We address comprehensive optimization of uplink spectral efficiency (SE) and resource allocation in multiple reconfigurable intelligent surfaces (RIS)-aided cell-free massive MIMO (CF-mMIMO) systems. While integrating CF-mMIMO with RISs enhances SE, existing solutions assume ideal conditions or separately optimize access point (AP) clustering, large-scale fading decoding (LSFD), RIS phase-shifts, and power allocation. To bridge these gaps, we propose a unified statistical channel state information (CSI)-based optimization (SCOP) framework that jointly optimizes AP clustering, LSFD, RIS phase-shift control, and uplink power allocation to maximize the minimum uplink SE. A closed-form SE expression is derived for maximum ratio (MR) combining, accounting for both direct and cascaded channels with spatially correlated Ricean fading. Leveraging statistical CSI significantly reduces real-time acquisition overhead while enabling robust and efficient uplink transmission design. SCOP is solved via a multi-step strategy: (i) for fixed power, an iterative algorithm computes joint optimization parameters (JOP) vector representing a combination of AP clustering, LSFD, and RIS phase-shift parameters; (ii) a closed-form solution updates power allocation; and (iii) an alternating optimization jointly refines both. We also introduce a novel method to extract the optimal system parameters from the JOP. Simulation results show that, in a representative 60 APs, 30 users, and 4 RISs scenario, the proposed SCOP framework lifts the median uplink SE from 2.4 bit/s/Hz to 3.9 bit/s/Hz (+65%) and more than doubles the bottom 5% rate, with similar 60–120% gains in other setups.

This paper proposes a hybrid user group (HUG) scheme for simultaneous transmitting and reflecting-reconfigurable intelligent surface (STAR-RIS)-aided millimeter wave (mmWave) cell-free massive multiple-input multiple-output (CF mMIMO) systems using rate-splitting multiple access (RSMA), where users in transmission and reflection zones are optimally paired. Nearby APs serve users, and multiple STAR-RISs enhance signals under imperfect SIC. We formulate a max-min spectral efficiency (SE) problem to jointly optimize power allocation, STAR-RIS phase shifts, and user grouping, leading to a mixed-integer non-convex problem. To solve it, we relax discrete variables and decompose the problem into sub-problems, using bisection search for phase shifts and a low-complexity iterative algorithm for power allocation. Simulations show the HUG scheme improves average SE by 14.03%, 18.79%, and 33.42% compared to random grouping, conventional beamforming, and HUG with conventional RIS, respectively.

In this paper, we propose a novel energy-efficient multicast routing protocol using federated learning (FL)-based optimal route selection (FLEMR) in internet of things (IoT)-enabled mobile ad hoc networks (MANETs) with reconfigurable intelligent surfaces (RIS) and cell-free massive MIMO (CF-mMIMO). The proposed FLEMR protocol integrates cross-layer design and federated learning (FL) to improve the network and physical layers performance. Specifically, the cross-layer design combines information from the physical layer, such as mobility (speed and direction), position, remaining energy, and spectral efficiency, with information from the network layer (hop count) to maximize a cost function for optimal route selection. RISs are deployed to improve the strength of the received signals, thus enhancing overall connectivity. To further enhance energy efficiency during data transmission, we design an adaptive transmit power allocation technique that dynamically divides the transmission area into regions and zones based on receiver positions. Furthermore, we design the FL framework to solve two problems: infer the optimal weight values of the cost function to select the multicast route and decide the optimal region and zone for adaptive transmit power allocation. The simulation results show that the proposed FLEMR protocol, integrated with the cross-layer federated learning-based clustering (CFLC) protocol under the reference point group mobility (RPGM) model, establishes more robust multicast routes, demonstrating superior performance in terms of connectivity, scalability, and energy efficiency compared to benchmark protocols.

