumed that, to improve the downlink performance, the UEs can exchange compressed versions of the downlink received signals on out-of-band device-to-device (D2D) communication links. Since the D2D links from a UE to the other UEs may have different channel qualities across the receiving UEs, this letter proposes a layered cooperation strategy which combines broadcast coding and successive refinement quantization techniques. With this approach, a UE can have a better reconstruction of the downlink signal of the transmitting UE than another UE with worse D2D channel quality. The problem of jointly optimizing the downlink precoding and layered UE cooperation strategies is tackled, and numerical results show the performance gains of the proposed layered scheme as compared to baseline schemes with no or single-layer cooperation.

In this paper, we propose joint sequencing and scheduling optimization for uplink machine-type communication (MTC). We consider multiple energy-constrained MTC devices that transmit data to a base station following the time division multiple access (TDMA) protocol. Conventionally, the energy efficiency performance in TDMA is optimized through multi-user scheduling, i.e., changing the transmission block length allocated to different devices. In such a system, the sequence of devices for transmission, i.e., who transmits first and who transmits second, etc., has not been considered as it does not have any impact on the energy efficiency. In this work, we consider that data compression is performed before transmission and show that the multi-user sequencing is indeed important. We apply three popular energy-minimization system objectives, which differ in terms of the overall system performance and fairness among the devices. We jointly optimize both multi-user sequencing and scheduling along with the compression and transmission rate control. Our results show that multi-user sequence optimization significantly improves the energy efficiency performance of the system. Notably, it makes the TDMA-based multi-user transmissions more likely to be feasible in the lower latency regime, and the performance gain is larger when the delay bound is stringent., Comment: Submitted in the IEEE for possible publication

Immersive virtual reality (VR) applications require ultra-high data rate and low-latency for smooth operation. Hence in this paper, aiming to improve VR experience in multi-user VR wireless video streaming, a deep-learning aided scheme for maximizing the quality of the delivered video chunks with low-latency is proposed. Therein the correlations in the predicted field of view (FoV) and locations of viewers watching 360$^\circ$ HD VR videos are capitalized on to realize a proactive FoV-centric millimeter wave (mmWave) physical-layer multicast transmission. The problem is cast as a frame quality maximization problem subject to tight latency constraints and network stability. The problem is then decoupled into an HD frame request admission and scheduling subproblems and a matching theory game is formulated to solve the scheduling subproblem by associating requests from clusters of users to mmWave small cell base stations (SBSs) for their unicast/multicast transmission. Furthermore, for realistic modeling and simulation purposes, a real VR head-tracking dataset and a deep recurrent neural network (DRNN) based on gated recurrent units (GRUs) are leveraged. Extensive simulation results show how the content-reuse for clusters of users with highly overlapping FoVs brought in by multicasting reduces the VR frame delay in 12\%. This reduction is further boosted by proactiveness that cuts by half the average delays of both reactive unicast and multicast baselines while preserving HD delivery rates above 98\%. Finally, enforcing tight latency bounds shortens the delay-tail as evinced by 13\% lower delays in the 99th percentile., Accepted for publication in IEEE Transactions on Communications 17 pages, 10 Figures

Generation and allocation of Target Channel Sequence (TCS) in relation to spectrum handoff introduces several challenges in a multi-user Cognitive Radio Network. The primary challenge is to ensure fair and unique TCS allocation to multiple Secondary Users (SUs) in order to prevent channel access conflict during concurrent handoff operations. Another challenge is to perform dynamic TCS generation to guard against channel obsolescence effects under time-varying user activities. Inclusion of real-time SUs further mandates the use of time-bound TCS generation in order to prevent call drops due to time-outs during handoff. To address these issues of fairness, uniqueness, dynamic and time-bounded TCS generation, this paper proposes a novel machine-learning enabled three-phase TCS generation and allocation scheme for multiple real-time SUs involved in Voice over IP (VoIP) communication. While proactive and periodic phases compute unique TCS in O (n log n) time, the reactive phase performs on-demand TCS generation. Generation of TCS is formulated as a fractional knapsack problem and greedy technique is applied therein to optimally select channels based on their existing conditions. A novel HMM-BiLSTM (Hidden Markov Model-Bidirectional Long Short Term Memory) framework is further devised to safeguard VoIP interests through periodic channel eligibility prediction. Additionally, an analytical framework is developed which records negligible probability of channel access conflict among multiple SUs. Simulation results confirm fair and dynamic channel allocation with reduced handoff delays and call-drops and intelligent learning based decision-making operations with high accuracy in comparison to existing approaches. Finally, test-bed implementation validates practical applicability of the proposed methodology.

