Channel Access Research Articles

A Dueling Deep Recurrent Q -Network Framework for Dynamic Multichannel Access in Heterogeneous Wireless Networks

This paper investigates a deep reinforcement learning algorithm based on dueling deep recurrent Q -network (Dueling DRQN) for dynamic multichannel access in heterogeneous wireless networks. Specifically, we consider the scenario that multiple heterogeneous users with different MAC protocols share multiple independent channels. The goal of the intelligent node is to learn a channel access strategy that achieves high throughput by making full use of the underutilized channels. Two key challenges for the intelligent node are (i) there is no prior knowledge of spectrum environment or the other nodes’ behaviors; (ii) the spectrum environment is partially observable, and the spectrum states have complex temporal dynamics. In order to overcome the aforementioned challenges, we first embed the long short-term memory layer (LSTM) into the deep Q -network (DQN) to aggregate historical observations and capture the underlying temporal feature in the heterogeneous networks. And second, we employ the dueling architecture to overcome the observability problem of dynamic environment in neural networks. Simulation results show that our approach can learn the optimal access policy in various heterogeneous networks and outperforms the state-of-the-art policies.

Artificial neural network assisted polling scheme for central coordinated low-latency WLANs

Focusing on uplink transmission , a low-latency medium access control scheme is proposed and investigated for central coordinated wireless local area networks (WLANs). By categorizing user-stations using a trained artificial neural network and avoiding polling empty user-stations during the polling period, effective polling of user-stations can be realized and network latency can be reduced. The artificial neural network is trained by samples collected from a request-based polling scheme, and the boundary for categorization decisioning is selected according to the calculated outputs of training samples. Compared to the conventional controlled channel access scheme, the proposed scheme achieves up to 32.9% reduction in network latency, and significantly improves the network capacity in delivering services requiring 200 μs average latency . Moreover, to improve service quality for user-stations delivering mission-critical services, a latency guarantee mechanism is incorporated. The incorporated scheme records the last access time for each of these user-stations and requires these user-stations to report their buffer statuses when they are in danger of exceeding their latency targets. Simulation results show that the incorporated scheme can ensure a delay below 500 μs for 99.9% of packets of these user-stations, enabling a WLAN to realize ultrahigh quality low-latency performance.

Deep Reinforcement Learning-Based Multichannel Access for Industrial Wireless Networks With Dynamic Multiuser Priority

In Industry 4.0, massive heterogeneous industrial devices generate a great deal of data with different quality of service requirements, and communicate via industrial wireless networks (IWNs). However, the limited time-frequency resources of IWNs cannot well support the high concurrent access of massive industrial devices with strict real-time and reliable communication requirements. To address this problem, a deep reinforcement learning-based dynamic priority multichannel access (DRL-DPMCA) algorithm is proposed in this article. Firstly, according to the time-sensitivity of industrial data, industrial devices are assigned with different priorities, based on which their channel access probabilities are dynamically adjusted. Then, the Markov decision process is utilized to model the dynamic priority multichannel access problem. To cope with the explosion of state space caused by the multichannel access of massive industrial devices with dynamic priorities, DRL is used to establish the mapping from states to actions. Next, the long-term cumulative reward is maximized to obtain an effective policy. Especially, with joint consideration of the access reward and priority reward, a compound reward for multichannel access and dynamic priority is designed. For breaking the time correlation of training data while accelerating the convergence of DRL-DPMCA, an experience replay with experience-weight is proposed to store and sample experiences categorically. Besides, the gated recurrent unit, dueling architecture and step-by-step <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> -greedy method are employed to make states more comprehensive and reduce model oscillation. Extensive experiments show that, compared with slotted-Aloha and deep Q network algorithms, DRL-DPMCA converges quickly, and guarantees the highest channel access probability and the minimum queuing delay for high-priority industrial devices in the context of minimum access conflict and nearly 100% channel utilization.

