Irregular Repetition Slotted ALOHA with Non-Orthogonal Multiple Access (IRSA-NOMA) suffers from power-level collisions where insufficient received power separation prevents successive interference cancellation (SIC) from resolving colliding users, rendering slots undecodable. This letter proposes Irregular Repetition Virtual Pairing NOMA (IR-VP-NOMA), which applies virtual pairing to resolve these collisions through decoder-side bandwidth partitioning. When received power separation falls below the SIC threshold due to similar channel conditions, the slot bandwidth is divided into orthogonal sub-bands for frequency-domain separation. Simulations demonstrate PLR reduction exceeding 99% at collision-dominated loads and 53.8% throughput improvement, with no additional overhead.

This article proposes two modulation schemes—binary polarized spatial modulation (BPSM) and permutation-based polarized spatial modulation (PPSM)—for multi-input multi-output systems employing polarized antennas. Unlike conventional spatial modulation techniques, which rely on partial antenna activation, the proposed schemes utilize all available transmit antennas with distinct polarization activation patterns to enhance spectral efficiency (SE) while reducing inter-antenna correlation. In BPSM, spatial bits are mapped to binary polarization states using dual-polarized antennas without permutations. PPSM extends this concept by employing permutation-based mapping over multiple polarization states and antennas, thereby allowing richer index information, particularly in systems with more than two polarization states or numerous transmit antennas. To enhance performance, singular value decomposition and particle swarm optimization are employed for optimal power allocation, maximizing channel capacity while adhering to practical constraints. Because of the high complexity of maximum likelihood detection, orthogonal matching pursuit is adopted as a low-complexity sparse recovery algorithm tailored to the inherent sparsity of the transmitted signal structure. The proposed schemes are analytically and numerically evaluated under Rayleigh and Rician fading channels by considering channel estimation errors and correlation effects. The results show that BPSM and PPSM offer favorable trade-offs between the SE and bit error rate, outperforming traditional spatial modulation schemes using both unpolarized and polarized antennas.

This article introduces a massive multiple-input—multiple-output (mMIMO) system that utilizes spectrally efficient frequency division multiplexing (SEFDM) and incorporates a deep neural network (DNN) for enhanced SEFDM channel estimation. Unlike existing studies on DNN-based channel estimation, this research employs estimated channel feedback to dynamically adjust SEFDM signal characteristics at the transmitter, thereby improving the system’s adaptability. This adaptive mechanism optimizes the SEFDM compression value and modulation order based on real-time channel conditions, significantly enhancing the symbol error rate (SER). Detailed simulations demonstrate that higher modulation techniques experience substantial performance degradation with increased subcarrier compression in SEFDM. The proposed DNN-based channel estimation and adaptive parameter selection outperform traditional linear schemes, utilizing a more stable SEFDM system to achieve significant spectral efficiency (SE) compared to conventional orthogonal frequency division multiplexing (OFDM).

In this letter, a novel irregular data compression based on HASH digest (CompHD) is proposed to enhance compression ratio and mitigate computational load at Internet of Things edges (IoT-E). The proposed CompHD utilizes variable length HASH functions, specifically SHAKE-128, to aggregate and compress data via flexible digest bytes. The CompHD is evaluated across three options and compared against established data compression methods–LZMA, BZ2, ZSTD, and DEFLATE-using key performance metrics: compression gain (CG), execution time (ET), and randomness (RD). The results indicate the advantageous influence of the proposed CompHD with 5B while showing 31.18% CG value and 0.98 RD value.