In this paper, we propose a novel federated blockchain (FedChain)-based clustering protocol to enhance network security and connectivity in flying ad hoc networks (FANETs) with cell-free massive MIMO (CF-mMIMO). By leveraging blockchain technology and federated learning (FL), the cluster can be protected against Sybil attacks, enabling secure cluster formation without increasing the number of control packets. We formulate the cost function maximization problem based on cross-layer design, which integrates physical layer information (mobility, position, channel capacity, and remaining energy) and network layer parameters (connectivity) to optimize the formation of stable clusters with minimal control overhead. Furthermore, we select the optimal cluster heads (CHs) based on the highest remaining energy and velocity-constrained criteria, ensuring long-term stability. To solve the security issue, blockchain technology is adopted to validate transactions among nodes and ensure secure formation by distinguishing legitimate users and Sybil attack nodes. Additionally, we develop a novel FL framework to predict and distinguish node status in real time without additional control packets, improving security and control overhead performance during cluster formation. Simulation results demonstrate that the proposed FedChain-based clustering protocol outperforms the lowest ID (LI), high connectivity degree (HCD), and conventional blockchain-based clustering (CBC) protocols in terms of connectivity, control overhead, and security performance. The results highlight that the FedChain-based clustering protocol provides robust security and connectivity, making it well-suited for dynamic FANET environments.

In this article, we propose novel federated learning (FL) and blockchain-based secure multicast routing (FBSMR) protocol in flying ad hoc networks (FANETs) with cell-free massive MIMO (CF-mMIMO) and simultaneously transmitting and reflecting-reconfigurable intelligent surface (STAR-RIS) effectively avoiding collaborative attacks. The proposed FBSMR protocol integrates FL with blockchain to enhance security and prevent collaborative attacks during the routing process. Besides, by utilizing a cross-layer design, the proposed FBSMR can enhance network security and Quality-of-Service (QoS) performance. Specifically, we implement a blockchain-based approach to support secure multicast routing, which efficiently detects and isolates malicious nodes. By using these techniques, all participating nodes achieve consensus on the validity of routing paths, thereby significantly enhancing overall network security. Besides, we address the cost-minimization problem in the proposed cross-layer design by optimizing the weight values of physical layer information, data link layer information, and network layer information subject to the minimum sequence numbers, maximum end-to-end delay, and hop count constraints. To further enhance the coverage area, improve receive signal quality, and reduce the number of hops, we leverage the capabilities of STAR-RIS technology attached to the AAV (F-STAR-RIS) to refract and reflect incident waves toward desired positions, enabling significant improvements in signal quality and transmission coverage. Additionally, the FL framework is employed for real-time prediction of the secure next node, utilizing local data from each flying access point (F-AP) to predict the optimal next node, STAR-RIS configuration, and phase shift at the STAR-RIS. Simulation results demonstrate that the proposed FBSMR protocol, combined with the FedChain-based clustering protocol, establishes a more secure route against collaborative attacks and outperforms benchmark protocols in terms of connectivity, stability, and security performance.

This paper proposes an adaptive user grouping (AUG) scheme for short-packet communication (SPC) in simultaneous transmitting and reflecting (STAR)-reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) systems with SWIPT. The information users with different channel conditions are optimally grouped while the energy user harvests the energy from the base station. Besides that, multiple STAR-RISs are deployed to assist the information users in improving the quality of received signals. We formulate the spectral efficiency (SE) maximization of the considered system to optimize the linear precoding matrix, phase shift of the reflection and transmission at STAR-RIS, energy beamforming matrix, and grouping variables. The formulated problem leads to a mixed binary integer programming which is challenging to solve optimally. To tackle this problem, we first relax the integer variable to be continuous and decouple the relaxed problem into two subproblems to alternately tackle the phase shift and beamforming parts. We then propose bisection search and low-complexity iterative algorithms to solve the phase shift and beamforming subproblems with guaranteed convergence at a relative optimum of each subproblem. Towards real-time optimization, we develop a convolutional neural network (CNN) to achieve the optimal solution of the relaxed problem via a quick-inference process. Numerical results demonstrate a SE improvement of 46% in the AUG scheme over the random user grouping one and 78% over the non-user grouping under various settings. Furthermore, the developed CNN model predicts optimal phase shift variables and beamforming matrices with high accuracy compared to conventional methods but in a shorter time.