The coexistence of parallel applications in shared computing nodes, each one featuring different Quality of Service (QoS) requirements, carries out new challenges to improve resource occupation while keeping acceptable rates in terms of QoS. As more application-specific and system-wide metrics are included as QoS dimensions, or under situations in which resource-usage limits are strict, building and serving the most appropriate set of actions (application control knobs and system resource assignment) to concurrent applications in an automatic and optimal fashion become mandatory. In this paper, we propose strategies to build and serve this type of knowledge to concurrent applications by leveraging Reinforcement Learning techniques. Taking multi-user video transcoding as a driving example, our experimental results reveal an excellent adaptation of resource and knob management to heterogeneous QoS requests, and increases in the amount of concurrently served users up to 1.24 × compared with alternative approaches considering homogeneous QoS requests.

802.11ax introduces OFDMA to WiFi. It thus enables multiplexing users/user groups in the frequency domain. WiFi networks usually operate in a multipath environment which generates a frequency selective channel. Hence, the capacity of a user/user group changes over different subcarriers. A good scheduling and resource allocation scheme can maximize user rates by allocating users and user groups on subcarriers based on their CSI and other system considerations. In this paper, we investigate how to optimally assign users and user groups to subcarriers with the goal of maximizing the throughput in the context of 802.11ax. We introduce a novel divide and conquer based algorithm which we prove to be optimal under the assumption that a user can be assigned to more than one resource unit. We also introduce two practical algorithms, a greedy and a recursive one, which split the bandwidth into resource units and schedule users on them while observing all 802.11ax protocol constraints. Extensive simulations and experiments comparing the performance of the aforementioned algorithms establish that our practical schemes achieve very good performance in all studied scenarios while handling a plethora of real-world constraints.

Full Duplex (FD) wireless communications is considered to be the next big step for future Wireless Local Area Networks (WLANs). Old (IEEE 802.11ac) and new (IEEE 802.11ax) WLAN features are expected to co-exist with FD operation. Some of these features include Downlink Multi-User (DL MU) transmissions at the physical layer while at the Medium Access Control (MAC) layer techniques such as the Transmission Opportunity (TXOP) sharing mechanism facilitates these multiuser transmissions. The traditional Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol is inadequate to support full duplex communications efficiently. A very promising adaptation of CSMA/CA and a powerful candidate as the basic channel access method for future WLANs is the Carrier Sense Multiple Access with Enhanced Collision Avoidance (CSMA/ECA). In this paper, we provide an analytical model to assess the performance of resource sharing techniques in full duplex WLANs operating with the CSMA/ECA protocol. Our analysis is based on Markov chains to obtain an estimate of the achievable throughput of the Quality of Service Access Point (QAP) during DL MU transmissions.

