In reconnaissance missions, Unmanned Aerial Vehicles (UAVs) can play an important role in collecting information from critical ground targets. However, most of the existing works either consider enhanced mobile broadband (eMBB) payload or ultra-reliable low-latency communication (URLLC) control information communication. In this paper, we investigate UAV trajectory planning and resource allocation for the coexistence of eMBB payload and URLLC control information communication in a multi-UAV reconnaissance scenario. This paper formulates a joint optimization of eMBB and URLLC resource allocation and UAV trajectory to maximize the average payload transmission data rate while fulfilling the latency and reliability requirements of URLLC services and the data rate requirement of eMBB services. To address the challenge of handling diverse Quality of Service (QoS) requirements, we decompose the problem into eMBB and URLLC sub-problems, which are solved iteratively.Simulations conducted in MATLAB confirm that optimizing UAV trajectories during the mission significantly enhances network performance by 39% rather than a linear trajectory in the 4-UAV scenario. Moreover, the proposed joint optimization algorithm outperforms URLLC and eMBB pre-allocated channel strategies by 13% and 10%, respectively. Additionally, the results show that stricter URLLC requirements, such as tighter delay constraints or larger packet sizes, lead to reduced average eMBB throughput.

This paper investigates uplink radio resource optimization of a user-centric (UC) cell-free (CF) massive multiple-input multiple-output (mMIMO) system aided by the rate splitting multiple access (RSMA) technique subject to pilot contamination. We formulate problem to maximize the minimum spectral efficiency (SE) problem by jointly addressing decoding order selection, power allocation, and access point (AP) - user equipment (UE) association assignment. The envisioned optimization exhibits two challenges. First, it requires global channel state information (CSI) for near-optimal performance, which incurs substantial overhead and data collection costs in large-scale CF networks. Second, the optimization is intractable due to its NP-hard and discrete non-linear programming nature. To address the CSI acquisition issue, we utilize a digital twin (DT) of the CF mMIMO system, leveraging its context-awareness to acquire global CSI with reduced overhead. To address computational intractiablity of the optimization problem, we decompose it into three sub-problems. The power allocation sub-problem is transformed into a second-order cone programming problem and solved by the bisection method. Additionally, we propose a computationally efficient heuristic approach for power allocation. Next, we propose an analytical method for the decoding order selection by ranking the channels in descending order of strength. Simulation results validate the ability of the proposed approach to attain the near-optimal performance. Subsequently, the AP-UE association assignment problem is solved by a heuristic approach to further improve the SE performance. Finally, we solve the original NP-hard problem in a unified manner via the block-coordinate descent algorithm. Simulation results underscore a substantial 61% improvement in the SE performance when integrating the RSMA technique into a UC CF mMIMO system.

This paper addresses channel estimation error (CEE) mitigation in user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) systems under pilot contamination (PC). To suppress the PC, we formulate an optimization problem to minimize the normalized CEE through the joint treatment of the pilot power allocation and the pilot assignment. However, the NP-hard nature of the problem makes finding a global optimal solution computationally infeasible. To tackle this challenge, we decompose the problem into two sub-problems: the fractional programming (FP) technique is employed for pilot power allocation, and a multi-agent reinforcement learning (RL) approach is applied for pilot assignment. The RL-based scheme, however, involves iterative action generation and reward computation, leading to significant overhead from repeated information exchanges. To mitigate this issue, we leverage a digital twin (DT) of the CF mMIMO system, utilizing its virtual environment and contextual awareness to optimize CEE reduction with minimal overhead. By employing closed-form expressions, the proposed schemes achieve computational efficiency without reliance on complex numerical solvers. Finally, we solve the original NP-hard problem via the block-coordinate descent and sequential optimization methods. Numerical evaluations demonstrate that the proposed FP-based pilot power allocation scheme improves the 95%-likely spectral efficiency (SE) by up to 15% and reduces the average pilot transmission power by up to 83% compared to existing methods. Furthermore, the pilot assignment scheme enhances the average SE performance by up to 7.4% over state-of-the-art pilot assignment approaches. These results validate the effectiveness of our proposed framework in reducing CEE, leading to enhanced improvement in system performance in the presence of PC.

This paper proposes a semi-supervised quantum particle swarm optimization-based tone reservation (SS-QPSO-TR) method with affinity propagation (AP) clustering and k-nearest neighbours (KNN) algorithms. The proposed SS-QPSO-TR method reduces the high peak-to-average power ratio (PAPR) in orthogonal time sequency multiplexing (OTSM) systems. The main finding is a significant 4.5 dB, 4.3 dB and 4 dB PAPR reduction in contrast to the original OTSM for QAM-64, 16 and 4, respectively while concurrently reducing transmission computational complexity by 16.7 times, compared to adapted tone reservation in both-domain (A-TR-BD), at the same bit error rate (BER) and error vector magnitude (EVM) performance. The proposed SS-QPSO-TR scheme enables efficient modulation of OTSM transmitters in high-mobility, real-time, wireless communication scenarios.

Network Slice Identification (NSI) is crucial for managing Quality of Service (QoS) in Beyond 5G (B5G) networks, particularly for smart city applications. This study explores and compares supervised, unsupervised, and semi-supervised learning techniques for NSI, addressing the challenges of limited labelled data in production environments. We use a publicly available 5G network dataset to model and perform comparisons among supervised, unsupervised, and semi-supervised learning approaches. Our methodology involves feature selection, dimensionality reduction using t-SNE, and addressing class imbalance through undersampling. We evaluate model performance using accuracy and Silhouette Score. Our results show a Random Forest Classifier achieves 100% accuracy with supervised learning. The unsupervised K-Means clustering model, optimized with both t-SNE and undersampling, achieves a mean accuracy of 92.83%. Semi-supervised learning using a self-training method and being trained on only 10% of the training data points performs comparably to the supervised models. Importantly, we demonstrate the robustness check using test data perturbation injecting additional variability in data simulating unknown 5G network fluctuations. This comparative analysis provides insights into the trade-offs between different learning approaches for NSI in B5G networks, offering practical solutions for scenarios with different conditions of labelled data.

Network slicing is a fundamental enabler for the advancement of fifth generation (5G) and beyond 5G (B5G) networks, offering customized service-level agreements (SLAs) for distinct slices such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communication (URLLC). However, smart city applications often require multiple slices concurrently, posing significant challenges in resource allocation, service isolation, and maintaining performance guarantees. This paper presents eSlice, an elastic inter-slice resource allocation mechanism specifically designed to address the dynamic requirements of smart city applications. eSlice organizes applications into hierarchical slices, leveraging cloud-native resource scaling to dynamically adapt to real-time demands. It integrates two novel algorithms: the Proactive eSlice Allocation Algorithm (PeSAA), which ensures the fair distribution of resources across the substrate network, and the Reactive eSlice Allocation Algorithm (ReSAA), which employs Multi-Agent Reinforcement Learning (MARL) to dynamically coordinate, reallocate, and recover unused resources as network conditions evolve. Experimental results demonstrate that eSlice significantly outperforms existing methods, achieving 94.3% resource utilization in simulation-based experiments under constrained urban-scale scenarios, providing a robust solution for dynamic resource management in 5G-enabled smart city networks.

This paper tackles the complex problem of maximizing the ergodic sum-rate in intelligent reflecting surface (IRS)-assisted full-duplex (FD) cluster non-orthogonal multiple access wireless-powered communication networks. The primary challenge lies in the joint optimization of hybrid access point (HAP) beamforming vectors, IRS phase shifts, and uplink power allocation for sensors, while considering practical constraints— including channel uncertainties, a nonlinear energy harvesting model, self-interference at both the HAP and sensors due to FD transmission, the rank-one constraint of the IRS, and the limited buffer capacity of the sensors. These factors result in an NP-hard optimization problem that conventional approaches cannot solve efficiently. To address this, we propose utilizing a deep reinforcement learning framework based on the twin delayed deep deterministic policy gradient (TD3) algorithm. Our results show that TD3 improves the system’s average sum-rate by up to 12% and 48% over the deep deterministic policy gradient (DDPG) and random allocation benchmarks, respectively. The results also show that both TD3 and DDPG have similar computational complexity. Thus, TD3 achieves a favorable balance between performance and computational complexity, making it a scalable and practical solution for next-generation networks where spectrum efficiency and throughput maximization are critical in hyper-dense environments.

This paper introduces the matroid-based modified greedy breadth-first search (MGBFS) algorithm, a pioneering approach tailored for optimizing network slicing for smart city applications. It adeptly allocates virtual network functions and virtual links, considering constraints while optimizing for cost and latency considerations. This approach extends beyond the boundaries of the access network, offering a comprehensive solution spanning the edge, transport, and core network domains. Additionally, we propose a dual integer linear programming (ILP) optimization problem to jointly minimize the embedding cost and latency of the network. An extensive comparison of the MGBFS algorithm with an existing baseline model and ILP confirms the superiority of our proposed method.

This paper studies the performance of integrated intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA)-aided wireless powered communication networks (WPCNs). The problem of maximizing the sum-throughput of the system under realistic operating conditions is formulated as a non-convex optimization problem. Given the complexity of jointly optimizing the decision variables for sum-throughput maximization, we propose a two-stage algorithm to achieve a solution. The algorithm first establishes an active DL energy beamforming matrix and IRS phase shift vector to maximize the sensors’ aggregated received power, followed by optimizing DL and UL time slots and UL energy resources. The numerical results demonstrate that the proposed algorithm increases the average sum-throughput by $45 \%$ in the perfect channel state information (CSI) scenarios. However, its performance declines with an increase in IRS elements under imperfect CSI, due to increased channel estimation errors.