This article proposes a novel QoS multicast routing (QSMR) protocol that leverages cross-layer design for IoT-enabled mobile ad hoc networks (MANETs) within a reconfigurable intelligent surface (RIS)-aided cell-free massive MIMO (CF-mMIMO) environment, especially when multiple active eavesdroppers (Eves) are present. The proposed QSMR protocol employs a cross-layer approach to establish a QoS multicast mesh (QMM) route, considering spectrum efficiency (SE), achievable secrecy rate (ASR), end-to-end (E2E) delay, route stability, and hop count requirements. Initially, we formulate the SE expressions for each IoT device in both uplink and downlink, and the downlink ASR expressions for users under attack. Second, we propose two optimization problems: 1) the phase-shift control (PSC) problem, aimed at maximizing the minimum uplink SE and 2) the power control (PC) problem, focused on maximizing the minimum ASR. Additionally, a deep neural network is designed to reduce the computation time required for solving the PSC and PC problems. Third, leveraging the cross-layer synergy, we propose the QSMR protocol to establish the QMM route from a source to multiple destinations by seamlessly integrating information from both the physical and network layers. Finally, the simulation results evaluate the secure performance and provide comparisons for different scenarios. Moreover, in the presence of Eves, the proposed QSMR protocol outperforms other routing protocols in terms of routing delay, control overhead, and packet delivery ratio.

In this paper, we propose an adaptive user pairing (AUP) scheme in multi-intelligent reflecting surface (IRS)-aided massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) networks. In the AUP scheme, two users with different channel conditions are selected for user pairing while multiple IRSs assist to improve received signal quality at users. We consider the problem of jointly optimizing the precoding matrix, the phase shift of IRSs, and the user pairing element to maximize the overall spectral efficiency (SE) subject to the maximum power budget at the base station (BS) and user-specific quality-of-service (QoS). The SE problem formulated as the maximization of non-concave functions involves a mixed-integer program, which is very challenging to solve optimally. To tackle this problem, we first relax the user pairing elements to be continuous and then transform the formulated problem into an equivalent non-convex problem with a more tractable form. We then apply the iterative algorithm (IA) with low complexity to guarantee convergence at a relative optimum. Towards real-time optimization, we propose a deep learning (DL) framework to predict the optimal solution of the precoding matrix, the phase shift of IRSs, and user pairing elements according to the user’s locations and channel gains. Compared to the conventional optimization method, the DL-based optimization framework can achieve the optimal solution within a very short time via an efficient inference process. Numerical results verify that the proposed algorithm improves the SE over state-of-the-art approaches. Moreover, the effects of essential parameters such as the total BS transmit power, the number of UEs, IRSs, and BS’s antennas on the system are discussed and evaluated to show the effectiveness of the proposed scheme in balancing resource utilization.

In this paper, we study short-packet communications in multi-hop networks with wireless energy transfer, where relay nodes harvest energy from power beacons to transmit short packets to multiple destinations. It is proposed a novel cooperative beamforming relay selection (CRS) scheme which incorporates partial relay selection and distributed multiuser beamforming to achieve a high-reliable transmission in two consecutive hops. A closed-form expression for the average block error rate (BLER) of the CRS scheme is derived, based on which an asymptotic analysis is also carried out. To achieve optimal channel uses allocation, we formulate a fairness end-to-end throughput maximization problem which is generally NP-hard due to the non-concavity of the objective function and mixed-integer constraints. To solve this challenging problem efficiently, we first relax channel uses to be continuous and transform the relaxed problem into an equivalent non-convex one, but with a more tractable form. We then develop a low-complexity iterative algorithm relying on inner approximation framework to convexify non-convex parts that converges to at least a locally optimal solution. Towards real-time settings, we design an efficient deep convolutional neural network (CNN) with multiscale-accumulation connections to achieve the sub-optimal solution of the relaxed problem via real-time inference processes. Numerical results are presented to verify the analytical derivations and to demonstrate performance improvements of the CRS scheme over the benchmark ones in terms of BLER, reliability, latency, and throughput in various settings. Moreover, the designed CNN provides the lowest root-mean-square error compared to the state-of-the-art deep learning approaches while the CNN-aided optimization framework estimates accurately the optimal channel uses allocation with low execution time.