A Collision-Avoided MAC Protocol With Time Synchronization and Power Control for Underwater Sensor Networks

In recent years, there has been a great deal of interest in underwater sensor networks (UWSNs) because they can effectively monitor the submerged environment. However, due to the long delay, high bit error rate, and limited propagation bandwidth of underwater acoustic channels, the media access control (MAC) protocol cannot function effectively in underwater acoustic sensor networks, particularly in offshore waters. To solve these issues, an MAC protocol with time synchronization and power control (TSPC) is proposed for a particular scenario. In addition, the proposed protocol is a joint channel access scheme, and the central nodes are deployed on an offshore seabed, which can be used as a synchronization reference standard time. A power control strategy is also implemented to limit the communication range of nodes. In brief, integrating TSPC with layered concepts and distributed clustering algorithms can effectively prevent communication collisions and reduce the energy consumption of UWSNs. The simulation results indicate that the TSPC MAC protocol performs well with regard to packet transmission rate, synchronization error, collision-free operation, and energy consumption.

A channel selection game for multi-operator LoRaWAN deployments

With over 75 billion Internet of Things (IoT) devices expected worldwide by the year 2025, inaugural MAC layer solutions for long-range IoT deployments no longer suffice. LoRaWAN, the principal technology for comprehensive IoT deployments, enables low power and long range communications. However, synchronous transmissions on the same spreading factors and within the same frequency channel, augmented by the necessary clustering of IoT devices, will cause collisions and drops in efficiency. The performance is further impacted by the shortage of radio resources and by multiple operators utilizing the same unlicensed frequency bands. In this paper, we propose a game theoretic based channel selection algorithm for LoRaWAN in a multi-operator deployment scenario . We begin by proposing an optimal formulation for the selection process with the objective of maximizing the total normalized throughput per spreading factor, per channel. Then, we propose centralized optimal approaches, as well as distributed algorithms , based on reinforcement learning and regret matching dynamics, to finding both the Nash and Correlated equilibria of the proposed game. We simulate our proposals and compare them to the legacy approach of randomized channel access, stressing their efficiency in improving the total normalized throughput as well as the packet delivery ratios .

Communication-Efficient Federated Learning Over MIMO Multiple Access Channels

Communication efficiency is of importance for wireless federated learning systems. In this paper, we propose a communication-efficient strategy for federated learning over multiple-input multiple-output (MIMO) multiple access channels (MACs). The proposed strategy comprises two components. When sending a locally computed gradient, each device compresses a high dimensional local gradient to multiple lower-dimensional gradient vectors using block sparsification. When receiving a superposition of the compressed local gradients via a MIMO-MAC, a parameter server (PS) performs a joint MIMO detection and the sparse local-gradient recovery. Inspired by the turbo decoding principle, our joint detection-and-recovery algorithm accurately recovers the high-dimensional local gradients by iteratively exchanging their beliefs for MIMO detection and sparse local gradient recovery outputs. We then analyze the reconstruction error of the proposed algorithm and its impact on the convergence rate of federated learning. From simulations, our gradient compression and joint detection-and-recovery methods diminish the communication cost significantly while achieving identical classification accuracy for the case without any compression.

Age of Information in Random Access Channels

In applications of remote sensing, estimation, and control, timely communication is critical but not always ensured by high-rate communication. This work proposes decentralized age-efficient transmission policies for random access channels with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> transmitters. We propose the notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">age-gain</i> of a packet to quantify how much the packet will reduce the instantaneous age of information at the receiver side upon successful delivery. We then utilize this notion to propose a transmission policy in which transmitters act in a decentralized manner based on the age-gain of their available packets. In particular, each transmitter sends its latest packet only if its corresponding age-gain is beyond a certain threshold which could be computed adaptively using the collision feedback or found as a fixed value analytically in advance. Both methods improve age of information significantly compared to the state of the art. In the limit of large <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> , we prove that when the arrival rate is small (below <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\frac {1}{eM}$ </tex-math></inline-formula> ), slotted ALOHA-type algorithms are order optimal. As the arrival rate increases beyond <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\frac {1}{eM}$ </tex-math></inline-formula> , while age increases under slotted ALOHA, it decreases significantly under the proposed age-based policies. For arrival rates <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta =\frac {1}{o(M)}$ </tex-math></inline-formula> , the proposed algorithms provide a multiplicative gain of at least two compared to the minimum age under slotted ALOHA (minimum over all arrival rates). We conclude that it is beneficial to increase the sampling rate (and hence the arrival rate) and transmit packets selectively based on their age-gain. This is surprising and contrary to common practice where the arrival rate is optimized to attain the minimum AoI. We further extend our results to other random access technologies such as Carrier-sense multiple access (CSMA).