This paper explores the use of an intelligent reflecting surface (IRS) in backscatter-assisted wireless powered non-orthogonal multiple access (NOMA) networks. In these networks, energy-constrained sensors first harvest energy from a power station with the help of an IRS in the downlink (DL) phase. They then transmit information to an access point using NOMA during the uplink (UL) phase. When sensors have low energy levels, they can switch from the high-power conventional mode to the low-power backscatter mode. By utilizing a practical energy harvesting model that captures realistic rectifier characteristics, we propose a new algorithm that optimizes DL energy harvesting time, UL backscatter time, UL conventional data transmission time, and DL phase-shift vector. Additionally, we analyze the impact of imperfect channel estimation on system performance. The numerical results demonstrate that IRS provides at least 30 % energy efficiency improvements.

Multi-Tenant Data Centers (MTDCs) allocate resources to tenants in terms of processors, memory, and storage. However, equal allocation of network resources is often overlooked, leading to unpredictable application performance. To address this issue, we propose Tenant-Aware Resource Allocation (TARA), a virtual resource allocation mechanism for MTDCs. TARA allocates tenants’ virtual network resources as virtual ports on the substrate physical network, enabling control and management by dedicated controllers. In this paper, we introduce a classification method for virtual nodes within Virtual Data Centers (VDCs) aimed at ensuring optimal network performance based on tenant demands. Furthermore, we present a source routing mechanism that utilizes path tables to minimize traffic forwarding delays and enhance network workload efficiency. The TARA model optimizes virtual resource allocation, enhances network performance, and simplifies virtual network resource management. Experimental evaluations demonstrate the effectiveness of the TARA system in improving network performance and meeting tenants’ quality of service requirements.

This paper proposes a pilot power allocation scheme that simultaneously reduces both the pilot contamination effect and per-user pilot transmission power for the user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) systems during the uplink training phase. The proposed pilot power allocation scheme comprises a simple algorithm that tries to minimize the channel estimation error iteratively for improving the estimation quality. It is found from the simulated results that the average convergence time of the proposed scheme demonstrates a scalable attribute. Moreover, numerical results verify the effectiveness of the proposed scheme in improving the 95%-likely spectral efficiency performance by up to 16% and reducing the average pilot transmission power by up to 78 % compared to a scheme that transmits full pilot power (i.e., no control) and a recent pilot power allocation scheme that uses successive approximation technique with first-order Taylor approximation.

To prolong the lifespan of low-power cognitive sensors, we consider a backscatter-assisted wireless-powered cognitive radio network (WPCRN) in which the sensors must harvest sufficient energy to support conventional data transmission. When the sensors experience low energy levels, they switch to the backscatter communication mode to reduce the latency prior to transmitting sensed data. Additionally, we use the full-duplex model to achieve improved spectral and time efficiency. However, due to channel uncertainty, designing robust conventional and backscatter data transmission is challenging. Thus, the goal is to design a robust data combining vector against channel inaccuracies and, consequently, maximize the sum-throughput of all the sensors. We propose an efficient and low-complexity algorithm named Joint Optimal Time and Energy Allocation with Robust Beamforming (JOTEA-R). Numerical results show that the JOTEA-R algorithm improves the sum-throughput by approximately 15% compared to the benchmark equal time allocation algorithm with robust beamforming. The proposed system model can be used in a remote environmental monitoring network in which the sensors must continuously monitor and transmit information such as temperature, humidity, air quality, and pollution levels data to a cloud data center.

This paper tackles the problem of maximizing the minimum spectral efficiency (SE) via allocation of the uplink optimal transmission power in a rate splitting multiple access (RSMA)-aided user-centric (UC) cell-free (CF) massive MIMO (mMIMO) system. It is shown that the optimization problem is non-convex. The problem is then transformed into a second-order cone programming problem which is solved iteratively by leveraging the bisection method. Numerical results demonstrate the effectiveness of the RSMA technique to improve the 95%-likely SE gain by up to 332% compared to a UC CF mMIMO system with no power control.

In this paper, an effective pilot assignment algorithm is proposed to counter the pilot contamination (PC) issue caused by limited pilot sequences during massive access in a user-centric cell-free massive multiple-input multiple-output system. The problem of pilot assignment is formulated as max-min spectral efficiency (SE) optimization problem which is NP-hard. Hence, the paper proposes a heuristic-based algorithm to assign pilots to users (UEs) in such a way that the repetitive assignment among the associated UEs for all the access points can be avoided. Numerical results demonstrate that the proposed algorithm achieves up to 37% average SE gain while reducing the interference power by up to 52% relative to the random and two state-of-the-art pilot assignment schemes. Moreover, the proposed pilot assignment scheme exhibits the best balance in the trade-off between the performance criteria and the computational complexity relative to its comparatives.

This paper proposes a power allocation scheme that improves the overall spectral efficiency (SE) and fairness performance, and reduces a significant amount of per user transmission power for the user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) systems during uplink transmission. The proposed power allocation scheme comprises of a new power allocation model based on some adjustable parameters and the large-scale fading coefficients, and an algorithm to adapt these parameters for improving the minimum SE performance among all UEs. One important aspect of the proposed scheme is its simplicity from a design perspective which avoids complex optimization methods. Moreover, the proposed scheme provides fairness performance close to that of the max-min-SE based power control strategy and offers a SE performance comparable to that of the power control scheme that maximizes the sum-SE (max-sum-SE) while reducing the average transmission power simultaneously. Numerical results show that, compared to the max-min-SE scheme, max-sum-SE scheme, and a recent fractional power allocation policy, the proposed power allocation scheme improves the SE performance by up to 64.3% while reducing the average transmission power by up to 89%.

This paper studies a resource allocation problem utilizing the power splitting (PS) architecture in SWIPT (Simultaneous Wireless Information and Power Transfer)- enabled device-to-device (D2D) communications underlaying a non-orthogonal multiple access (NOMA) cellular network, where the cellular users receive signal from the base station at different power levels. The device transmitter and receiver employ a PS architecture; hence a fraction of the received signal power is used for energy harvesting and the rest is used for information decoding. The device transmitter communicates with the device receiver such that the minimum rate requirement of the cellular users in the network is guaranteed. The formulated resource allocation problem, which aims at maximizing the throughput of D2D communications, is non- convex. Analysis and simplifications lead to an optimal solution obtained using the gradient descent method. The numerical results depict that the NOMA cellular network offers considerable D2D throughput gain over the benchmark orthogonal multiple access network. The results are significant for the adoption of energy harvesting D2D communications combined with NOMA-based cellular networks in the future.

This paper proposes a cascading artificial neural networks (ANNs) algorithm with performance-enhancing filters for ambient radio frequency (RF) energy harvesting (EH) with environment detection (ED), referred to as ANN-ED. This ANN-ED algorithm can reliably operate in both urban and rural environments where there is unpredictable availability of unintended sources and dynamic channel conditions between the sensors and the unintended sources. Numerical results show that sensors using the ANN-ED algorithm can successfully sense up to 98.7% of the data compared to an ideal sensor, offering a significant improvement compared to the 0.3% achieved by an ANNs-based RF EH without ED. Sensors using the ANN-ED algorithm have an accuracy rate of up to 100% as well; a significant improvement over that of an ANNs-based RF EH without ED whose accuracy can be as low as 0%. The reliable operation of ambient RF EH sensors in all environments enhances the practicality of its usage regardless of location.

In this paper, an adaptive pilot assignment scheme is proposed to solve pilot contamination (PC) caused by the reuse of limited pilot sequences, while allowing massive access in the scalable Cell-free massive multiple-input multiple-output systems. The proposed allocation scheme consists of algorithms for determining initial serving relationships between access points (APs) and user equipments (UEs), and an adaptive group forming strategy where groups are formed with UEs that contain the least common associated APs. The proposed scheme guarantees the association of all UEs and better spectral efficiency (SE) by reducing PC efficiently with limited pilot sequences. Numerical results prove that the proposed allocation scheme outperforms the random assignment scheme and a recent pilot assignment proposal that allows massive access in respect to 95%-likely SE and average SE performance.

Autonomous driving is a use case in 5G enhanced vehicle to vehicle (V2V) communication. Secure transmission of V2V messages is paramount for the successful deployment and operation of autonomous vehicles, especially in the presence of passive eavesdroppers. The secrecy outage probability and instantaneous secrecy capacity necessary for successful V2V communication in a cascaded Rayleigh fading environment are assessed. An algorithm for selecting the appropriate vehicle to serve as a relay to achieve secure dual-hop communication between a legitimate transmitter and a legitimate receiver in the presence of a passive eavesdropper is proposed. The simulation and analytical results are compared. Numerical results show that the relay selection algorithm can successfully be used in dual hop communications. Results suggest that designers of V2V communication should consider fading techniques outside of the traditional single Rayleigh.

Abstract This paper analyzes the achievable secrecy rate in a millimeter wave (mmWave) ad hoc network with multi‐array antenna transmission in the presence of non‐colluding and colluding eavesdroppers. By exploiting the tools of stochastic geometry, the average achievable secrecy rate is derived, taking into consideration the impact of blockages, directional beamforming, and Nakagami‐ m fading. Moreover, a simple yet effective artificial noise transmission (Tx‐AN) technique is applied at the transmitting nodes to enhance the secrecy performance while the channel state information at the desired transmitter is unknown. Numerical and simulation results are presented for the average achievable secrecy rate in the mmWave ad hoc network without and with the Tx‐AN technique. For example, at the high transmit power (>20 dBm), the average achievable secrecy rate with the Tx‐AN technique is up to three times higher than that obtained when the Tx‐AN technique is not used. Furthermore, the results demonstrate the secrecy robustness of the Tx‐AN technique against increasing eavesdroppers' intensity. Finally, the proper power allocation between the message and AN signals that maximizes the average achievable secrecy rate is computed.