This study proposes a layerwise quantum-based deep reinforcement learning (LQ-DRL) method for optimizing continuous large space and time series problems using deep-layer training. The actions in LQ-DRL are optimized using a layerwise quantum embedding that leverages the advantages of quantum computing to maximize reward and reduce training loss. Moreover, this study employs a local loss to minimize the occurrence of barren plateaus phenomena and further enhance performance. As a particular case, the proposed scheme is employed to jointly optimize: 1) unmanned aerial vehicle (UAV) trajectory planning; 2) user grouping; and 3) power allocation for higher energy efficiency of a UAV as the reward. The combination of these optimized factors is referred to as action space in the presented LQ-DRL. The LQ-DRL is employed to solve the optimization problem due to its nonconvexity, continuous and large action space, and time-series domain. In a practical view, LQ-DRL aims to solve the issue of energy consumption related to limited-battery energy of a UAV base station (BS) while maintaining Quality of Service (QoS) for users, by gaining maximum energy efficiency as the reward. One of real applications, as an example, LQ-DRL can be employed to maximize the energy efficiency of a UAV BS in UAV empowered disaster recovery networks scenario. The quantum circuits of layerwise quantum embedding are presented to show the practical implementation in noisy intermediate-scale quantum computers. Based on the results, LQ-DRL outperformed the classical DRL by achieving higher effective dimension, rewards, and lower learning losses. In addition, better performances were achieved using more layers.

Quantum neural network with parallel training (called PS-QNN) is presented in this study to optimize wireless resource allocation. Instead of sending the whole dataset, each edge only requires to send the statistical parameters of the dataset; hence reducing the dimension of training data. As a particular case, the proposed PS-QNN is utilized to optimize transmit precoding and power allocation in non-orthogonal multiple access with multiple-input and multiple-output antennas (MIMO-NOMA). Compared to the conventional training method, analysis shows that the proposed parallel training yields a lower complexity, while achieving a comparable sum rate compared to conventional method.

This study proposes an intelligent approach to identifying an injured soldier on blockchain-integrated Internet-of-Battlefield Things (IoBT) employing unmanned aerial vehicles (UAVs). The intelligent approach combines a unique deep learning (DL) model with a smartwatch-based heart-rate (HR) data collection technique. Different activation functions (i.e., MISH and Leaky rectified linear unit) are used in the proposed DL model to enhance the identification task by extracting the in-depth features from the images. Furthermore, a smart-watch-based HR data analyzing technique is introduced to confirm the injury of a soldier. However, due to the UAV’s low battery capacity, the identification task is offloaded to the neighboring edge computing server to improve system performance. Moreover, to restrict the access of registered IoT devices (e.g., UAV, smartwatch, etc.) and protect the sensitive data leakage on IoBT, a blockchain-integrated access control (ACL) mechanism is utilized. Detailed experimental results are provided for the proposed DL model that outperforms existing DL models. Besides, implementing a smartwatch-based HR data analysis technique for the soldiers improves the outcome of the proposed DL model. To provide a fine-grained data protection mechanism in the proposed system, a private blockchain-based ACL management policy is constructed utilizing hyperledger, and various assessment metrics have been scrutinized.

This study proposes a double deep reinforcement learning (D-DRL) to improve an index of rate fairness and sum-rate in UxV-enabled multi-user communication systems. In this study, a UxV-assisted multi-user communications scenario is considered. By taking into account the tradeoff between the two objectives and the time-sequential movement of the UxV, two DRL-based actor-critic networks are integrated to solve the designated problem. In the first actor-critic network, the rate fairness is maximized by jointly optimizing a hybrid precoder with a UxV trajectory. Subsequently, considering the rate fairness as a learning reward of the first network, sum-rate is maximized in the second network under the consideration of transmission power budgets, limited UxV battery capacity, and quality of service (QoS) constraints. The results show that the D-DRL which considered rate fairness outperformed DRL which did not by achieving maximum rate fairness and a higher sum-rate.

This article presents a deep learning (DL) model integrated automated and secure garbage management scheme using unmanned any vehicle (UxV) to minimize the human effort in terms of the traditional garbage management system. Different kinds of UxV (unmanned aerial vehicles, automated guided vehicles, unmanned surface vehicles, unmanned underwater vehicles, etc.) are utilized to establish an automated garbage management scheme to collect and place the garbage both from the ground and sea surfaces. However, due to the limited battery capacity and inadequate resources of different UxV, a lightweight DL model is developed to detect the garbage successfully with a higher accuracy rate. The proposed lightweight DL model uses two activation functions named MISH and rectified linear unit to enhance the feature extraction and detect the garbage. Moreover, a multiaccess edge computing (MEC) server is allocated in the proposed scheme to improve the Quality of Service (QoS) (i.e., reduce latency and improve security). Furthermore, a blockchain-based secure hazardous garbage (e.g., infectious, toxic, or radioactive materials) tracking technique is concluded in this scheme to identify the individual and reduce the potential harm to the environment. Experimental results demonstrate that the UxV can successfully detect the garbage using the proposed lightweight DL model within a minimum time frame and the obtained accuracy is higher than the other existing DL models. Besides, QoS has been investigated to verify the efficacy of the proposed scheme. Finally, a private blockchain network is established to demonstrate the performance of the proposed hazardous garbage tracking technique.