Evaluation of microleakage and push-out bond strength of various composite resins for sealing the screw-access channel in implant-supported restorations

Statement of problemMicroleakage and loss of the composite resin sealing the screw-access channel are frequent complications of screw-retained implant-supported prostheses. How the screw-access channel should be best restored to reduce such complications is unclear. PurposeThe purpose of this in vitro study was to evaluate the microleakage and bond strength of 3 types of composite resins (flowable, packable, and bulk-fill) with or without a bonding agent treatment to seal the screw-access channel of 2 types of restorative materials (zirconia and Co-Cr alloy) with or without thermocycling. Material and methodsIn total, 240 yttria-stabilized tetragonal zirconia polycrystal (Y-TZP) specimens (IPS e.max ZirCAD) and 240 Co-Cr alloy (Vera PDS) specimens were prepared with a Ø3×3-mm cylindrical cavity at the center to simulate the screw-access channel. Three types of composite resins (flowable, packable, and bulk-fill resin) (Filtek Z350 XT Flowable Restorative, Filtek Z350 XT Universal Restorative, and Filtek One Bulk Fill Restorative) were applied to restore the access channel of the zirconia and Co-Cr specimens with or without a bonding agent (Single Bond Universal Adhesive). Microleakage and push-out bond strength were determined and compared by dividing the specimens into experimental groups with or without thermocycling (1000 times with 30 seconds at 5 ±2 °C and 55 ±2 °C). The results were analyzed by using a 1-way ANOVA and 4-way ANOVA. Adjustment for multiple comparisons was made with the Tukey Honestly Significant Difference (HSD) test. ResultsThe specimens subjected to thermocycling showed a lower bond strength (P<.001) and higher microleakage (P<.001) than specimens stored in a constant-temperature water bath. Specimens treated with bonding agents showed a higher bond strength (P<.001) and lower microleakage (P<.001) than specimens not treated with a bonding agent. Higher bond strengths were observed in the order of bulk-fill resin, packable resin, and flowable resin (P<.001). Packable resin showed higher microleakage than flowable resin and bulk-fill resin (P<.05). No significant difference in microleakage was found between the flowable resin and bulk-fill resin (P>.05). ConclusionsHigher bond strengths were observed in the order of bulk-fill resin, packable resin, and flowable resin. Less microleakage was observed in the flowable resin and bulk-fill resin than in the packable composite resin. Bonding agent treatment was effective in increasing bond strength and decreasing microleakage. Zirconia and Co-Cr showed a bond strength similar to that of composite resins, but zirconia showed higher microleakage than Co-Cr. Restoring the screw-access channel with the bulk-fill resin should increase bond strength and reduce microleakage.

Performance Analysis of Synchronous Multilink MAC Protocol with Automatic Repeat Request

Multilink operation (MLO) is considered a key candidate technique in 802.11be, which allows devices to transmit and receive data using multiple links concurrently, thereby contributing to improving throughput and reducing latency. However, the performance of the MLO scheme will gradually degrade as the channel environment deteriorates. To tackle this problem, in this paper, we propose a synchronous multilink media access control (MAC) protocol with Automatic Repeat Request (called SML-ARQ). With SML-ARQ, in the contention process, MLDs are allowed to perform channel access procedures over multiple links concurrently; in the transmission process, MLDs partition a link packet into multiple blocks and then transmit these blocks using multiple links. Any failed division of a link packet is copied and retransmitted once using multiple channels concurrently; as a consequence, SML-ARQ can mitigate the adverse impact of transmission failure on system performance. A theoretical model is being developed to analyze the performance of the proposed SML-ARQ. Extensive simulations verify the efficiency of SML-ARQ and the accuracy of our theoretical model.