This article presents the design and performance evaluation of a hysteresis-free on-the-fly (HF-OTF) scheduling function for the Internet protocol version 6 over the time slotted channel hopping (TSCH) mode of IEEE 802.15.4 based Internet-of-Things (IoT) networks. The proposed HF-OTF scheduling function tackles the problem of determining the amount of network resources (i.e., timeslots and frequency channels) a node needs to request from its preferred parent node. Unlike the conventional OTF scheduling function, the proposed algorithm can determine the required bandwidth without the configuration of a hysteresis quantum. The performance evaluation of HF-OTF scheduling function shows that, under bursty traffic in a TSCH-based IoT mesh network, the proposed HF-OTF outperforms the conventional on-the-fly (OTF) and the enhanced OTF (E-OTF) with optimal hysteresis quantum settings. Specifically, the HF-OTF achieves an end-to-end packet delivery ratio 23% and 34% higher than that of the E-OTF and the conventional OTF, respectively. For the end-to-end latency performance metric, the HF-OTF exhibits an end-to-end latency that is 4% and 24% lower than that of the E-OTF and conventional OTF, respectively.

In this paper, a file popularity and size-aware (PSA) caching scheme is proposed for a hybrid heterogeneous network (HetNet) comprising a file server, macro base stations (BSs) operating in the microwave frequency bands, and pico BSs operating in the millimeter wave frequency wave bands. Different than the state-of-the-art size-weighted popularity (SWP) and popularity-based caching schemes that assume equal file size, the file PSA caching scheme utilizes knowledge of the file popularity and size distributions to maximize the network average cache hit probability and the network average success probability subject to the available cache capacity. Moreover, the maximization of the network average success probability is shown to be non-convex and dynamic programming and branch and bound algorithms are employed to circumvent its convexity. New expressions for the optimal file caching probabilities that maximize the network average success probability in noise-limited and interference-limited HetNet scenarios are provided which are evaluated using numerical techniques. The results demonstrate that the proposed file PSA caching scheme provides up to 7% gain in the network average success probability compared to the SWP-based scheme. Lastly, the combination of coded caching with cooperative transmission achieves up to 40% gain compared to a non-cooperative transmission scheme without coded caching.

This paper analyzes the achievable secrecy rate in a millimeter wave (mmWave) multi-input single-output (MISO) ad hoc network in the presence of colluding eavesdroppers. First, using the tools of stochastic geometry, the mathematical analysis of the average achievable secrecy rate is performed, taking into consideration the impact of blockage and Nakagami-m fading. Moreover, a multi-array artificial noise transmission technique is applied at the typical transmitter (Tx-AN) to enhance the average secrecy rate. Consequently, the mathematical expression of the average achievable secrecy rate for mmWave MISO ad hoc network with Tx-AN technique is presented. Numerical and simulation results show that, at given values of colluding eavesdroppers' intensity and total transmit power, using Tx-AN technique achieves 56.4% improved average secrecy rate over that without. Furthermore, by applying the Tx-AN technique, increasing the colluding eavesdroppers' intensity provides no negative impact on the average secrecy rate. However, without the AN technique, the average secrecy rate faces a fast degradation when the intensity of colluding eavesdroppers increases. The results therefore show that the Tx-AN technique is a useful technique to enhance the secrecy performance of mmWave MISO ad hoc network in the presence of colluding eavesdroppers.

Secure transmission of cached files in wireless networks is crucial to protect the transmitted files from interception by illegitimate nodes. This paper analyzes the secrecy rate performance of a cache-enabled millimeter wave (mmWave) cellular network in the presence of colluding eavesdroppers. The average cache hit probability is computed and the stochastic geometry framework is used to evaluate the average secrecy rate of the network, taking into consideration directional beamforming and Nakagami-m fading. Moreover, the size of the available files is modeled by a Pareto distribution based on existing studies, while a memory and file size-aware caching (MFC) scheme is proposed. The MFC scheme incorporates an optimized memory allocation algorithm that reserves memory blocks in the cache of a base station (BS) based on the frequency of the requested files, the parameters of the Pareto file size distribution, and the network design parameters. The numerical results show that the proposed MFC scheme achieves up to two-fold gain in the average secrecy rate compared to the state-of-the-art probabilistic and most-popular-content (MPC) caching schemes. Furthermore, the impact of the skew exponent and the colluding eavesdroppers' intensity on the average cache hit probability and secrecy rate, respectively, is investigated.

A memory and file size-aware caching (MFC) scheme is proposed and studied in a D2D-enabled small cell network operating in millimeter wave bands. The proposed MFC scheme is characterized by the parameters of the file size distribution which are assumed to be Pareto distributed based on existing studies. The MFC scheme incorporates a memory allocation protocol where memory blocks are reserved for files based on the user requests, the Pareto file size distribution, and the available memory capacity. Analytical expressions for the cache hit probability and the cache-layer throughput are derived under the proposed MFC scheme. The results show that the MFC scheme achieves up to 33% increase in the cache hit probability of the cellular tier when compared to the state-of-the-art geographic and popularity-based caching schemes. Lastly, insights are provided on the impact of concurrent D2D and cellular transmissions on the cache-layer throughput.

Secure communication in the millimeter wave (mmWave) network is an important issue in the next-generation wireless network due to the massive improvement in the eavesdroppers' ability. This paper studies the secrecy rate performance of a mmWave multi-input single-output (MISO) ad hoc network in the presence of colluding eavesdroppers. Firstly, to enhance the average achievable secrecy rate, an artificial noise (AN) transmission with null space linear precoder (Tx-ANLP) is applied, taking into consideration the effect of blockage and Nakagami fading. Consequently, the tools of stochastic geometry are used to derive the mathematical expression of the average achievable secrecy rate for mmWave MISO ad hoc network with Tx-ANLP technique. Numerical and simulation results show that, using the Tx-ANLP technique achieves more than three-fold improvement in the average secrecy rate over that without in the high power transmit regime (>15dBm). Moreover, the effect of increasing the colluding eavesdroppers' intensity without using the Tx-ANLP technique is studied which provides a high deterioration in the average secrecy rate. Conversely, when the Tx-ANLP technique is applied, increasing the colluding eavesdroppers' intensity has no negative impact on the secrecy rate. Furthermore, the proper power allocation between the message and AN signals which maximizes the average secrecy rate is computed. The results therefore show that the Tx-ANLP technique is a useful technique to enhance the secrecy performance of mmWave MISO ad hoc network in the presence of colluding eavesdroppers.

In most industrial applications, failed nodes will be repaired, but these repairs take time. For critical system applications, network operators need to understand how their networks function immediately after a failure but before a repair is possible. In this paper, we introduce an extension to the frame-level optimized routing/ scheduling algorithm to improve reliability and energy efficiency and compare it with the other industrial routing algorithms considering the oil refinery wireless sensor networks. We present a structured performance evaluation approach for studying the impact of the routing/scheduling algorithm on the post-failure/pre-repair regime. Simulation results show that the proposed algorithm exerts a positive impact on network performance: highly-reliable, low-latency, energyefficient, and fitting with most industrial applications.

The downlink performance of a millimeter-wave cellular network comprising multiple-antenna-remote radio heads (RRHs) that service users under a hybrid Joint Transmission and Coordinated Beamforming (JT-CB) scheme is analyzed. Using the tools of stochastic geometry, the signal-to-interference-plus noise ratio (SINR) distribution at a typical user is derived and analytical expressions are provided for the coverage probability and network area spectral efficiency (ASE) under the proposed scheme. In addition, the average rate gain under the proposed scheme is characterized as a function of the network load, file request and caching distributions, and the spatial distribution of the RRHs. Numerical results reveal up to 68% coverage probability gain under the hybrid JT-CB scheme, compared to a CB-only scheme in the low coverage regime (>10 dB SINR threshold), while a CB-only scheme outperforms the hybrid JT-CB scheme by up to 5% in the high coverage regime (<;10 dB SINR threshold). Moreover, the hybrid scheme is shown to outperform a JT-only scheme in all coverage regimes, attaining 52% at 30 dB SINR threshold. In addition, up to 25%, 35%, and 120% gains in ASE are shown to be achieved, compared to the JT-only, CB-only, and non-coordinating schemes, respectively.

This paper formulates and solves a joint-optimization problem whose objective is to maximize the sum-throughput of a wireless powered communication network (WPCN) with radio frequency (RF) energy harvesting and backscatter communication using a beamforming hybrid access point. A new multi-sensor blind adaptive beamforming with combination protocol (MS-BABF/combo) is proposed and analyzed. Numerical results show that the jointly optimized MS-BABF/combo protocol delivers up to 48.8% and 26.4% increase in sum-throughput compared to a single input, single output configuration and the MS-BABF with time-switching (MS-BABF/TS) protocol, respectively, in a dynamic environment. The MS-BABF/combo protocol also considerably decreased the sensor dropout rate compared to the MS-BABF/TS protocol from 1.0% to 0.43% at 7.5m. The findings are significant for future widespread adoption of WPCN systems powered by RF energy sources in the real world by increasing the amount of harvested energy and system achievable data rate.