This paper presents dual-mode index modulation for spectral efficient frequency division multiplexing (SEFDM-DM). SEFDM is a non-orthogonal multicarrier technique created by compressing the subcarrier spacing of classical orthogonal frequency-division multiplexing (OFDM). SEFDM provides high spectrum efficiency (SE) at the expense of increased inter-carrier interference (ICI). SEFDM with index modulation (SEFDM-IM) has two information-bearing units, that is, a subcarrier activation pattern and modulated symbols, which reduce ICI at the expense of an SE deficit owing to inactive subcarriers. The proposed SEFDM-DM uses all available subcarriers while retaining the diversity gain of index modulation (IM). It enhances SE by transmitting distinct constellation modes through a subcarrier index selection mechanism. This study also presents SEFDM-DM with coordinate interleaving (SEFDM-CDM), which introduces space-time block codes with coordinate interleaving to enhance transmission diversity by sending real and imaginary parts of the constellation symbols over different subcarriers. Analytical and simulation results corroborate the benefits of the suggested work in terms of SE and bit error rate.

This paper presents a joint power and number of intelligent reflecting surfaces (IRS) elements optimization in an IRS-assisted non-orthogonal multiple access (NOMA) network. The proposed optimized IRS NOMA (OIRS-NOMA) solution finds the optimal number of IRS elements and the power allocation factors with log-time complexity. Furthermore, the pair sum rate (PSR) is maximized while satisfying the required individual user rates. In addition, the proposed and the existing IRS-NOMA systems are analyzed under the fixed power allocation (FPA) and optimized power allocation (OPA) schemes. Finally, a comparison of the proposed (OIRS-NOMA), the conventional IRS-NOMA, and NOMA techniques is performed in detail. Simulation results show that the proposed OIRS-NOMA outperforms the existing IRS-NOMA and NOMA techniques in terms of individual user rates and PSR.

Due to the increasing number of unmanned aerial vehicles (UAVs) with generally limited battery power, energy-efficient data transmission schemes with massive connectivity capabilities are required for future wireless networks. Nonorthogonal multiple access (NOMA) is a promising technique that provides such massive connectivity by allowing superposed data transmission of multiple users over the same resource block. This article presents a user pairing and power allocation technique for energy efficiency and Quality-of-Service (QoS)-based NOMA transmission for cellular-connected mobile UAVs and ground users (GUs) in a NOMA UAV network. The aim is to minimize the energy consumption of both aerial users (AUs) and GUs during uplink data transmission and guarantee their required transmission rates through efficient pairing and power optimization. Therefore, a joint pairing and power optimization problem is formulated as a nonconvex optimization problem under QoS constraints. Furthermore, a suboptimal solution is proposed to show that the proposed energy-efficient pairing technique effectively fulfills the QoS requirements of both GUs and AUs in a network with low complexity. The simulation results of the proposed technique show the performance gain in terms of energy efficiency, outage probability, and pairing complexity compared to the conventional channel condition and power-consumption-based NOMA pairing and power allocation techniques.