The optimal spectrum access strategy is investigated for multi-user multi-channel scenario in cognitive radio networks. At first, an online learning method based on Dirichlet Process is adopted to predict the channel usage based on ACK/NACK feedbacks, which can avoid frequent information exchange among users. Based on the prediction result, the delay performance can be computed when a user transmits certain percentage of multimedia packets on a specific channel. Second, the packet delivery ratio (PDR) is derived from the prediction result of channel usage to reflect the accessing competition among multiple users. Finally, the quality of service (QoS) of multimedia applications is defined as the joint delay and throughput performances. Moreover, a dynamic spectrum access scheme is proposed to optimize the QoS metrics. The simulation results demonstrate that the QoS and the peak-signal-to-noise ratio (PSNR) of the proposed spectrum access algorithm outperform the three existing spectrum access algorithms, i.e., cognitive cross-layer algorithm, dynamic learning algorithm, and dynamic least interference algorithm. The proposed algorithm achieves more than 21.8%, 5.4%, and 3.9% PDR enhancement and over 3.23 dB, 0.82 dB, and 0.50 dB PSNR gains, compared with those three algorithms, given the transmission power as 10, 20, and 30 units, respectively.

For Internet of Vehicles (IoV) systems with multiple users, network coding can be introduced to provide efficient error control and throughput improvement services. However, if the heterogeneity characteristics and requirements of the end users (vehicles) are neglected, it will be difficult for an IoV system to provide each end user with fair system services, without which the advantages of network coding cannot be fully achieved and the performance of the multi-user diversity system will be degraded. In this paper, we propose a Dynamic Resource Scheduling Optimization (DRSO) algorithm, a dynamic fair scheduling algorithm combined with network coding for system resource allocation in a multi-user IoV system. We construct a general solution framework for service scheduling: first, we estimate the fairness index for each end user (vehicle) with the key information on Quality of Service (QoS). Second, we construct a service scheduling control model based on the service capability of control entities (multi-access edge computing servers), and propose a new utility evaluation function. Third, based on the fairness index, we select end users into multiple network coding sets. Network coding sets are the basic units of service scheduling. The optimization objective of the scheduling service is to maximize the total utility of all the network coding sets (the utility of the control entity). Finally, we establish a coding cache queue in the control entity based on the scheduling decision. To obtain the global optimal solution for active queue control, we combine a Quantum Particle Swarm Optimization (QPSO) algorithm with a Proportional Integral (PI) model. Then, the optimal scheduling decision can be made. Extensive simulation results show that DRSO outperforms related scheduling algorithms in varying traffic loads, demonstrating that DRSO can effectively guide service resource allocation.

Driven by the ubiquitous smart devices, wireless traffic data has increased significantly and brings a great burden on backhaul links. Edge caching and device-to-device are proved not only can overcome the above challenges, but also to reduce the energy consumption of devices. Meanwhile, the development of hardware makes it possible for terminal devices to cache popular content (referred to as “helpers”). In this paper, a device-to-device assisted edge caching network via helpers is designed to provide low-latency content access and alleviate the traffic load. Then, we formulate the problem of maximization of the successful delivery rate under the constraints of the Signal-to-Interference-plus-Noise Ratio and caching capacity as an integer programming and pose a novel efficient and effective scheme. Based on the algorithm, we first transform the problem into a tractable form. Then this paper presents a hybrid of heuristic approach and iterative selection method to limit the search scope. The simulations demonstrate the method can significantly reduce the system computation time. Moreover, the effects of different scenarios and parameter settings about the successful delivery rate of the Device-to-Device assisted edge caching networks are analyzed.

Offloading technique is an effective approach to migrate tasks from mobile devices to cloud to prolong the battery life. However, it cannot be guaranteed that all devices can successfully offload their tasks to cloud due to the limited network resources, thus offloading decisions should be made to coordinate devices. The existing works focus on how to maximize energy saving for a group of devices instead of the number of mobile devices that benefits from offloading. Therefore, how to maximize the number of energy-saving devices in the multi-user offloading scenario remains a challenging issue to be solved. In this paper, we aim to obtain a beneficial offloading group where all device can offloading tasks simultaneously to achieve energy saving and the number of beneficial offloading devices maximum for the group. In order to get such a group where each device can achieve its own benefit, firstly, we adopt game theory to model each device's demand of saving energy by offloading in OFDMA communication system, and formulate multi-user offloading game problem (MUOG). Furthermore, we propose an offloading game mechanism (OGM), including: beneficial offloading threshold (BOT) algorithm and beneficial offloading group (BOG) algorithm. BOT algorithm can obtain the threshold of each device, i.e., the maximum number of mobile devices that the device can tolerate to offload tasks simultaneously while BOG algorithm can obtain a group of beneficial offloading devices. It can be proved that OGM strategy can achieve the Nash equilibrium of MUOG problem so as to obtain the maximum number of beneficial offloading devices. Experimental comparisons verify the number of beneficial offloading users and the overhead of the mechanism among different strategies. The results show that OGM can benefit more devices by offloading to save energy without high overhead compared with other offloading strategies.