This paper formulates and solves a joint-optimization problem whose objective is to maximize both the sum-throughput and fairness of a wireless powered communication network (WPCN) with radio frequency (RF) energy harvesting. An algorithm based on the multi-source blind adaptive beamforming with hybrid protocol that maximizes the sum-throughput and fairness (MS-BABF/Hybrid-STF) is proposed and analyzed. Numerical results show that the jointly optimized MS-BABF/Hybrid-STF protocol achieves a 36.7% increase in fairness compared to a single input, single output configuration in a dynamic environment. The MS-BABF/Hybrid-STF protocol also delivers an average of 19.1% fairness gain when the number of sensors is varied from 2 to 8 compared to a similar protocol that optimizes only the sum-throughput of a WPCN instead. The findings are significant for future widespread adoption of WPCN systems powered by RF energy sources in the real world by allowing fairer throughputs between sensors. In turn, the transmission of sensors' sensed data with the required quality can be supported even when there are ongoing changes in channel quality.

This paper presents the design and performance evaluation of successive interference cancellation (SIC)-based pure Aloha (PA) for Internet-of-Things (IoT) networks. To this end, a window-based SIC algorithm is presented. A throughput model of the SIC-based PA is developed and analyzed to study both throughput and packet delivery ratio (PDR) performance metrics. The numerical results show that high throughput gain can be achieved in PA, thanks to the increased PDR contributed by the SIC. Specifically, after ten iterations of the SIC, the maximum throughput gain of the SIC-based PA can achieve up to 6.55 dB over the conventional PA. The SIC efficiency results also suggest that the interference residual level after each iteration of SIC plays an important role in terms of improving throughput in PA networks. Finally, the conditions under which the proposed SIC-based PA meet both throughput and PDR performance requirements are discussed.

The spectral efficiency (SE) and energy efficiency (EE) performance of a millimeter-wave (mmWave) cellular network is studied where a user device can associate with a base station (BS) or another user for device-to-device (D2D) communication based on an interference-aware D2D distance threshold. Using the tools of stochastic geometry, the mean interference, coverage probability, area SE, and network EE are derived under the proposed association scheme. Performance of the proposed scheme is compared with that of the minimum path loss (Min PL)-based and maximum biased-received-power (Max BRP)-based association schemes. The proposed scheme is shown to give the best coverage probability performance in noise-limited networks, while the three schemes converge in performance in interference-limited networks in the high coverage threshold regime (>20 dB). Further, the proposed scheme achieves up to 60% increase in the area SE and EE, compared to the Min PL-based scheme that gives the next best performance. Lastly, the paper proposed a goal attainment algorithm that achieves up to a seven-fold decrease in the mean deviation from a preset SE objective and 50% savings in EE, compared to the achievable performance under a constant transmit power and bandwidth allocation scheme.

This paper addresses the problem of long term location prediction for collision avoidance in Connected Vehicle (CV) environment where more information about the road and traffic data is available through vehicle-to-vehicle and vehicle-to-infrastructure communications. Gaussian Process Regression (GPR) is used to learn motion patterns from historical trajectory data collected with static sensors on the road. Trained models are then shared among the vehicles through connected vehicle cloud. A vehicle receives information, such as Global Positioning System coordinates, about nearby vehicles on the road using inter-vehicular communication. The collected data from vehicles together with GPR models received from infrastructure are then used to predict the future trajectories of vehicles in the scene. The contributions of this work are twofold. First, we propose the use of GPR in CV environment as a framework for long term location prediction. Second, we evaluate the effect of pre-analysis of training data via clustering in improving the trajectory pattern learning performance. Experiments using real-world traffic data collected in Los Angeles, California, US show that our proposed method improves prediction accuracy compared to the baseline kinematic models.

This paper evaluates the single channel throughput, packet delivery ratio, average packet delay, and the end device battery lifetime performance of the LoRaWAN technology for deployment in North America urban scenario. In order to account for the operational details of the technology, the simulation approach is used for performance evaluation. The simulation results reveal that, by using four data rates on a single 125 kHz channel, the throughput is 1.8 times of the one data rate scenario. Second, for the simulated elderly sensor devices network with 1000 EDs, at least 6 GWs are needed to guarantee the packet delivery ratio to be higher than 0.8, the average packet delay to be less than 3 s, and the end device battery lifetime to be higher than 1300 h.

This paper proposes and analyzes a distance-based duplex mode switching scheme for maximizing the average throughput of a cellular network and its underlay device-to-device (D2D) network. For each duplex mode of the cellular network, a node distance based duplex mode selection scheme is proposed to maximize the throughput of the D2D network. The proposed duplex mode selection scheme provides average throughput improvements in the range of 10%-200% for the D2D network compared to the always half-duplex and always full-duplex communications for high D2D user scatter variances.

This paper presents an analytical framework to investigate the performance of full duplex (FD) device-to-device (D2D) networks where devices in a cluster are capable of caching popular content. Assuming spatially randomly distributed D2D users in the cluster where the users request for content according to the Zipf distribution, analytical expressions are derived for the device communication probability, coverage probability, cluster average spectral efficiency, and content average download latency of the system. The system performance metrics are derived for the three device operating modes of FD only mode, half duplex (HD) only mode, and mixed FD/HD mode. Numerical results show that the mixed FD/HD mode is the most appropriate communication mode for cache enabled FD D2D networks to achieve the highest cluster average spectral efficiency and low content average download latency.

A throughput analysis is conducted for a large scale peer-to-peer wireless network whose devices are capable of operating in both full- and half-duplex modes, using the tools of stochastic geometry. Each device selects its operating duplex mode based on the distance to its partner node. In both duplex modes, the energy consumption and the hardware configurations of the nodes are made equivalent for fair comparisons between full-duplex and half-duplex operations. Using the results of the analysis, an approach is proposed to determine a distance threshold value for duplex mode selection, and the proposed distance threshold can be computed offline. It is found that the throughput performance of the hybrid full-/half-duplex network generated with the proposed distance threshold based duplex mode selection technique outperforms both the pure half-duplex and pure full-duplex network settings for most practically common network parameter values.

In capillary machine type communications, random multi-access lends itself as a natural choice at the link layer due to the largely intermittent, event-driven traffic involved. A major challenge to that end arises, however, from the anticipated large scale of the network. Considering a carrier sense multiple access based random access protocol where infrastructural support is available in the form of a coordinator node, this paper presents a framework to help sustain efficient protocol performance when faced with temporal variation in packet traffic. The coordinator dynamically determines a timely contention parameter by means of an online algorithm based on certain analytic characterizations of the protocol performance, and the end-nodes get opportunistically notified of it. Throughput, fairness and delay-variation performance produced by the proposed scheme turns out to be consistently close to an optimal protocol in identical settings, in contrast with that of the binary exponential backoff based solutions.

This paper presents the results of ambient RF energy measurements conducted at 23 locations in Calgary and area, including a mix of indoor/outdoor, rural/urban, public/private locations across frequency bands for cell phones (824-960MHz, 1710-2170MHz) and unlicensed industrial, scientific, and medical devices (2.4-2.5GHz, 5.150-5.875GHz) using a spectrum analyzer. A best case test scenario where an active RF source was nearby a user was also conducted. It was found that RF energy at the measurement locations is generally too low or too inconsistent for energy harvesting. Analysis of measurement data reveals that up to 37% and 11% of the locations can attain a peak power of at least -30dBm and -20dBm, respectively. However, ambient RF harvesting is probably feasible with an active source nearby. The findings open the door for further research in intended RF energy harvesting with a dedicated transmitter for simultaneous wireless information and power transfer in future generation sensors powered by ambient RF energy.

An analytical approach is developed to evaluate the performance of a hybrid base station (BS) location model which considers the location regularity of macro BSs and topological randomness of small BSs, where the coverage probability and spectral efficiency are derived and verified by simulation. When both tiers have the same BS intensity, the tier of tessellated macro BSs achieves a 50% advantage in spectral efficiency and the tier of underlaid randomly positioned small BSs exhibits a 30% improvement, over that of a 2-tier pure random model where both tiers are modeled by Poisson point processes. With the increasing small BSs to macro BS intensity, the spectral efficiency gain of the hybrid model over that of the pure random model decreases and even becomes marginal, thus providing a design guideline on the small BSs to macro BS intensity ratio to achieve a desired coverage probability and spectral efficiency in HCNs.

The Cognitive Radio (CR) is a fully-reconfigurable wireless device that can intelligently sense, manage, and exploit temporarily-vacant licensed spectrum bands during the absence of incumbent users. Broadly, the IEEE 802.22 is the first complete Wireless Regional Area Network (WRAN) standard that utilizes CR technology in the opportunistic access of white spaces in the television (TV) bands. Intrinsically, the increased CR networks' vulnerabilities alongside the experienced growth of attackers' capabilities, creates a strain on CR network designers to act accordingly. However, most of the existing efforts solely examined the issues of Denial-of-Service (DoS) attacks of IEEE 802.22 networks, such as Primary User Emulation (PUE) attack and Spectrum Sensing Data Falsification (SSDF) attack, each treated in isolation. One main challenge in CR network security domain is to efficiently represent possible simultaneous multiple security threats, and assess their effects. Unlike the previous works, this paper addresses the aforementioned challenge through using the holistic approach of assessing the combined effect of simultaneous multiple DoS attacks. The Bayesian Attack Graph (BAG) model is utilized in this paper to capture the probabilistic dependencies among IEEE 802.22 DoS threat-environment and known vulnerabilities. The simulation results indicate up to 51.3% increase in the probability of DoS in IEEE 802.22 networks considering simultaneous multiple attacks in comparison to the most severe sole attack. Finally, the paper introduces the BAG model as a feasible CR vulnerability metric that can facilitate the creation of a security tightening plan.