The sixth-generation cellular networks bring about proactive communications with predictive decision making by incorporating artificial intelligence (AI) and machine learning (ML) in vehicular networks, toward envision of the Internet of Vehicles (IoV). Currently, vehicular communications suffer from unreliable communication links due to multihopad hoccommunications and the high-mobility environment. The available literature falls short in providing a reliable routing scheme that proactively and accurately estimates or predicts connectivity duration between two vehicles. In this study, we highlight the need for communication route compatibility (connectivity duration) as a route selection parameter along with trustworthiness. We propose an ML and analytical compatibility-basedad hocrouting protocol that allows a vehicle to estimate or predict the compatibility time of all candidate routes, to choose the best route. We evaluated one analytical and five ML classification techniques on our OpenStreemMap (OSM) and SUMO mobility trace generated data set (Seoul and Berlin). Our exhaustive simulation demonstrated that our proposed scheme (six variations) dismisses all short-lived routes and achieves 2–3 times higher packet delivery ratio in comparison to the existing hop count-based routing (AOMDV and trust cryptographic secure routing). The proposed scheme disregards paths having few intermediate nodes for long-lasting paths with the expenses of a few extra hops. We also present a comprehensive comparative study to evaluate ML techniques based on the well-known metrics, such as accuracy, time, misclassification,$F1$-score, etc.

This article proposes a multidimensional search space (or directional space) with more degrees-of-freedom (DOFs) to increase the energy efficiency of limited-battery-powered unmanned aerial vehicle (UAV) in the Internet of Things (IoT) data collection scenario. In this article, the UAV navigates from the initial to the goal point while collecting data from IoT sensors on the ground. Owing to the limited battery power of UAVs, an optimized trajectory is a crucial practical problem. Based on the available directional space, the direction of the UAV related to the navigation trajectory is optimized using reinforcement learning (RL). The objective of RL is to maximize the energy efficiency of the UAV as a long-term reward by selecting the optimal direction. Moreover, a practical energy consumption model and environment are presented in this article. Simulation results verified that the proposed multidimensional trajectory for UAV achieves higher energy efficiency compared with benchmark models.

In this paper, a vehicular pairing scheme is proposed to balance the resource distribution in non-orthogonal multiple access (NOMA) based vehicular networks. The proposed BVP: balanced vehicular pairing scheme chooses pairs based on fair inter-vehicle distances through the base station (BS) for mobile NOMA users with high mobility. BVP reduces the biased pairing and the disparity of resource utilization by each pair, resulting in fewer signal decoding errors than the existing state-of-the-art solutions. The exhaustive evaluation in Matlab-based network simulator, using real vehicular traffic traces from OpenStreetMap (OSM) and simulation of urban mobility (SUMO) traffic simulator shows that the proposed BVP outperforms existing schemes. The proposed solution reduces throughput and pair sum rate standard deviation by 2 ~ 3 b/s/Hz for every device and every resource, while also significantly reducing the decoding bit error rate.

This study exploits a quantum neural network (QNN) for resource allocation in wireless communications. A QNN is presented to reduce time complexity while still maintaining performance. Moreover, a reinforcement-learning- inspired QNN (RL-QNN) is presented to improve the perfor- mance. Quantum circuit design of the QNN is presented to ensure the practical implementation in noisy intermediate-scale quantum (NISQ) computers. For the QNN, the complexity and the number of required qubits are analyzed as well. As a particular use case, the QNN is utilized for user grouping in non-orthogonal multiple access. The results reveal that the QNN schemes have lower complexities and similar performance in terms of the achievable sum rate when compared with that of the classical neural network.

In this study, joint transmission coordinated multi‐point (JT‐CoMP) is integrated with virtual user pairing based non‐orthogonal multiple access (VP‐NOMA), termed as JT‐CoMP VP‐NOMA. JT‐CoMP is a multi‐cell coordination scheme, where base stations of neighbouring cells coordinate with each other to improve the performance of their common cell edge users (CEUs). On the other hand, VP‐NOMA is a one‐to‐many user pairing scheme in non‐orthogonal multiple access (NOMA), where one cell centre user (CCU) within a cell can be paired/multiplexed with multiple CEUs for better spectrum utilisation. The proposed scheme combines both VP‐NOMA for enhancing ergodic sum capacity (ESC) within a cell and JT‐CoMP for mitigating inter‐cell interference (ICI) between neighbouring cells. To demonstrate the achieved performance gains, ESC of a three‐cell scenario is analysed as a key performance metric, where each cell contains two users; a CCU and a CEU. Closed‐form expressions for ESC of the considered system are derived, and validated through simulations. Furthermore, the analytical and simulation results of JT‐CoMP VP‐NOMA are compared with orthogonal multiple access, NOMA, and VP‐NOMA to show the achieved performance gains. It is shown that the proposed JT‐CoMP VP‐NOMA outperforms the other schemes in the viewpoint of ESC.