Smartphone-Based Social Distance Detection Technology with Near-Ultrasonic Signal.

With the emergence of COVID-19, social distancing detection is a crucial technique for epidemic prevention and control. However, the current mainstream detection technology cannot obtain accurate social distance in real-time. To address this problem, this paper presents a first study on smartphone-based social distance detection technology based on near-ultrasonic signals. Firstly, according to auditory characteristics of the human ear and smartphone frequency response characteristics, a group of 18 kHz–23 kHz inaudible Chirp signals accompanied with single frequency signals are designed to complete ranging and ID identification in a short time. Secondly, an improved mutual ranging algorithm is proposed by combining the cubic spline interpolation and a two-stage search to obtain robust mutual ranging performance against multipath and NLoS affect. Thirdly, a hybrid channel access protocol is proposed consisting of Chirp BOK, FDMA, and CSMA/CA to increase the number of concurrencies and reduce the probability of collision. The results show that in our ranging algorithm, 95% of the mutual ranging error within 5 m is less than 10 cm and gets the best performance compared to the other traditional methods in both LoS and NLoS. The protocol can efficiently utilize the limited near-ultrasonic channel resources and achieve a high refresh rate ranging under the premise of reducing the collision probability. Our study can realize high-precision, high-refresh-rate social distance detection on smartphones and has significant application value during an epidemic.

Factors Driving Duration to Cross-Selling in Non-Life Insurance: New Empirical Evidence from Switzerland

Customer relationship management and marketing analytics have become critical for non-life insurers operating in highly competitive markets. As it is easier to develop an existing customer than to acquire a new one, cross-selling and retention are key activities. In this research, we focus on both car and household-liability insurance products and consider the time a customer owning only a single product takes before buying the other product at the same insurer. Based on longitudinal consumer data from a Swiss insurance company covering the period from 2011 to 2015, we aim to study the factors driving the duration to cross-selling. Given the different dynamics observed in both products, we separately study the car and household-liability insurance customer cohorts. Considering the framework of survival analysis, we provide descriptive statistics and Kaplan–Meier estimates along major customer characteristics, contract history and distribution channel usage. For the econometric analysis of the duration, we compare the results from Cox and accelerated failure time models. We are able to characterize the times related to the buying behavior for both products through several covariates. Our results indicate that the policyholder age, the place of residence, the contract premium, the number of contracts held, and the initial access channel used for contracting influence the duration to cross-selling. In particular, our results underline the importance of the tied agent channel and the differences along the geographic region and the urbanicity of the place of residence. By quantifying the effects of the above factors, we extend the understanding of customer behavior and provide a basis for developing models to time marketing actions in insurance companies.

EEM-MAC: Enhanced energy efficient mobility aware MAC protocol for mobile internet of things

Mobile wireless sensor networks (MWSNs) have become a foremost solution in many emerging applications both in industry and academia. Moreover, considering the mobile node in WSN is a challenging task to designing efficient communication protocols, specifically at a medium access control (MAC) layer. Most of the existing protocols consider only for static and slow mobility. To meet with future MIoT applications, in this paper we propose Enhanced Energy Efficient Mobility aware MAC (EEM-MAC) protocol. Our EEM-MAC protocol consists of 3 contributions i) static synchronization and mobility handling phase to support both environments, ii) By using queue length-based channel access priority for static nodes, and iii) the combined highest signal strength and node status-based channel access priority for mobile nodes without any control packet overhead. The simulation results verify that EEM-MAC yields a notable improvement in the average power consumption, packet latency, and packet delivery ratio performances against the well-known mobility-aware MAC protocols under different mobility models and environments.