This paper develops a stochastic geometry-based analytical approach for calculating the throughput reliability of a cloud-radio access network (C-RAN) comprising randomly distributed remote radio heads (RRHs) and randomly located users. A tunable distance-based RRH transmit power control mechanism along with cooperative joint transmissions by the RRHs is employed to achieve power savings and high throughput reliability. The analytical result for the throughput reliability serves as input to analysis of per user achievable average rate and C-RAN network-level performance metrics of spectral efficiency and energy efficiency. The analytical results are validated by Monte Carlo simulation results with good agreement, thus confirming the accuracy of the developed analytical approach. The key finding from the analysis is that by carefully tuning the RRH transmit power and cooperation parameter (cluster radius), it is possible to realize a threefold improvement in the energy efficiency along with 108% enhancement in the spectral efficiency of C-RANs. Copyright © 2016 John Wiley & Sons, Ltd.

This paper analyzes the location-dependent performance metrics of coverage probability and spectral efficiency in hexagonal cellular networks under Rayleigh fading with a general distribution for shadowing and also including two special cases of no shadowing and lognormal shadowing. The effects of system parameters such as frequency reuse factor, transmission probability of base stations, and signal-to-interference-plus-noise ratio gap from Shannon capacity are accurately characterized. The proposed approach is applied to fractional frequency reuse FFR scheme where the impact of FFR on spectral efficiency is evaluated. Numerical results show that i in a lognormal-shadowed Rayleigh fading channel with the shadowing standard deviation of 12dB, the cell area wide spectral efficiency is degraded by approximately 40% compared with when there is Rayleigh fading without shadowing; ii the improvement in spectral efficiency achieved by FFR over the universal frequency reuse increases as the transmission probability increases and the shadowing becomes less severe; and iii in Rayleigh fading without shadowing environment where all the base stations are actively transmitting, FFR achieves approximately 20% improvement in spectral efficiency in the cell edge area. Interestingly, this improvement increases to about 30% if a 3-dB signal-to-interference-plus-noise ratio gap from Shannon capacity is further accounted. Copyright © 2015 JohnWiley & Sons

This paper addresses the problem of simultaneously achieving high spectral efficiency (SE) and energy efficiency (EE) in the heterogeneous radio access technology (RAT) environment via biased intra- and inter-RAT offloading. An analytical framework is developed for investigating the SE and EE performance of a two-RAT heterogeneous network (HetNet), based on which it is shown that the feasibility of increasing the SE and EE via offloading is strongly dependent on the load level and the biased offloading factors. For jointly maximizing the SE and EE, a multi-objective optimization problem subject to quality of service (QoS) constraints is formulated and solved to give the Pareto optimal operational regime in terms of the network parameters. Following this, the constrained Pareto regime is used to quantify the tradeoff between SE and EE as an opportunity cost measure. By opportunistically adapting the small cell BS density and biased offloading factors to the load conditions, we numerically show the range of load values that achieves a good balance in the SE-EE tradeoff while satisfying the specified users' QoS requirements.

This paper proposes an inter-wireless body area network scheduling algorithm for animal health monitoring. An optimization problem is formulated first to jointly reduce the energy consumption per unit time and to improve the packet success rate. Due to the combinatorial complexity of the optimization problem, a two-step heuristic solution algorithm is proposed. The first step is to find the optimal transmit power and packet payload size for each sensor by using local information. Then the second step uses a best effort method to assign time slot to each of the wireless body area networks. At the end of step two, the effect of interference is known, and this knowledge helps to determine the optimal frame size among all the feasible choices after several iterations of objective value calculation or a period of performance learning. The simulation results on packet success rate and energy consumption show that the proposed best effort time slot allocation method outperforms the color-based scheduling algorithm.

Multiuser multiple-input multiple-output orthogonal frequency division multiple access MIMO-OFDMA is considered as the practical method to attain the capacity promised by multiple antennas in the downlink direction. However, the joint calculation of precoding/beamforming and resource allocation required by the optimal algorithms is computationally prohibitive. This paper proposes computationally efficient resource allocation algorithms that can be invoked after the precoding and beamforming operations. To support stringent and diverse quality of service requirements, previous works have shown that the resource allocation algorithm must be able to guarantee a specific data rate to each user. The constraint matrix defined by the resource allocation problem with these data rate constraints provides a special structure that lends to efficient solution of the problem. On the basis of the standard graph theory and the Lagrangian relaxation, we develop an optimal resource allocation algorithm that exploits this structure to reduce the required execution time. Moreover, a lower-complexity suboptimal algorithm is introduced. Extensive simulations are conducted to evaluate the computational and system-level performance. It is shown that the proposed resource allocation algorithms attain the optimal solution at a much lower computational overhead compared with general-purpose optimization algorithms used by previous MIMO-OFDMA resource allocation approaches. Copyright © 2012 John Wiley & Sons, Ltd.

A stochastic model is presented in this paper for packet-level performance analysis of the medium access control (MAC) schemes considered for adoption in smart utility networks. Smart grid communications involve critical issues in terms of reliability and timeliness, and the MAC largely determines efficiency of packet-level communication within the immediate neighborhood. Powerline communication (PLC), a relatively old communication technology, has reemerged in recent times, and is regarded as one of the key enablers in smart grid communication. The analytic problem addressed in this paper is formulated in light of the distinctive characteristics of some of the PLC based MAC schemes, and also relevant to smart grid utility networks, e.g., low data rate CSMA/CA with access differentiation. Specifically, with consideration of two classes of packets, this paper investigates basic performance metrics such as throughput and packet service time in view of the effectiveness of the access differentiation mechanisms. The evaluation shows that with increase in the number of contending nodes, strong discrimination between the classes persists in medium access opportunities though both aggregate and class-wise throughputs degrade.

This paper addresses the problem of characterizing the quality of experience (QoE) of users accessing different types of applications in 802.11n wireless local area network (WLAN) deployment environments. Using the generalized logistic function with fractional powers, an analytically tractable QoE model that can be flexibly applied to different application types (real time and nonreal time) is formulated as a function of several quality of service (QoS) parameters. An 802.11n WLAN testbed is then used to validate the accuracy of the QoE model via experimental QoE measurements. The paper also investigates the impact of different network operational parameters including the number of users supported, percentage of real time traffic streamed and level of co-channel interference on both the QoS and QoE performance. Next, an optimization problem is solved to maximize the overall QoE achievable subject to the QoS constraints of the applications in use. Overall, the developed QoE model can be used as an effective tool for enhancing the Access Point (AP) capacity and the QoE performance by optimally adapting the network operational parameters.

This paper analyzes downlink coverage probability and spectral efficiency of Poisson cellular networks with lognormal shadowed Rayleigh fading, provided each user is associated to the closest base station (BS). Both location-dependent and cell area wide aspects of the coverage probability and spectral efficiency metrics are presented. Performance impact of system parameters such as frequency reuse factor, transmission probability, and Signal to Interference Ratio (SIR) gap from Shannon capacity are characterized. Numerical results support the view that shadowing significantly degrades the performance. The cell area wide spectral efficiency decreases by 37% when the shadowing standard deviation increases from 0dB to 12dB. Finally, the derived results on location-dependent metrics are applied to Fractional Frequency Reuse (FFR) optimal partitioning in terms of system spectral efficiency, which is found dependent on system parameters such as SIR gap from Shannon capacity. It is also numerically shown that the FFR significantly improves the (link) spectral efficiency for cell edge users. For a user far away from its associated BS (at a distance 3 times the radius of average cell area) and considering SIR gap from Shannon capacity of 3dB, FFR(1,3) improves the (link) spectral efficiency by 239% compared to the universal reuse factor.

This paper proposes a session state aware (SSA) handover procedure to solve the problem of seamless mobility in heterogeneous wireless networks. The novelty of the proposed SSA procedure is the alignment of the network layer handover execution instant with the callee's voice over Internet protocol (VoIP) session silence period. The effectiveness of the proposed SSA is assessed mathematically using Markov chain theory whose results are validated by simulation results. Numerical results show that the proposed SSA handover procedure reduces the handover packet loss by 46% and delivers mean opinion score rating of at least 3.6 where most of the VoIP users are satisfied.

This paper addresses the problem of balancing the tradeoff between spectrum efficiency (SE) and energy efficiency (EE) in heterogeneous cellular networks (HetNets). A framework is developed for analyzing the SE and EE of a 2-tier HetNet consisting of macro and femtocell base stations (BS) with shared spectrum operation. From the analysis it is shown that both the SE and EE can be significantly enhanced with the overlaid deployment of the femto tier. However, the performance gain achievable is found to be strongly dependent on the load level and the BS power consumption attributes. A multi-objective optimization problem that maximizes the SE and EE subject to Quality of Service (QoS) constraints is formulated and solved to give the Pareto optimal operational regime. In contrast to the conventional approach, this paper quantifies the SE-EE tradeoff as a Lebesgue measure, defined by the Pareto optimal regime. Numerical results show that while the performance improvement achieved by balancing the SE-EE tradeoff is marginal under high load conditions, it is feasible to significantly increase the SE and EE during low load conditions and satisfy the users' QoS requirements by optimally adapting the network configuration.