This paper presents an integrating concept of de-noising convolutional neural networks (DnCNN) with quadrature amplitude modulation (QAM) for symbol denoising. DnCNN is used to estimate and denoise the Gaussian noise from the received constellation symbols of QAM with unknown noise level. Proposed system shows a significant gain in terms of peak signal-to-noise ratio, system throughput and bit-error rate; in comparison with conventional QAM systems. The basic concept, system level integration, and simulated performance gains are presented to elucidate the concept.

Importance of bio-inspired algorithms (BIAs) is increasing exponentially because of their capability to optimization different problems in various fields like artificial intelligence, and medicine etc. Because of the autonomous nature, BIAs are now being used in unmanned aerial vehicles (UAVs) for applications areas like internet of things (IoT), autonomous area searching and reconnaissance etc. This paper presents a UAV reconnaissance strategy jointly using the particle swarm optimization (PSO) algorithm and a few attributes of the penguin search optimization algorithm (PeSOA). The strategy mainly focuses on finding the rescue targets utilizing UAVs by optimizing their movement using combined PSO and PeSOA attributes. The results are analyzed in terms of grouping and non-grouping scenarios with and without using PeSOA attributes.

We propose a novel architecture for the continual representation learning for images, called variational continual auto-encoder (VCAE). Our approach builds a time-variant parametric model that generates images close to the observation by using optimized approximate inference over time. When the dataset is sequentially observed, the model efficiently learns underlying representations without forgetting previously acquired knowledge. Through experiments, we evaluate the development of test log-likelihood over time, which shows resistance to the catastrophic forgetting. The results show that VCAE has stronger immunity against catastrophic forgetting in comparison to the benchmark while VCAE requires much less time for training.

Because of the phenomenal increase in online data sharing and transmission in the multimedia networks, data compression and security are under the limelight. This work proposes a new encoding scheme using Huffman coding for the encryption of data transmitted through a multimedia network. The proposed technique manipulates the Huffman coded compressed data without changing its compression ratio. The encoding is performed by a mutation process which in short generates multiple Huffman tree mutations by swapping the branches of the original Huffman tree, i.e., 0 to 1 & 1 to 0, using a checklist-rotation approach. Further, the decoding process is also explained briefly followed by validation of the proposed scheme by encrypting, decrypting and reconstructing different images.

This paper presents an intrusion oriented heuristic for wireless video surveillance systems that jointly controls the frame rate (FR), spatial or video resolution (VR), and compression level of videos prior to transmission leading to minimum bandwidth and storage consumption. In the proposed model, video streams are transmitted in high quality, with high FR and VR, only when an intrusion is detected, hence saving substantial amount of bandwidth and storage for durations when no intrusion is detected. Results for the proposed model in terms of bandwidth and storage efficiency, using H. 264 and H. 265 video encoders, are also shown.

This paper proposes a novel method named exploiting spatio-temporal attributes of videos, which focuses on reducing bandwidth consumption in high-definition video streaming services. The method sequentially embeds every ninth frame of a video sequence in the spatial domain of its preceding eight consecutive frames. In this way, every ninth video frame does not require transmission, hence saving substantial amount of bandwidth. To avoid any visible degradation in videos, the embedding is done in an intelligent fashion by considering the variations in temporal information between consecutive video frames. The results show that up to 19% of bandwidth can be saved, while maintaining high video quality.