In this paper, we design an efficient deep convolutional neural network (CNN) to improve and predict the performance of energy harvesting (EH) short-packet communications in multi-hop cognitive Internet-of-Things (IoT) networks. Specifically, we propose a Sum-EH scheme that allows IoT nodes to harvest energy from either a power beacon or primary transmitters to improve not only packet transmissions but also energy harvesting capabilities. We then build a novel deep CNN framework with feature enhancement-collection blocks based on the proposed Sum-EH scheme to simultaneously estimate the block error rate (BLER) and throughput with high accuracy and low execution time. Simulation results show that the proposed CNN framework achieves almost exactly the BLER and throughput of Sum-EH one, while it considerably reduces computational complexity, suggesting a real-time setting for IoT systems under complex scenarios. Moreover, the designed CNN model achieves the root-mean-square-error (RMSE) of 1.33 × 10-2on the considered dataset, which exhibits the lowest RMSE compared to the deep neural network and state-of-the-art machine learning approaches.

In this paper, we study short-packet communications (SPCs) in multi-hop wireless-powered Internet-of-Things networks (WPINs), where IoT devices transmit short packets to multiple destination nodes by harvesting energy from multiple power beacons. To improve system block error rate (BLER) and throughput, we propose a best relay-best user (bR-bU) selection scheme with an accumulated energy harvesting mechanism. Closed-form expressions for the BLER and throughput of the proposed scheme over Rayleigh fading channels are derived and the respective asymptotic analysis is also carried out. To support real-time settings, we design a deep neural network (DNN) framework to predict the system throughput under different channel settings. Numerical results demonstrate that the proposed bR-bU selection scheme outperforms several baseline ones in terms of the BLER and throughput, showing to be an efficient strategy for multi-hop SPCs. The resulting DNN can estimate accurately the throughput with low execution time. The effects of message size on reliability and latency are also evaluated and discussed.

In this paper, we propose an efficient deep convolutional neural network-based relay selection (CNS) scheme to evaluate and improve the end-to-end throughput in energy harvesting full-duplex Internet-of-Things (IoT) networks. In this system, multiple full-duplex relays harvest energy from a power beacon to assist data transmission from a source node to multiple users under short packet communications. We propose a best relay best user (bR-bU) selection scheme to improve the diversity packet transmission. We then develop a deep convolutional neural network framework for relay selection and throughput prediction with high accuracy and low execution time. Simulation results show that the proposed CNS scheme achieves almost exactly the throughput of bR-bU one, while it considerably reduces computational complexity, suggesting a real-time configuration for IoT systems under complex scenarios. Moreover, the designed CNN model achieves the root-mean-square-error (RMSE) of 8.4 × 10−3on the considered dataset, which exhibits the lowest RMSE as compared to the deep neural network and state-of-the-art machine learning approaches.