Fuzzy-MAC: An FIS based MAC protocol for a multi-constrained traffic in wireless body area networks

In this paper, we introduce Fuzzy-MAC protocol to facilitate the uniform transmission of the multi-constrained data generated by the sensor devices in a wireless body area network (WBAN). A physiological sensor device allows us to monitor various health-related parameters. In the process, the sensed data become associated with diverse Quality of Service (QoS)-specific constraints depending on the application requirements, thus imposing a challenge in front of the enabling communication protocols such as IEEE 802.15.4. To deal with these multi-constrained traffic data, Fuzzy-MAC first categorizes the sensor data according to the application-specific QoS requirements. Moreover, improved channel access mechanisms are applied according to the priority class value assigned to the different data types. Lastly, employing a Fuzzy Inference System (FIS), Fuzzy-MAC brings context-awareness to a health monitoring environment. We conduct rigorous simulations of the proposed Fuzzy-MAC to justify the competence with the existing literature. The results show that the proposed MAC protocol outperforms the benchmarks in terms of throughput, average delay, and overall reliability of a WBAN.

An intelligent access algorithm for large scale multihop wireless networks based on mean field game

In a distributed wireless network with a large number of nodes, competitive access of nodes may result in the deterioration of throughput and energy. Therefore, in this paper we propose an intelligent access algorithm based on the mean field game (MFG). First, we formulate the competitive access process between nodes as a game Query ID="Q1" Text="Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary." process by a stochastic differential game model, which maximizes the energy efficiency of nodes and obtain the optimal behavior strategy while meeting the requirements of channel access. However, as the number of nodes increases, the dimension of the matrix used to characterize the interaction between nodes becomes too large, which increases the complexity of the solution procedure. Therefore, we introduce the MFG and the interaction between nodes can be approximately transformed into the interaction between nodes and the mean field, which not only reduces the complexity, but also reduces the computational overhead. In addition, the HJB-FPK equation is solved to obtain the Nash equilibrium of the MFG. Finally, a backoff strategy based on the Markov model is proposed, and the node obtains the corresponding backoff strategy according to the network situation and its own state. Simulation results show that the proposed algorithm has good performances on optimizing network throughput and energy efficiency for a large scale multi-hop wireless network.

A Usage Aware Dynamic Spectrum Access Scheme for Interweave Cognitive Radio Network by Exploiting Deep Reinforcement Learning.

Future-generation wireless networks should accommodate surging growth in mobile data traffic and support an increasingly high density of wireless devices. Consequently, as the demand for spectrum continues to skyrocket, a severe shortage of spectrum resources for wireless networks will reach unprecedented levels of challenge in the near future. To deal with the emerging spectrum-shortage problem, dynamic spectrum access techniques have attracted a great deal of attention in both academia and industry. By exploiting the cognitive radio techniques, secondary users (SUs) are capable of accessing the underutilized spectrum holes of the primary users (PUs) to increase the whole system’s spectral efficiency with minimum interference violations. In this paper, we mathematically formulate the spectrum access problem for interweave cognitive radio networks, and propose a usage-aware deep reinforcement learning based scheme to solve it, which exploits the historical channel usage data to learn the time correlation and channel correlation of the PU channels. We evaluated the performance of the proposed approach by extensive simulations in both uncorrelated and correlated PU channel usage cases. The evaluation results validate the superiority of the proposed scheme in terms of channel access success probability and SU-PU interference probability, by comparing it with ideal results and existing methods.