Online monitoring of multimedia networks is required to ensure seamless and ubiquitous delivery of services to the end users. Quality of multimedia content, such as video streams, often gets degraded due to network losses such as packet loss. Frame freezing artifacts are introduced in a video stream when packet loss or packet delay takes place. Estimating the perceptual impact of these artifacts on quality of experience of end users helps service providers to maintain quality of service. In this paper, we have presented a novel no-reference video quality metric, which measures the impact of frame freezing due to packet loss and delay in video streaming networks. The proposed metric is based on several features that directly impact the quality of experience of end users. These features, including motion characteristics of videos, are calculated using the temporal information between video frames and then combined mathematically to form a video quality metric. Different weights are assigned to different features for better performance of the proposed metric. With detailed experiments, we have shown that our method outperforms other contemporary methods in terms of high accuracy and low computation time in frame freeze detection, low root mean square values, high coefficient of determination, and high correlation between subjective and objective measurements. We have used five video databases for our model's evaluation and validation. Furthermore, we have shown that our method is statistically superior to the other models in comparison.

The Unique Reliable Identifier is increasingly becoming popular in the IoT world and consistency of the communication process in Network Management System (NMS). Likely, the NMS enabler should support Management Object (MO) and Object ID (OID) for the constrained or unconstrained network. Objects are able to choose suitable identity uniqueness which deploys the current technology of management system. Truly, the legacy NMS can dominate extensively terrestrial communication devices as well. However, growing number of smart devices and widely enhancing Internet of Things (IoT) nodes need to adjust uniqueness in the constrained management system. In this paper, the main focus concerning on Unique ID for Management Object and regulation MIB storage in Underwater-IoT (U-IoT). Generation steps are analyzing constrained devices and Objects uniqueness, MIB structure inside of the IoT and U-IoT. The U-IoT may support Service Discovery, Device discovery and register of the underwater devices. Our main goal is how to calculate the dynamic changing number of the device in the system and keep standard balance uniqueness in the NMS.

Next-generation mobile network development for future radio access, or 5G, intends to provide high capacity, improved Quality of Service (QoS), carrier broadening in new spectrum, and enhanced security in data privacy. So far, the main focus of researchers and vendors in the areas of cellular telecommunication is to improve capacity and spectrum in order to get better QoS, but less attention has been given to provide a secure and trustworthy connectivity service. Among different potential candidates for 5G, non-orthogonal multiple access (NOMA) is one of the key contenders. NOMA employs the principle of successive interference cancellation (SIC) at the receiver side to separate the users information. However, implementation of SIC increases concerns over user privacy. In this paper, shortcomings of conventional SIC are discussed and a new secure SIC scheme is presented. Our proposed SIC scheme enhances user security by using the MAC address and IMEI, which are dedicated to a smartphone as private keys. This private key enables the user to encode data without disturbing other users privacy.

This letter proposes a strategy to efficiently utilize the spectrum of unpaired users in non-orthogonal multiple access (NOMA) systems. This is done by pairing multiple similar gain users to a single distant user in a nonoverlapping frequency band pairing fashion. We consider a case where the number of far users in a cell is larger than the number of near users. Thus, some far users cannot be paired and should get data using conventional multiple access (OMA). In such case, we propose a strategy where a near user can be paired with multiple (two) far users to optimally utilize the spectrum of unpaired users in the NOMA system. Performance of the proposed pairing scheme is evaluated in terms of ergodic sum capacity over independent Rayleigh flat fading channel. Analytical and simulation results show that significant increase in the capacity gain of the system is achieved by using the proposed strategy compared to OMA and conventional NOMA.

This paper addresses the issues related with conventional near–far user pairing in non-orthogonal multiple access. Performance effects of near–far pairing on regions with negligible channel gain differences between users are investigated. These regions occur when pairing is performed between cell center and cell edge users, thus leaving the cell mid users to be either paired with each other or kept unpaired. Pairing these mid users with each other causes successive interference cancelation (SIC) performance degradation resulting in capacity reduction for these users. On the other hand, leaving these mid users unpaired perfectly avoids the SIC issue but makes these users unable to benefit from the capacity gains provided by non-orthogonal multiple access. Therefore, two user pairing strategies have been proposed that can provide capacity gains to almost all the users by accommodating them in pairs, while avoiding or minimizing the mid users pairing problem. A generalized M-users pairing scheme is also proposed. Simulations have been performed to investigate the performance of proposed schemes for both perfect and imperfect SIC receiver scenarios in comparison with conventional pairing where the mid users are kept paired with each other. Simulation results show that proposed schemes achieve high capacity gains, especially when imperfect SIC is considered. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we propose binary particle swarm optimization (BPSO) algorithm for distributed node localization in wireless sensor networks (WSNs). Each unknown node performs localization under the measurement of distances from three or more neighboring anchors. The node that gets localized during iteration will be used as a reference for remaining nodes. A comparison of the performances of PSO, basic BPSO and modified BPSO in terms of localization error and computation time is presented using simulation in Matlab.