Cellular mobile systems aim at aggressive spectrum reuse to achieve high spectral efficiency. Unfortunately, this leads to unacceptable interference near cell borders. To control this, network multi-input multiple-output (MIMO) can be adopted to improve coverage and cell-edge throughput through multi-cell cooperation. With network MIMO, multiple geographically separated base stations (BSs) cooperatively serve their cell-edge users (CEUs) using their antennas, acting together as a network of distributed antenna array. It can be single-user (SU) or multi-user (MU) network MIMO by coordinating channel allocation in adjacent cells. In this paper, we make a capacity comparison of SU- and MU-network MIMO. In network MIMO, a collaborative BS simultaneously serves its own cell-center users (CCUs) and CEUs, and the CEUs of other partner BSs under a power constraint. As a result, power management among three types of users (intra-cell CCUs/CEUs, inter-cell CEUs) becomes necessary. Accordingly, we propose power management methods to help raise the signal strength of inter-cell CEUs and in the meantime gratify the performance of intra-cell users. Simulation results show that MU-network MIMO with superposition coding offers much better CEU capacity than SU-network MIMO. As for the CCU capacity, MU-network MIMO is generally better than SU-network MIMO.

MU-MIMO is a high-speed technique in IEEE 802.11ac and upcoming 802.11ax technologies that improves spectral efficiency by allowing concurrent communication between one Access Point and multiple users. In this paper, we present MuVIS, a novel framework that proposes MU-MIMO-aware optimization for multi-user multimedia applications over IEEE 802.11ac/ax. Taking a cross-layer approach, MuVIS first optimizes the MU-MIMO user group selection for the users with the same characteristics in the PHY/MAC layer. It then optimizes the video bitrate for each group accordingly. We present our design and its evaluation on smartphones and laptops over 802.11ac WiFi.

As the demands for uplink traffic increase, improving the uplink throughput has attracted research attention in IEEE 802.11 networks. To avoid excessive competition among stations and enhance the uplink throughput performance, the IEEE 802.11ax standard supports uplink multi-user transmission scenarios, in which AP triggers certain stations in a network to transmit uplink data simultaneously. The performance of uplink multi-user transmissions highly depends on the scheduler, and station scheduling is still an open research area in IEEE-802.11ax-based networks. In this paper, we propose a transmission delay-based uplink multi-user scheduling method. The proposed method consists of two steps. In the first step, the proposed method makcreateses station clusters so that stations in each cluster have similar expected transmission delays. The transmission delay-based station clustering increases the ues of uplink data channels during the uplink multi-user transmission scenario specified in IEEE 802.11ax. In the second step, the proposed method selects cluster for uplink multi-user transmissions. The cluster selection can be performed with a proportional fair-based approach. With the highly channel-efficient station cluster, the proposed scheduling method increases network throughput performance. Through the IEEE 802.11ax standard compliant simulations, we verify the network throughput performance of the proposed uplink scheduling method.

In this paper, we propose an energy-efficient nonorthogonal multiple access (NOMA) technique for multi-user visible light communication (VLC) systems, by adopting a low-complexity quality of service (QoS)-guaranteed power allocation strategy. From the perspective of energy saving, we design the energy-efficient NOMA-enabled multi-user VLC system with the goal to achieve maximal energy efficiency (EE). The closed-form QoS-guaranteed optimal power allocation strategy is obtained and the analytical EE of the proposed NOMA-enabled multiuser VLC system is derived. The analytical and simulation results show that, for a VLC system with ten users, the average EE can be improved by 19% when adopting the proposed NOMA in comparison to NOMA with gain ratio power allocation (GRPA), and the EE improvement becomes much more significant when the users have more diverse QoS requirements.