A two-hop socially aware resource allocation scheme is proposed for device-to-device communication in the downlink of Orthogonal Frequency Division Multiple Access networks. An optimization problem has been formulated with the objective to maximize the overall network utility and the data rate achieved by the users of a network plagued by limited bandwidth, limited power and user selfishness. Degree and eigenvalue centrality metrics capture the social awareness of the users. The mixed integer non-linear programming problem formulated is solved using the advanced integrated multidimensional modeling system optimization solvers. The proposed scheme is compared to conventional allocation schemes using system spectral efficiency, system fairness index and overall network utility performance metrics. Numerical results show that employing the proposed scheme in dense networks achieves performance improvement of approximately 75% in terms of spectral efficiency, up to 50% in terms of fairness and up to 7% in terms of utility over the conventional schemes.

In this paper, a tractable analytical framework for investigating the energy efficiency (EE) of a 2-tier heterogeneous cellular network consisting of macro and picocell base stations (BS) is developed. The BSs, which are randomly distributed, serve a number of users in the downlink under shared spectrum operation. By incorporating a tunable downlink power control mechanism, the successful transmission probability and the average throughput achievable in both tiers are analytically derived. Since varying load conditions has an important implication on the network performance, the EE is investigated as a function of the load level, considering the ability to transfer the load between the 2 tiers. An optimization problem that maximizes the throughput constrained 2-tier EE is formulated and solved to determine the optimal network operational parameters. In addition to providing valuable design insights, the findings reveal that, for different load conditions, it is feasible to significantly enhance the EE performance by optimally adapting the network configuration.

This paper deals with the performance analysis for the downlink with Rayleigh fading in Orthogonal Frequency-Division Multiple Access (OFDMA)-based hexagonal cellular networks with static frequency reuse. A statistical analytical framework is developed where inter-cell interference is approximated as being generated by surrogate interfering base stations whose locations are modeled by a spatial Poisson point process and the theory of stochastic geometry is exploited. Analytical expressions for coverage probability and spectral efficiency (including location-dependent and cell area wide) are derived and their numerical results are verified by simulation. In addition to the analytical tractability of the proposed framework, the effect of important parameters such as transmission probability, path loss exponent, and SINR gap from Shannon capacity are captured.

In this paper, the energy efficiency (EE) of a 2-tier cellular network subject to downlink transmission outage probability constraints is analyzed under a shared spectrum scenario. The heterogeneous network considered consists of randomly distributed microcell BSs overlaid with picocell BSs. Based on the analysis of the structure of the EE function, the sufficient condition that enables the EE of the 2-tier network to exceed that of a single tier network is determined. The operational bounds for the network in terms of the pico tier BS density is derived by analytically solving the optimization problem that maximizes the outage constrained 2-tier EE. The Lagrange multipliers attributed to the outage constraints, which quantify the marginal loss in the EE performance, are found to be strongly dependent on the power consumption parameters of both the micro and pico tier BSs. The implication of the findings is that, by carefully tuning the BS power consumption parameters, it is possible to realize significant improvement in the EE performance for different operational scenarios while satisfying the outage probability objectives.

The performance of preamble detection in Physical Random Access Channel (PRACH) for long term evolution (LTE) and its advanced degrades when channels become more time dispersive, such as Extended Typical Urban (ETU). However, the Base Station (BS) target received power is determined by users with time dispersive channels, in order to keep the acceptable performance for most of users in a cell. Motivated by the idea of collecting the energy of multiple paths, the optimal statistic is derived in this paper given the Power Delay Profile (PDP) of Rayleigh fading channels. An analytical framework of performance is also proposed in this paper and verified by simulation. It is shown that the proposed optimal statistic increasingly outperforms the traditional peak detecting when time-dispersion of channels increases. Considering the difficulty of achieving the accurate PDP in some scenarios, a suboptimal but robust solution is further developed. Compared with peak detecting, the suboptimal solution lowers the BS's target received power by over 1.1dB for Format 0 PRACH with both 2 and 4 receive antennas in the ETU channel, significantly reduces the user's power consumption and improves the LTE PRACH coverage.

This paper analyzes the interrelationship between the spectral efficiency (SE) and energy efficiency (EE) of cellular networks where the base stations (BSs) are arbitrarily distributed. The SE and EE performance metrics are assessed subject to a downlink transmission outage constraint when operating in interference limited operational environments. The network EE is expressed in closed-form as a function of SE, based on which the performance bounds of the network are determined. It is found that there exists an operating regime for which both SE and EE can be increased up to the optimal density of BSs concurrently utilizing the shared spectrum while satisfying the outage constraint. The SE-EE tradeoff regime is found to be applicable only when certain conditions related to the outage objective and the BS density are satisfied. Furthermore, for a given outage constraint it is shown numerically that, by carefully tuning the signal to interference ratio (SIR) threshold, it is possible to balance and improve both SE and EE performance.

This paper studies the performance of random access backoff algorithm in Long Term Evolution (LTE) system, taking the physical loss into account. Analytical results are developed for system performance metrics including throughout, drop probability and medium access delay. All these analytical results are verified with simulation. Performance of the backoff algorithm under different parameter settings is compared in a typical scenario of LTE. The performance results provide useful insights on optimal backoff parameter settings. For example, under a physical loss probability of 1%, the optimal backoff window size should be set to 1 and the attempt limit set to 2 or 3.

The objective of improving energy efficiency in Wireless Sensor Networks (WSNs) is a primary design requirement given the limited energy available in the battery powered sensor nodes. In this paper, a battery aware distributed clustering and routing protocol is proposed for the maximization of the network lifetime. The proposed protocol incorporates the state of the battery's remaining charge and health parameters in computing the charge utility metric at each cluster formation round. The performance of the proposed protocol is assessed via analysis and simulations and the gains are quantified using common energy related metrics. The numerical results demonstrate that the proposed approach is able to effectively increase the network lifetime significantly when compared to other distributed flat and hierarchical based routing protocols. The implication of the findings is that, by carefully harnessing the information pertaining to the nodes' battery condition at the network layer, it is feasible to realize major improvements in energy efficiency of the WSN.

This paper focuses on the problem of queue-aware resource allocation for OFDMA cellular systems supporting both real-time and non-real-time services. A set of practical algorithms is proposed to solve the problem of maximizing the system utility under the constraints of both system resources and the actual amounts of data to be transmitted in buffers. Moreover, an upper bound is devised to quantify the global maximum of the overall system utility, and to appraise the proposed practical algorithms. Numerical results verify the efficacy of the upper bound and demonstrate the excellent performance of the proposed algorithms in solving queue-aware resource allocation problems.

This paper considers the problem of power allocation for cooperative communication with multicast in coded wireless sensor networks (WSNs). We formulate and solve the problem using a cross-layer optimization framework, in which a simplified cooperative communication scheme that does not require channel state information (CSI) is considered. When compared against conventional cooperative and non-cooperative communication schemes under the same packet error rate (PER) objectives, the performance analysis shows that the simplified cooperative communication scheme achieves the lowest power cost. We then combine the simplified cooperative communication scheme with multicast using a linear optimization model, and show that for a random network topology, up to 40% power saving is achieved under stringent PER objectives.

This paper addresses the resource allocation problem in Orthogonal Frequency Division Multiple Access (OFDMA)-based wireless networks. The resource allocation problem is posed as an optimization problem with individual user constraints. This formulation provides a special structure that lends to efficient solution of the problem. We develop an optimal algorithm based on standard graph theory and Lagrangian relaxation. Based on the special structure of the problem, the proposed resource allocation algorithm attains the optimal solution at a much lower complexity compared to general-purpose optimization algorithms used by previous OFDMA resource allocation approaches. Moreover, the resource allocation problem solved by the proposed algorithm supports practical features such as discrete modulation set and multiple OFDM symbols per resource allocation decision. Furthermore, by assuming even power allocation across the OFDM subchannels, a suboptimal resource allocation algorithm with lower complexity is developed. The proposed algorithms enable the system designer to control the tradeoffs among system performance, system complexity, and the quality of service (QoS) experienced by the users. Extensive simulations are conducted to evaluate the performance and complexity of the proposed algorithms under different system operating conditions.

The effort dedicated to the ultimate goal of designing an architectural framework for tactical mobile ad hoc networks (MANETs) has long been hindered by the excessive control overhead of routing protocols to maintain a full view of the network topology. This paper explores the viability of adopting network coding as a means of improving reliable data delivery while lowering control overhead in large scale MANETs. Simulation results show that the proposed protocol, Network Coded STORM, achieves in excess of 95% packet delivery ratio and reduces the overall overhead by more than 50% in scalability experiments.

Future wireless communication systems will be comprised by the integration of different radio access technologies (RATs), referred to as heterogeneous wireless network (HWN). In this paper, a realistic HWN simulator is proposed to evaluate different network selection algorithms, the main mechanism of joint radio resource management (JRRM). The simulator is designed for an HWN that comprises cellular and wireless local area network radio access technologies. Here, the simulator architecture and the assumed system model are illustrated. Using the simulator, the performance results of the network selection algorithms are generated and then compared against analytical results. The outputs from the simulator provide valuable insights for the design of HWNs.

In this paper, a heuristic approach is proposed to solve the problem of supporting both energy efficiency and quality of service (QoS) in time division multiple access (TDMA) based wireless sensor networks (WSNs). The proposed approach consists of a linear cross-layer optimization model and a slot reuse based TDMA scheduling algorithm, which are jointly inter-connected through a hop distance/slot reuse control parameter. Compared to existing approaches, both the analysis and simulation results show that the proposed approach provides more choices in deriving the optimal solution, which achieves significant power savings and competitive QoS support.