Recently, the International Society of Automation (ISA) released ISA100.11a as an open standard for reliable wireless networks for industrial automation. ISA100.11a operates in the 2.4-GHz industrial, scientific, and medical (ISM) unlicensed band, and may suffer from interference from other radio technologies operating in the same band. This coexistence issue can lead to significant degradation of ISA100.11a performance. In this work, the performance of the ISA100.11a industrial wireless network under interference from an IEEE 802.11b wireless local area network (WLAN) is evaluated. An analytical model for the coexistence between ISA100.11a and IEEE 802.11b is suggested. The packet error rate (PER) and average end-to-end delay are evaluated, where the PER is obtained from the bit error probability and collision time, whereas the average end-to-end delay is investigated from the waiting time in buffer and the transmission time. Simulation results from the OPNET modeler are presented to validate the numerical analysis. It is demonstrated that ISA100.11a achieves acceptable PER and satisfies the delay requirement for industrial process control and monitoring, even under significant WLAN interference.

Bit width is a limitation of hardware implementation in baseband signal processing. It influences bit error rate (BER) performance and hardware complexity. This paper investigates BER performance of various bit width. We analyze bit width errors that come from quantization errors and clipping errors. As a result, it is shown that increasing bit width improves BER performance. At some point, increasing bit width does not enhance BER performance. This paper provides direction to determine optimum bit width value for 802.11n receiver design.

In this paper, we present an adaptive transmission power control in cluster-based routing to enhance performance of information dissemination in Vehicular Ad-hoc Networks (VANETs). In our scheme, transmission power is adapted based on the distance between vehicles and cluster head. The proposed algorithm also considers the uncertainty of vehicle mobility to achieve better performance in real situation. Extensive simulations have demonstrated that the proposed scheme shows better throughput and lower average transmission delay compared to constant power transmission scheme.

In this paper, we propose a sync-mechanism optimized for MAC protocol, which can be appropriate mechanism for controlling an underwater vehicle efficiently based on underwater acoustic communication. At present, underwater vehicle control system has difficulties and limits due to some underwater restrictions. For controlling the underwater vehicle, acoustic communication has many different features, unlike terrestrial wireless communication. The existing medium has limited wireless bandwidth, high transmission energy consumption and long propagation delay. And most of existing underwater MAC protocols haven't considered yet about nodes mobility, as well underwater vehicles. In this paper, we present our proposed sync-mechanism to reflect above-mentioned underwater features and we describe the methods to prevent data collision.

This paper proposes a new media access control (MAC) protocol, Preamble-MAC (P-MAC). P-MAC is adaptive and dynamic MAC protocol based on Virtual Distance Level (VDL), which is the estimated, accumulated information of channel status and variation obtained through periodically monitoring underwater environment information such as long, unknown propagation delays and low bandwidth for underwater acoustic sensor networks.

In this paper, the packet error rate of Zigbee under the interference of multiple Bluetooth networks is analyzed. The analytic model for the coexistence between ZigBee network and a Bluetooth network is presented, where the acknowledgement packets are considered. A numerical method is used for the interference of the multiple Bluetooth networks. The packet error rate (PER) is used for the performance measure. The PER is obtained from the bit error rate (BER) and the collision time. The BER is obtained from signal to noise and interference ratio. The simulation results are shown to validate the analysis.