With the increasing integration of humans and the cyber world, user authentication becomes critical to support various emerging application scenarios requiring security guarantees. Existing works utilize Channel State Information (CSI) of WiFi signals to capture single human activities for non-intrusive and device-free user authentication, but multi-user authentication remains a challenging task. In this paper, we present a multi-user authentication system, MultiAuth, which can authenticate multiple users with a single commodity WiFi device. The key idea is to profile multipath components of WiFi signals induced by multiple users, and construct individual CSI from the multipath components to solely characterize each user for user authentication. Specifically, we propose a MUltipath Time-of-Arrival measurement algorithm (MUTA) to profile multipath components of WiFi signals in high resolution. Then, after aggregating and separating the multipath components related to users, MultiAuth constructs individual CSI based on the multipath components to solely characterize each user. To identify users, MultiAuth further extracts user behavior profiles based on the individual CSI of each user through time-frequency analysis, and leverages a dual-task neural network for robust user authentication. Extensive experiments involving 3 simultaneously present users demonstrate that MultiAuth is accurate and reliable for multi-user authentication with 87.6% average accuracy and 8.8% average false accept rate.

Fair power allocation to the users in a power domain nonorthogonal multiple access (PD-NOMA) networks is an essential aspect. In this paper, we have proposed a coefficient-scaling-based fair power allocation scheme, for a single cell multi-user PD-NOMA network. First, we have developed a model for two users with a fixed power allocation scheme, and proposed a modified strong user fair-power allocation scheme. Furthermore, this two-user scheme is extended for an ad-hoc multi-user PD-NOMA network and a coefficient-scaling-based approach for fair power allocation is proposed to address the outage issue. The performance of the proposed schemes is evaluated through bit-error-rate, sum-rate, outage, and fairness index. The simulation results reveal a remarkable increase in fairness performance of the proposed scheme, in comparison with a state-of-the-art method.

Beyond 5G (B5G) and future 6G systems are expected to serve a massive number of interconnection links between base stations and Internet of Thing (IoT) devices. Thus, it is critical to develop new effective multiple access techniques, which can serve the demands of this massive number of connections. In this regard, power domain non-orthogonal multiple access (PD-NOMA), which was studied extensively by both academia and industry, was perceived as a potential candidate to the problem of serving more devices while having limited resources; however, the PD-NOMA design was eliminated from the list of the 17th release of 3GPP work items. Even though PD-NOMA proves efficient compared to OMA systems in certain scenarios and under specific conditions, it suffers from many issues including: low reliability due to having inter-user-interference, security vulnerability to both internal and external eavesdropping, the complexity of the transceiver due to the use of successive interference cancellation (SIC), and the inapplicability in power-balanced scenarios, where the superimposed users have the same distance from the base station. To address the aforementioned problems related to PD-NOMA, in this work, we propose and develop a novel new alternative non-orthogonal transmission design through the use of specially designed auxiliary signals superimposed with the users’ data. The proposed design is termed and coined as MU-AS-ST (Multi-User Auxiliary Signal Superposition Transmission) and is featured with a very simple transceiver design, where all the processing is done at the base station, thus freeing the receiver from any complex processing. The offered advantages by the proposed design make from it an ideal candidate for low-power and processing-limited devices such as IoT applications.