In this article, we propose an efficient deep-neural-network-based relay selection (DNS) scheme to evaluate and improve the end-to-end throughput in wireless-powered cognitive Internet-of-Things (IoT) networks. In this system, multiple energy harvesting (EH) relays are deployed randomly to assist data transmission from a source node to multiple users under practical nonlinearity of the EH circuits. We first design an incremental relaying protocol, where a selected user will request the help from relays if the direct transmission is not favorable. In such a protocol, we develop a deep neural network framework for relay selection and throughput prediction with high accuracy, less channel feedback amount, and short execution time. Simulation results show that the proposed DNS scheme achieves higher throughput than the conventional relay selection methods, while it considerably reduces computational complexity, suggesting a real-time configuration for IoT systems under complex scenarios. Moreover, the proposed DNS scheme achieves the root-mean-square error (RMSE) of 6.6×10-3on the considered dataset, which exhibits the lowest RMSE as compared to the state-of-the-art machine learning approaches.

In this article, we study the physical-layer security (PHY-security) improvement method for a downlink nonorthogonal multiple access (NOMA) system in the presence of an active eavesdropper. To this end, we propose a full-duplex (FD)-enabled NOMA system and a promising scheme, called the minimal transmitter selection (MTS) scheme, to support secure transmission. Specifically, the cell-center and cell-edge users act simultaneously as both receivers and jammers to degrade the eavesdropper channel condition. Additionally, the proposed MTS scheme opportunistically selects the transmitter to minimize the maximum eavesdropper channel capacity. To estimate the secrecy performance of the proposed methods, we derive an approximated closed-form expression for secrecy outage probability (SOP) and build a deep neural network (DNN) model for SOP evaluation. Numerical results reveal that the proposed NOMA system and MTS scheme improve not only the SOP but also the secrecy sum throughput. Furthermore, the estimated SOP through the DNN model is shown to be tightly close to other approaches, i.e., the Monte-Carlo method and analytical expressions. The advantages and drawbacks of the proposed transmitter selection scheme are highlighted, along with insightful discussions.

In this paper, we propose a novel hybrid user pairing (HUP) scheme in multiuser multiple-input single-output non-orthogonal multiple access networks with simultaneous wireless information and power transfer. In this system, two information users with distinct channel conditions are optimally paired while energy users perform energy harvesting (EH) under non-linearity of the EH circuits. We consider the problem of jointly optimizing user pairing and power allocation to maximize the overall spectral efficiency (SE) and energy efficiency (EE) subject to user-specific quality-of-service and harvested power requirements. A new paradigm for the EE-EH trade-off is then proposed to achieve a good balance of network power consumption. Such design problems are formulated as the maximization of non-concave functions subject to the class of mixed-integer non-convex constraints, which are very challenging to solve optimally. To address these challenges, we first relax binary pairing variables to be continuous and transform the design problems into equivalent non-convex ones, but with more tractable forms. We then develop low-complexity iterative algorithms to improve the objectives and converge to a local optimum by means of the inner approximation framework. Simulation results show the convergence of proposed algorithms and the SE and EE improvements of the proposed HUP scheme over state-of-the-art designs. In addition, the effects of key parameters such as the number of antennas and dynamic power at the BS, target data rates, and energy threshold, on the system performance are evaluated to show the effectiveness of the proposed schemes in balancing resource utilization.

This paper studies the performance of multi-hop cognitive wireless powered communication networks (WPCNs), where secondary nodes can harvest energy from multiple dedicated power beacons and access spectrum of primary receivers (PRs) to support data transmission. Specifically, we consider a practical scenario of cognitive WPCNs, where the channel state information (CSI) of interference links is assumed to be imperfect. To improve the network performance, we propose dual-hop scheduling scheme (DHS) for multi-hop network, where two best relays in two consecutive clusters are selected for data transmission. We then analyze the performance of the proposed scheme in terms of outage probability and outage floor. Numerical results show that DHS scheme outperforms random scheduling scheme, arising as an efficient scheme for multi-hop transmission. Furthermore, the effects of the number of hops, number of power beacons, and time switching ratio on multi-hop cognitive WPCNs are evaluated and discussed.