Coexistent heterogenous wireless networks may interfere with each other and result in significant performance degradation when devices are collocated in the same environment. Understanding the impacts of interference among different wireless networks is one of critical issues for designing and implementing network system with multiple wireless technologies. The main goal of this paper is to provide a general framework to evaluate the interfering effects of one network to another network system. As a measure of performance, packet error rate (PER) is evaluated, where the PER is obtained from bit error rate (BER) and collision time. Analytic results are validated by simulations using OPNET.

In high density (HD) mesh networks, packet losses can occur due to co-channel interference (asynchronous interference) or collisions (synchronous interference). In this paper, we first propose a novel method of estimating the probability of collision and interference statistically. Further, we integrate this differentiation method with physical carrier sensing adaptation in a novel centralized algorithm to improve the aggregate throughput in HD mesh network. Extensive simulations results show that the on-line algorithm approaches the optimal throughput predicted by analytical models.

In this paper, a new clear channel assessment (CCA) method: cascaded-CCA, is proposed that is apropos for next generation cognitive radios. The primary motivation for the proposed approach is to integrate the respective advantages of two 'standard' CCA mechanisms (energy detection and preamble detection) to arrive at a new dual-threshold CCA family that can provide greater flexibility towards tuning MAC performance. The performance of cascaded-CCA is explored via MATLAB simulations that implement the CCA modules and medium access control (MAC) protocol for IEEE 802.11 and IEEE 802.15.4 as representative examples.

In this paper, the performance of IEEE 802.15.4 ZigBee under the interference of IEEE 802.11b wireless local area network (WLAN) and/or Bluetooth is evaluated using an analytic model for the coexistence among ZigBee, WLAN, and Bluetooth. The packet error rate (PER) is evaluated, where the PER is obtained from the bit error rate (BER) and the collision time. The BER is obtained from the signal-to-interference-plus-noise ratio (SINR). Finally, the analytic results are validated by simulations.

As a 4G (generation) network architecture, IP based IMT network is the architecture which is based on a new concept and proposed to cope with large multimedia traffics generated by mobile communications in the near future as the backbone network for ubiquitous computing networks. During the paging process in IP based IMT network, location manager (LM) makes flooding the paging packets to every node in location area (LA) to which MN is belong. At a result of the defects, the overall network efficiency is significantly lowered to process the paging of one MN resulting in the waste of network resources. To resolve this problem, this paper proposed novel paging mechanism using a proxy concept

This paper presents an interference model of IEEE 802.11b wireless local area network (WLAN) affected by IEEE 802.15.4 wireless personal area network (WPAN). The packet error rate (PER) of the IEEE 802.11b under the interference of the IEEE 802.15.4 is analyzed, and is obtained by the bit error rate (BER) and the collision time. The safe distance ratio can be obtained from the PER. Further, this paper suggests a packet length to reduce the effect of the IEEE 802.15.4 interference and obtain a maximum throughput of the IEEE 802.11b. The analytic results are validated using the simulation

CDN does not calculate connections between infra networks and user networks by increasing physical network. Instead, CDN decentralizes bottlenecked data traffics or contents to main parts of Internet network. By this, an amount of traffics passing through middle-mile of complicated Internet is decreased and CP (contents provider)'s server loads are reduced by allocating contents to many distributed servers. In this paper, we proposed an extended Rosary overlay network which is suitable for CDN environment by modification and supplement of Pastry. The proposed Rosary extends Pastry by dividing it into inter-Pastry and intra-Pastry to fit for CDN environment, makes application-level multicasting and adopts semi-hash scheme. By adopting geographic layout technology to proximity neighbor selection technology of Pastry, Rosary node was made to act as landmark server.

In this paper, a new communication network with high safety, maintainability, and user convenience for digital I and C systems of nuclear power plants are proposed based on integrated design concepts, such as simple structure, standardized protocol, hard real-time property, high-speed data rate, fault-tolerance, verification and validation, hierarchical architecture, data transfer control using data gateway, and enhanced network management. Components of the proposed communication network are three level networks such as a field network, a control network, and an information network, a data gateway between the control network and the information network, and data gateway between control divisions. A new data flow concept for enhancing independence and user convenience in the data gateway between the control network and the information network is proposed. The architectures of three level networks and both gateways are proposed.