With the large-scale mobile Internet promotion, people frequently use social applications, such as Facebook, Instagram, etc., making the social network a valuable research subject in Device-to-Device (D2D) communication. Maintaining high throughput and quality of service (QoS) are vital metrics worthy of exploration. This paper proposes a social-relay ad hoc network model based on multi-user cooperative mobility, consisting of two issues: relay selection scheme and multi– user cooperative mobility strategy. To ensure link stability in the relay selection scheme, we jointly consider social links’ requirements and the probability of successful data transmission in the physical layer. Since the optimization problem is NP– hard, with game theory, we define two strategic games that include relay selection game (RSG) and multi-user mobility game (MMG), to obtain the optimal relay and the best mobility strategy for maximizing throughput. The numerical results show that the proposed potential-game approach yields 13.93%, 81.78%, and 240.83% throughput gain over the previous mid-point selection scheme, random relaying and mobility, and direct transmission, respectively.

This paper investigates the ultimate performance limits of distributed multi-server systems with cache-aided users, where the users aim to retrieve a linear function of the files of a library that are replicated at multiple non-colluding servers. In addition to correct decoding, the following conditions are imposed: (a) the content of the library must be kept secure from a wiretapper who obtains all the signals sent by the servers; (b) any subset of users together can not obtain any information about the demands of the remaining users; and (c) the users’ demands must be kept private against any individual server. A Distributed Key Superposition (DKS) scheme is proposed, which uses the idea of superposition of security and privacy keys to guarantee conditions (a) and (b) simultaneously, as in the single server setup. Condition (c) is guaranteed by the fact that each server is responsible for delivering a fraction of the requested linear function, and insuring that the privacy keys used by a server are generated and pushed to the user caches by another server. Interestingly, the achievable load-memory tradeoff with the additional constraint (c) is the same as the single server case if there are at least two servers.

Institute of Electrical and Electronics Engineers (IEEE) 802.11ax, as the next generation wireless local area networks (WLANs) standard, which introduces many new media access control (MAC) technologies, can increase the access efficiency greatly. Unlike traditional IEEE 802.11 protocol using random access mode, the draft of IEEE 802.11ax introduced an innovative hybrid uplink access mechanism including scheduled access and random access modes. Because of the essential difference between the two access modes, the traditional utility based resource optimization algorithms for scheduled access are no longer suitable for the next generation WLANs with two access modes. In this paper, due to the coexistence of two access modes in 802.11ax, a utility function for resource optimization is defined firstly. The function depends on the network services and the capacity entropy multi-user access (CEM). CEM is used to measure the carrying capacity of the networks with both scheduled and random access modes. Secondly, a utility maximization based on resource transfer (UMRT) algorithm is proposed for two modes to maximize the overall satisfaction of all users in the networks. Thirdly, through mathematical analysis, the resource allocation ratio between the scheduled access and random access part is obtained. Then, in order to reduce the computational complexity of our UMRT algorithm, a fast utility maximization based on resource transfer (F-UMRT) algorithm is proposed for industrial applications. Finally, the effectiveness of proposed algorithms in this paper are verified by simulation. With proposed algorithms in this paper, the quality of service (QoS) can be improved for the next generation WLANs.

In the past few years, there has been a proliferation in commercial wireless services such as mobile, Wi-Fi, TV, etc. This has raised a demand for radio spectrum. As per the reports of spectrum occ...

The latest standard of IEEE 802.11 WLANs embraces multi-user (MU) transmission via orthogonal frequency-division multiple access (OFDMA), multi-user multiple-input and multiple-output (MU-MIMO), or a mixture of both. However, due to the frame-basis transmission of WLANs, all concurrent user frames must have an identical transmission duration so as to fit in a common MU frame, which requires shorter frames padded with dummy bits, thus reducing transmission efficiency. Since stations are likely to have heterogeneous traffic demands and transmission bit rates in real networks, such inefficiency arises as a practical problem. To address this challenge, first we analyze how different overhead components impact the efficiency of MU transmission. Then, we propose a scheme to construct an MU frame with an optimal length maximizing its transmission efficiency, which provides a unified framework for OFDMA and MU-MIMO in both downlink and uplink. Through simulation, we demonstrate that the proposed scheme integrated with various scheduling algorithms reduces transmission delay and enhances traffic delivery ratio considerably compared to basic approaches.