In this paper, we study the problem of jointly optimizing user pairing and beamforming design in multiuser multiple-input single-output (MU-MISO) non-orthogonal multiple access (NOMA) downlink systems with simultaneous wireless information and power transfer (SWIPT). Aiming at maximizing the achievable sum throughput subject to energy harvesting (EH) constraints, we propose a hybrid user pairing beamforming scheme (HBS), where two users with distinct channel conditions are optimally selected to perform user pairing. Moreover, we adopt a non-linear EH model for energy users to reveal their practical circuit characteristics. The sum throughput problem is formulated as a class of mixed-integer nonconvex optimization programming which is computationally prohibitive. To solve this challenging problem, we propose a low-complexity iterative algorithm, yet efficient, based on sequential convex approximation method to arrive at least the local optima. Numerical results are provided to demonstrate the performance improvement of the proposed HBS scheme over the multiuser beamforming one without user pairing, revealing to be an effective scheme for MU-MISO-NOMA downlink systems.

In this paper, we study the performance improvement methods for a cell-edge user of two-user non-orthogonal multiple access (NOMA) systems in downlink scenarios. To this end, we propose two cooperative relaying schemes, namely ON/OFF-full-duplex relaying (ON/OFF-FDR) and ON/OFF-half-duplex relaying (ON/OFF-HDR) schemes. More specifically, in order to improve the performance of the cell-edge user, we consider a cell-center user as a relay, where either FDR or HDR can be employed to assist the direct NOMA transmission from a base station (BS) to the cell-edge user. An ON/OFF mechanism is proposed to decide whether the cooperative relaying transmission is necessary or not. The ON/OFF relaying decision is made based on the quality of the direct and relaying links from the BS to the cell-edge user. The performance of the two proposed schemes is investigated in terms of outage probability and sum throughput. Numerical results reveal that the proposed schemes not only provide essential outage performance improvements for the cell-edge user, but also are able to improve the sum throughput of the two-user NOMA systems. The advantages and drawbacks of each proposed scheme are highlighted and insightful discussions are provided.

In this paper, we address the opportunistic scheduling in multitwo user NOMA system consisting of one base station, multinear user, multifar user, and one eavesdropper. To improve the secrecy performance, we propose the users selection scheme, called best‐secure‐near‐user best‐secure‐far‐user (BSNBSF) scheme. The BSNBSF scheme aims to select the best near‐far user pair, whose data transmission is the most robust against the overhearing of an eavesdropper. In order to facilitate the performance analysis of the BSNBSF scheme in terms of secrecy outage performance, we derive the exact closed‐form expression for secrecy outage probability (SOP) of the selected near user and the tight approximated closed‐form expression for SOP of the selected far user, respectively. Additionally, we propose the descent‐based search method to find the optimal values of the power allocation coefficients that can minimize the total secrecy outage probability (TSOP). The developed analyses are corroborated through Monte Carlo simulation. Comparisons with the random‐near‐user random‐far‐user (RNRF) scheme are performed and show that the proposed scheme significantly improves the secrecy performance.

In this paper, we study performance and reliability improvement for a cell-edge user in downlink scenarios of two-user non-orthogonal multiple access (NOMA) networks. To this end, we propose a full-duplex (FD) cooperative scheme, in which a near user acts as a FD relay to forward source's signals to a far user, while the far user employs distributed switch-and-stay combining (DSSC) technique to process the incoming signals. We then investigate the performance of the far user in terms of outage probability (OP). In particular, we obtain a closed-form expression for the OP of the far user as well as its asymptotic OP. The developed analysis is corroborated through Monte-Carlo simulation. Numerical results reveal that the proposed scheme achieves better outage performance in comparison with conventional NOMA systems. It is also showed that the choice of the switching threshold used in DSSC technique and/or the target data rate of the system sensitively affects the outage performance of the proposed scheme.