This paper studies the problem of energy consumption minimization in wireless sensor networks (WSNs) under uncertainty in sensor node generated traffic. It is important to consider traffic uncertainty because WSNs designed for surveillance and monitoring applications typically operate in event-driven and query-driven modes for which the node generated traffic changes from instant to instant. The problem is formulated as a two-stage stochastic linear program with recourse where the objective is to minimize the sum of the energy cost expended on the carried traffic and the average energy cost of blocked traffic, under the constraint of a fixed energy budget at each node. The stochastic programming formulation lends to the consideration of two approaches for handling the randomly generated traffic: a deterministic approach in which all future generated random traffic are constrained by the link flows determined for the nominal traffic, and a stochastic approach where the link flows are adapted as the node generated traffic changes. Numerical results show that, compared to the deterministic approach, the stochastic approach provides up to 10% savings in network-level energy consumption and this is achieved without a loss in received data quality. The network-level energy savings translate to a prolonged network lifetime.

In this paper, we propose a solution to the scheduling problem in clustered wireless sensor networks (WSNs). The objective is to provide network-wide optimized time division multiple access (TDMA) schedules that can achieve high power efficiency, zero conflict, and reduced end-to-end delay. To achieve this objective, we first build a nonlinear cross-layer optimization model involving the network, medium access control (MAC), and physical layers, which aims at reducing the overall energy consumption. We solve this problem by transforming the model into two simpler subproblems. Based on the network-wide flow distribution calculated from the optimization model and transmission power on every link, we then propose an algorithm for deriving the TDMA schedules, utilizing the slot reuse concept to achieve minimum TDMA frame length. Numerical results reveal that our proposed solution reduces the energy consumption and delay significantly, while simultaneously satisfying a specified reliability objective.

In this paper, we develop an efficient resource allocation algorithm for multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) networks that considers all OFDM symbols in the current frame. By recognizing the special structure of the optimization problem, we develop an efficient algorithm based on graph theory that has a complexity independent of the number of OFDM symbols. System simulations show that the proposed algorithm enhances the system throughput and the user's data rate.

In this paper, we propose an algorithm which is capable of forming reuse sets of links in time division multiple access (TDMA) based wireless sensor networks (WSNs) to gain less frame length. For each reuse set, we use linear programming to achieve optimal energy consumption. We further show how to apply this algorithm with end-to-end packet loss rate constraint. The major contribution of this paper is twofold. First, the proposed scheme gives an energy efficient way to support quality of service (QoS) requests such as delay (by reduced frame length) and end-to-end packet loss rate. Secondly, the relationships amongst energy consumption, frame length (thus delay) and packet loss rate in WSNs revealed in this paper can be utilized in designing WSNs with delay and packet loss constraints.

Video streaming over bandwidth constrained and highly unpredictable wireless environment has prompted innovative cross-layer design strategies in the literature. However, most of the existing cross-layer designs lack the experimental validation of their real-life performance capability. In this paper, we have developed a test-bed that implements a recently proposed cross-layer framework for video streaming applications over IEEE 802.11 based ad-hoc wireless networks. The test-bed allows joint adaptation of the transcoding rate (at the application layer) with the number of redundant forward error correction packets (at the data link layer). When the proposed framework is invoked, the experimental results show that the video transcoding rate is adapted according to the channel, resulting in a reduced number of lost video frames, which contributes to a better video quality.

This paper studies time division multiple access (TDMA) scheduling with both energy efficiency and optimized delay in clustered wireless sensor networks (WSNs). To achieve this goal, we first build a cross-layer optimization model for attaining network wide efficient energy consumption. We solve this model by transforming it into simpler sub-problems that can be solved using conventional methods. We then propose a TDMA scheduling algorithm based on the input derived from the cross-layer optimization model. The proposed algorithm utilizes the slot reuse concept, which significantly reduces the end-to-end latency in WSNs, while retaining the feature of energy efficiency. In addition, the proposed solution in this paper is applied to clustered WSNs. This feature facilitates the application of our approach in large size WSNs.

Quality of service (QoS) support for real-time applications (e.g., voice) in mobile ad hoc networks (MANETs) has drawn much attention from researchers nowadays. Currently, most of the efforts are only concentrated on the routing algorithms. This paper shows that the design of QoS routing algorithms should not be treated in isolation but must also consider the feature of medium access control (MAC) layer and QoS metric collection schemes. We investigate the feasibility and effectiveness of voice support in multi- hop IEEE802.11 MANETs using the QoS constrained ad hoc on demand distance vector (AODV) routing protocol. Three main findings are presented in this paper. First of all, we found that, using QoS constrained AODV routing in multi-hop IEEE802.11 MANETs, voice communication can only be supported in a very limited manner (i.e., in a network composed of around a hundred nodes with pedestrian speed or less). Secondly, our simulations in OPNET and analysis show that IEEE802.11 performs poorly in multi-hop MANETs due to its hidden node problem, and is the major obstacle of voice support. Thus, new MAC protocols or improvements at the physical layer are needed to support voice with QoS guarantee in MANETs. Thirdly, we found that QoS routing metrics play an important role in determining the performance of a QoS routing algorithm, which should be selected with caution.

We propose two efficient scheduling algorithms for OFDMA networks based on graph theory. The algorithms overcome many drawbacks of previously proposed algorithms such as assuming a single service scenario, limiting the QoS parameters to instantaneous data rate and considering a single OFDM symbol in each scheduling decision. Also, our algorithms enable the system designer to control the tradeoff between system performance, system complexity and the QoS experienced by the users. System simulations show that the proposed algorithms considerably enhance the system performance.

This paper proposes and analyzes a Quality of service enhanced Base station Controlled Dynamic Clustering Protocol (QBCDCP), suitable for the support of video and imaging traffic over resource constrained wireless sensor nodes. The protocol achieves energy efficiency through a rotating head clustering approach and delegation of energy-intensive tasks to a high-power base station, while providing quality of service (QoS) support by including delay and bandwidth parameters in the route selection process. A Time Division Multiple Access (TDMA) scheme is used for intra- and intercluster communication, providing bandwidth reservation. Performance of QBCDCP is evaluated in terms of energy consumption and end-to-end image delay via analytical and discrete-event simulation techniques. Numerical results provide insights on the selection of network parameters such as number of clusters that improve the sensing node lifetime while maintaining high quality of service. The results also demonstrate the trade-off between end-to-end image delay and sensor node lifetime.

In this paper, we evaluate the performance of adaptive algorithms for selecting the number of redundant packets by jointly considering the forward error correction (FEC) and automatic repeat request (ARQ) mechanisms under Gilbert-Elliot (GE) wireless channel. Joint FEC and ARQ mechanisms are applied to video streaming for recovering the transmission errors. It is shown that the proposed heuristic algorithms achieve an improvement in the error recovery process under long burst of errors and in situations where error probability is high. System efficiency and gains in peak-signal-to- noise-ratio (PSNR) are compared, using NS2 based simulations. It is found that the proposed adaptive heuristic algorithms outperform other existing adaptive schemes in terms of system efficiency and PSNR gains. The results show that the proposed algorithms can perform well in wireless channels where long bursts of errors are common phenomena.

Support of real-time applications in Mobile Ad Hoc Networks (MANETs) is very challenging due to the dynamic characteristics of such networks. This paper studies the problem of voice application support in multi-hop IEEE802.11 ad hoc networks. We propose reactive-based and non-intrusive methods for calculating the quality of service (QoS) metrics of bandwidth, delay and packet loss that are important for voice support. Results from OPNET based simulations show that voice application can be supported in IEEE802.11 MANETs only under light traffic. When the network traffic is heavy, the calculated delay and packet loss become significantly high due to the hidden node problem, thus making it difficult to satisfy the voice delay and packet loss objectives in multi- hop IEEE802.11 MANETs.

In this paper, the problem of selecting an appropriate number of forward error correction (FEC) packets under given channel and network conditions is formulated as an optimization problem and two heuristic algorithms are proposed to find the solution. These heuristic algorithms are used for selecting the parameters of FEC scheme under a given maximum number of automatic repeat request (ARQ) retries, to achieve higher visual quality and efficient transport of multimedia applications over an IEEE 802.11 wireless channel. The proposed algorithms are: adaptive linear FEC (ALFEC) and adaptive exponential FEC (AEFEC), where the number of FEC packets respectively decreases linearly and exponentially, as the sender's queue increases. Simulated performance of the proposed algorithms is compared against those for the existing static and dynamic FEC generation schemes. It is found that the proposed heuristic algorithms outperform the other existing schemes in terms of system efficiency and provides comparable peak-signal-to-noise-ratio (PSNR) gain.

A two-level hierarchical clustering based hybrid routing protocol (THCHP) for wireless sensor networks is proposed in this paper. The per frame time average energy dissipation performance of THCHP is analytically evaluated. The accuracy of the derived analytical expression is validated via comparisons with simulation results. Furthermore, we compare the performance of THCHP to the previously proposed APTEEN protocol. Our results show that THCHP achieves significant energy savings when compared to APTEEN. In addition, comparisons between the average query response times of THCHP and APTEEN show that the proposed THCHP scheme achieves a much lower query response time and can operate over a broad range of query arrival rates with minimal increases in response time.