In this paper, we investigate outage performance and diversity gain of transmit antenna selection (TAS) schemes in two-user multiple-input single-output non-orthogonal multiple access (MISO-NOMA) cooperative downlink transmissions. To this end, two TAS criteria, namely Criterion I and Criterion II, are proposed, which select an antenna that experiences the best fading condition of the channel from the source to the far user and to the near user, respectively. Additionally, considering the near user as a relay to help improve the reliability of the far user, hybrid simultaneous wireless information and power transfer (SWIPT) architecture is adopted to power the near user's relaying operation. Tight closed-form approximate expressions for the outage probability (OP) of both users are derived. Numerical results reveal that Criterion I and II achieve, respectively, the diversity order of K + 1 and 2 at the far user, and 1 and K at the near user, where K denotes the number of transmit antennas at the base station.

In this paper, we analyze the downlink outage performance of opportunistic scheduling in dual-hop cooperative networks consisting of one source, multiple radio-frequency energy harvesting relays, and multiple destinations. To this end, two low-complexity, suboptimal, yet efficient, relay-destination selection schemes are proposed, namely direct links plus opportunistic channel state information (CSI)-based selection (DOS) scheme and direct links plus partial CSI-based selection (DPS) scheme. Considering three relaying strategies, i.e., decode-and-forward (DF), variable-gain amplify-and-forward (VG-AF), and fixed-gain amplify-and-forward (FG-AF), the performance analysis in terms of outage probability (OP) is carried out for each selection scheme. For the DF and VG-AF strategies, exact analytical expressions and tight closed-form approximate expressions for the OP are derived. For the FG-AF strategy, an exact closed-form expression for the OP is provided. Additionally, we propose a gradient-based search method to find the optimal values of the power-splitting ratio that minimizes the attained OPs. The developed analysis is corroborated through Monte Carlo simulation. Comparisons with the optimal joint selection scheme are performed and it is shown that the proposed schemes significantly reduce the amount of channel estimations while achieving comparable outage performance. In addition, regardless of relaying strategy used, numerical results show that the DOS scheme achieves full diversity gain, i.e., M + K, and the DPS scheme achieves the diversity gain of M+1, where M and K are the numbers of destinations and relays, respectively.

In this study, the authors analyse the outage performance of multirelay decode‐and‐forward cooperative networks subject to two joint practical issues of wireless communications, namely, energy constraints and transceiver hardware impairment (HI). To deal with energy constraints at relay nodes, radio‐frequency (RF) energy harvesting (EH) technique is adopted. Considering the joint impacts of RF EH technique and HI, two relay selection schemes, namely, harvested energy‐based relay selection (HEbS) scheme and channel quality‐base relay selection (CQbS) scheme are proposed. Tight closed‐form approximate expressions for the outage probability of each scheme are derived and corroborated through Monte Carlo simulations. Some representative performance comparisons are carried out and show that the diversity order achieved by the HEbS scheme is 1 and is independent of the number of relays, K , whereas that achieved by the CQbS scheme is K , which is full diversity.

Summary In this paper, we propose a Stability‐aware Cooperative Routing scheme for Reliable high‐speed data transmission in Multi‐rate mobile ad hoc wireless networks, called SCRRM, to provide high data transmission with stable and reliable routes. The main features and contributions of our proposed routing scheme are as follows: First, we use the cross‐layer concept with network layer, media access control (MAC) layer, and physical (PHY) layer. Second, a stable routing path based on link lifetime calculated from node mobility information is selected as the main routing path. Third, we use the received signal strength indicator (RSSI), PHY delay, and MAC delay for adaptively choosing relay and appropriate data rate. Fourth, we derive mathematical models to investigate the tradeoff between point‐to‐point transmission rates and the corresponding effective transmission ranges. The performance evaluation through analysis and simulation demonstrates that our proposed routing scheme can adaptively select optimal data rate and outperforms single‐rate routing protocol in terms of packet delivery ratio, network throughput, and average end‐to‐end delay in all settings of node density and node mobility. Copyright © 2014 John Wiley & Sons, Ltd.

The number of hops between source node and destination node is one of key parameter in studying multi-hop ad-hoc networks. Although hop count in conventional ad-hoc networks has been well studied, to the best of our knowledge, there have no works that intensively investigate the hop count of multi-hop path in cognitive environment. This paper presents detail simulation analysis on the hop count of multi-hop path in Cognitive Radio Ad-hoc Networks (CRAHNs). The effect of network parameters such as node density of Secondary User (SU) and Primary User (PU), operating frequencies, average activating rate of PUs, in the networks are studied. Simulation experiments with different network parameters are conducted to clarify the features of hop count in cognitive ad-hoc networks.

Abstract The field of mobile ad hoc networking has enjoyed dramatic increase in popularity over the last few years. However, owing to the fact that such networks have dynamic, sometimes rapidly changing, random, multihop topologies, the development of efficient and applicable multicast routing protocols presents many issues and challenges. In this paper we propose a Mobility‐based Hybrid Multicast Routing (MHMR) protocol suitable for mobile ad hoc networks. The main features that our proposed protocol introduces are the following: (i) mobility‐based clustering and group‐based hierarchical structure, in order to effectively support stability and scalability; (ii) group‐based (limited) mesh structure and forwarding tree concepts, in order to support the robustness of the mesh topologies, which provides ‘limited’ redundancy and the efficiency of tree forwarding simultaneously; and (iii) combination of proactive and reactive concepts that provide low route acquisition delay and low overhead. The use of dynamic mobility‐based clustering as the underlying structure is motivated by the observation that in mobile ad hoc networks communications are often among teams that tend to coordinate their movements, and as a result we can dynamically and adaptively partition the network into several groups, each with its own mobility characteristics and behaviors. In our protocol we support the creation of a limited mesh structure based on the clusterheads of the various created clusters, and not among all the members that participate in the multicast session and route, thereby reducing significantly the complexity of the created mesh topology. The performance evaluation of the proposed protocol is achieved via modeling and simulation. The corresponding results demonstrate the proposed multicast protocol's efficiency in terms of packet delivery ratio, scalability, control overhead, end‐to‐end delay as a function of mobility, packet transmission rate, and multicast group size. Copyright © 2003 John Wiley & Sons, Ltd.

We present a printed planar antenna suitable for mobile wireless communications. Since the printed antenna is easy to fabricate due to its simplicity, low cost, and light weight, it is widely used in communication systems. The conventional patch antennas take too much surface area to be applied to a mobile receiver. Although the size is reduced using the printed antenna, a reasonably wide bandwidth should still be considered. To overcome the disadvantage of narrow bandwidth, the substrate should be physically thick and the dielectric constant should be smaller. We suggest a simple form of printed planar antenna and show the optimal input impedance depending on the antenna size and operating frequency. The performance evaluation is achieved analytically for a prototype antenna model.

Abstract The basic philosophy of personal communication services is to provide user‐to‐user, location independent communication services. The emerging group communication wireless applications, such as multipoint data dissemination and multiparty conferencing tools have made the design and development of efficient multicast techniques in mobile ad‐hoc networking environments a necessity and not just a desire. Multicast protocols in mobile ad‐hoc networks have been an area of active research for the past couple of years. This paper summarizes the activities and recent advances in this work‐in‐progress area by identifying the main issues and challenges that multicast protocols are facing in mobile ad‐hoc networking environments, and by surveying several existing multicasting protocols. This article presents a classification of the current multicast protocols, discusses the functionality of the individual existing protocols, and provides a qualitative comparison of their characteristics according to several distinct features and performance parameters. Furthermore, since many of the additional issues and constraints associated with the mobile ad‐hoc networks are due, to a large extent, to the attribute of user mobility, we also present an overview of research and development efforts in the area of group mobility modeling in mobile ad‐hoc networks. Copyright © 2001 John Wiley & Sons, Ltd.