In this paper, we propose a novel and efficient OFDMA radio resource management approach based on standard Linear Programming theory. Our approach overcomes the drawbacks of previously proposed algorithms by considering multiple service scenarios, multiple quality of service (QoS) parameters and use of multiple time slots per frame. Further, the algorithm has a considerably low complexity and is shown to be a generalization of many previously proposed algorithms. System simulations show that by using the proposed approach, the system performance is considerably enhanced.

User distribution and mobility behaviour vary based on environment types and characteristics. Heterogeneous wireless networks (HWNs) are deployed to utilise these characteristics and serve users with better quality. For efficient resource management in HWN environment, an understanding of multi-mode user mobility behaviour is paramount. Here, a multi-mode user mobility model is proposed in the context of wireless local area network (WLAN) coverage in the hotspot, overlaid on a macrocell of wireless wide area network (WWAN). An expression for microcell residence time of multi-mode users in HWNs is derived, based on the cell residence time in the constituting WLAN and WWAN. The boundary-crossing probabilities of moving into microcell, moving out of microcell and moving out of macrocell during a call for different types of hotspot topologies are also derived analytically. The numerical results obtained using the analytical expressions for boundary-crossing probability are validated by simulation results. The significance of the proposed mobility model is demonstrated through its application in common radio resource management (CRRM). Numerical results show that the mobility-based CRRM scheme exhibits a lower rate of unnecessary vertical handoffs than that achieved by the ‘WLAN ‘if coverage’ scheme that does not use mobility information for resource management.

Smart homes have enjoyed increasing popularity in recent years. In order for them to further expand their market share, users need to be able to fully control devices. Policies are one way for users to achieve such flexible control of devices. However, user policies often tend to interact in unwanted ways leading to unexpected behavior of devices. This paper describes the design of a run-time policy interaction management module (PIMM) that serves as manager for detecting and resolving interactions between user policies in KNX-based smart homes. This module extends the traditional KNX networking system with the ability to manage policy interactions. The module operates in the run-time S-mode of the KNX network and works as part of the engineering tool software (ETS) used to configure and control the operation of the KNX network in smart homes. The proposed module serves as the first of its kind that can be implemented inside the KNX networking system to detect and resolve unwanted policies interactions.

This paper proposes a quality of service (QoS)-enhanced routing protocol for wireless sensor networks. The suggested protocol is based on a rotating head clustering approach, and incorporates QoS metrics estimation and efficient resource allocation to allow the support of video and imaging traffic over resource constrained nodes while extending the overall network lifetime. Performance of the proposed protocol is evaluated using analytical techniques. Numerical results provide insights on the selection of network parameters that improve overall network lifetime while maintaining high quality service.

A major challenge of the heterogeneous wireless networks is how to jointly utilize the resources of different radio access technologies (RATs) in an efficient manner while simultaneously achieving the desired multi-service quality of service (QoS) and minimizing the service cost. To meet the challenge, this paper proposes an adaptive common radio resource management (CRRM) scheme in the context of wireless wide area network (WWAN) and wireless local area network (WLAN). The key parameters of the scheme are service type, user mobility and location information, load information, and service cost. The effectiveness of the proposed CRRM scheme is assessed analytically using the theory of Markov chains. Numerical results show that the proposed scheme minimizes the rate of unnecessary vertical handoffs (VHO), thereby providing stable communication without degrading call blocking performance and increasing service cost.

The draft IEEE 802.11e standard aims at providing quality of service (QoS) support in 802.11 wireless local area networks (WLANs). Enhanced distributed coordination function (EDCF), being the fundamental medium access control mechanism in IEEE 802.11e, can only provide service differentiation but offers no QoS guarantees. While service differentiation does not perform well under high traffic load conditions, call admission control (CAC) becomes necessary in order to provide and support the QoS of existing calls. In this paper, we first analyze the saturation throughput and mean access delay performance of differentiated service provided by EDCF. Specifically, we investigate the impact of transmission opportunity (TXOP) and wireless channel errors on the performance of the EDCF. Based on the results from this analysis, we propose a new CAC algorithm that provides the desired throughput and access delay performance. Simulated performance results demonstrate the effectiveness of the proposed CAC algorithm

In this paper, a user-aided adaptive TDMA (UAAT) algorithm is developed for real-time services to improve the system efficiency of OFDM based cellular networks. In the UAAT scheme, a subchannel is shared by multiple users adaptively in a TDMA manner based on their instantaneous channel conditions. The system efficiency is improved dramatically by applying adaptive modulation schemes to all real-time users and using each subchannel more efficiently. The performance of the proposed algorithm is evaluated and compared with that of other resource allocation schemes. Results show that the proposed algorithm provides up to 100% improvement in the system efficiency or, equivalently, the user capacity of OFDM based cellular networks.

Ensuring quality of service (QoS) for high bandwidth applications over wireless networks is a challenging research problem. Accurate modeling of the wireless TCP connection arrival process aids in designing QoS algorithms. In this paper we perform an extensive statistical analysis of collected wireless TCP connection arrival process. Unlike wired TCP connection arrivals which are readily modeled as a heavy-tailed Weibull renewal process, we show that more sophisticated models are needed for wireless. Our proposed model, a multinomial canonical cascade with 3 stages, is able to capture the long- range dependent and multifractal characteristics of wireless TCP connection interarrival times. In addition, the model gives good approximation to the packet loss rate of measured wireless traffic trace.

Multimode reconfigurable terminals can route through or receive traffic stream from co-operating heterogeneous systems, simultaneously or individually, according to the restrictions and advantages of each system, e.g., wireless wide area network (WWAN) supports wide coverage but with limited data rate. In contrast, wireless local area network (WLAN) provides high bandwidth but only in "hot spot" areas. The objective of this paper is to propose a joint admission control and scheduling scheme based on fairness, available bandwidth and user mobility characteristics in the context of cdma-2000 and IEEE 802.11 wireless technologies. The aim is to provide, at a minimum, the basic data service for all admitted calls routed through any of the co-existing radio technologies. Vertical channel sharing approach is also included in the scheme. The analytic and simulation results show that new call blocking probability is reduced with the proposed scheme. The results also show that a considerable number of active users use the two radio interfaces at the same time, thus improving the communication quality

In this paper, we analyze six major security properties of EAP (Extensible Authentication Protocol) methods and employ SVO (Syverson van Oorschot) logic based formal approach to evaluate those properties. We present a detailed analysis and comparative assessment of the three dominant EAP-based authentication protocols. The results of analysis are useful to WLAN designers in selecting a suitable authentication method for implementation.

In this paper, we first analyze the saturation throughput and mean access delay performance of differentiated service provided by enhanced distributed coordination function (EDCF). Specifically, we investigate the impact of transmission opportunity (TXOP) and wireless channel errors on the performance of the EDCF. Based on the results from this analysis, we propose a new call admission control (CAC) algorithm that provides the desired throughput and access delay guarantees. Simulated performance results demonstrate the effectiveness of the proposed CAC algorithm

In this paper, two co-channel diversity schemes are proposed to improve the reception quality for the users in an orthogonal frequency division multiplexing (OFDM) based cellular system with one-cell frequency reuse. In these schemes, the same subchannel in 2 or 3 contiguous cells is assigned simultaneously to one user near the boundaries of these cells. The proposed schemes enhance the desired signal effectively and reduce the co-channel interference simultaneously, hence improving the user's reception quality significantly. The performance of the proposed schemes is evaluated and compared with that of the subchannel diversity (SD) and single frequency diversity (SFD) schemes. Results show that, for the users near the cell boundaries, the proposed schemes improve the reception quality significantly and outperform the SD and SFD schemes remarkably

In this paper, we study the performance of a combined guard channel and channel reservation with queueing resource management scheme for efficient handling of handoff calls in a macro-/micro-cell hierarchical cellular network. To minimize the forced call termination probability during the handoff process, guard channels are introduced at each microcell, along with channel reservation and queueing of handoff calls at the macrocell. Performance analysis of the combined guard channel and channel reservation with queueing scheme in an hierarchical cellular structure is performed using analytical and simulation techniques. Numerical results show that the proposed scheme reduces forced call termination probability and increases resource utilization with minimal call queueing delay.

This paper proposes a new soft handoff-like rerouting (SHLR) scheme for handoff of quality of service (QoS) based multimedia connections in wireless asynchronous transfer mode (ATM) networks and evaluates its performance. The main idea behind the SHLR scheme is the use of a make-before-break concept at the radio and network links to minimize latency and cell loss ratio during the handoff process. Performance of the SHLR scheme is evaluated using a discrete-event simulation technique to determine cell queueing delay and cell loss ratio. Results from simulation indicate that the cell queueing delay obtained for the SHLR scheme is better than that of existing schemes in the literature by a factor of at least 2. Further, the SHLR scheme provides up to two orders of magnitude improvement in cell loss ratio performance over that of the existing schemes. The results presented in the paper are useful for dimensioning buffer size as a function of QoS requirements and handoff traffic. The results also provide insights on the underlying causes of cell loss in wireless ATM networks.

The effects of the message length statistics on the mean message response time and message and packet access delays are investigated for variable-length multipacket messages transmitted over CSMA-CD (carrier-sense multiple-access with collision detection) local area networks. The steady-state distribution of the number of messages in the system is determined using the imbedded Markov chain technique and limiting results from renewal theory. Application of Little's law then yields the mean message response time, from which the mean message and packet access delays are determined easily due to the gated transmission strategy adopted. Sample numerical results are presented for geometrically and truncated Poisson-distributed message lengths. It is concluded from the numerical results that the access delays as well as the mean response time are sensitive not only to the message length statistics but also to the message length distribution and the node buffer size.>