SafetyCrown: a patient-centered, fully digital concept for immediate implant restoration following the one-abutment/one-time concept—a pilot case series of a new treatment concept

IntroductionThe patient-centered SafetyCrown-workflow enables the immediate restoration of posterior missing teeth and short free-end situations following one-abutment/one-time within three visits and only one surgical approach. This prosthodontic rehabilitation aims to combine the advantages of cemented and screw-retained restorations.ReportThe concept has been performed with 4 restorations in 3 patients and followed up for up to 1 year (mean: 11.2 months) without technical and/or biological complication. Visit 1: Intraoral optical impression, CBCT, and tooth shade selection. Virtual implant planning is performed, and a surgical guide is printed. After exporting the planned implant position, a tooth-colored abutment is fabricated from zirconia with a 1-mm supragingival cementation line, adhesively bonded to a titanium base. Visit 2: Fully navigated implant placement with insertion of the definitive abutment. Subsequently, optical impressions are prepared for A: immediate restoration using a PMMA crown without functional contacts; B: definitive crown fabricated from monolithic zirconia and individualized. The localization of the screw channel is marked using stain thus permitting precise screw channel access, if necessary. Visit 3: After osseointegration of the implant, the definitive crown is adhesively cemented supragingival. In a retrospective analysis of PROMs (‘How stressful was the treatment process […]?’ (0 = not stressful at all, 100 = very stressful), mean VAS score for SafetyCrown of 14 (SD 11.7) and 29.8 (SD 23.1) for standard procedure were present.ConclusionThe SafetyCrown offers a shortened, patient-oriented concept for implant-supported single-tooth reconstructions omitting second-stage surgery. Clinical performance and hypothesized prosthodontic benefits require confirmation via an RCT.

New Bounds on Capacity Region of Multiple Access Channels in Visible Light Communications.

In this paper, we propose new inner and outer bounds of the capacity region for multiple access channels in visible light communication (VLC) networks under both peak and average optical power constraints. Specifically, the proposed inner bounds are established by employing the single-user capacity achieving input distribution for each user. The proposed outer bounds are derived by determining single-user capacities for each user and calculating a sum capacity upper bound by relaxing the input constraints. Numerical results show that the proposed new bounds are extremely tight and outperform existing bounds over wide ranges of SNRs.

A Multi-Class Channel Access Scheme for Cognitive Edge Computing-Based Internet of Things Networks

Edge computing-based framework is capable of improving users’ quality of experience in cognitive Internet of Things (IoT) networks. To explore the advantages of this edge computing-based framework, possible offloading and processing delay resulting from computation bottlenecks, and the offloading latency caused due to inter-cell interference must be properly considered. This paper thus considered a multi-class channel access mechanism for cognitive edge computing-based IoT networks where IoT users were categorized based on their quality of experience requirements. Essential IoT devices are permitted to offload to the edge server at any time following the hybrid channel access model, while delay-tolerant IoT devices are only permitted to offload to the server when the channel is idle following the overlay channel access model. Analyses were obtained for transmission rate and offloading delay to demonstrate the performance of the proposed mechanism, while important metrics such as total offloading latency and total offloading cost were investigated. The total offloading costs were formulated through the mixed strategy Nash equilibrium method. The proposed mechanism achieves lower offloading latencies and costs for both type 1 and type 2 CUs when compared with existing methods. The obtained results showed that multi-class channel access mechanisms can reduce packet offloading delay in cognitive edge computing-based IoT networks.

Mutli-Link Channel Access Schemes for IEEE 802.11be Extremely High Throughput

Multi-link Operation (MLO) was introduced into 802.11be as a promising technology to improve throughput and provide Quality of Services (QoS) guarantees. In this article, we start by introducing the multi-link transmission issues for MLO. Particularly, we focus on the channel access issue, and we present the recently proposed channel access schemes in the latest 802.11 draft. More importantly, we propose the novel channel access schemes under the three different scenarios to increase the link utilization. Finally, we conduct simulations to demonstrate that the multi-link transmission can effectively improve the throughput performance.

An analytical model for the directional hybrid MAC protocol in the IEEE 802.15.3c

Nowadays, mobile data demand has grown significantly. Millimeter-wave (mm-wave) bands are a favorable technology to support high data rate communication. The IEEE 802.15.3c standard is an attractive candidate for mm-wave carrier frequencies for short-range indoor applications. Providing a comprehensive analytical model of the medium access control (MAC) layer gives great information to improve network performance. In this paper, for the first time, a comprehensive Markov chain model has been presented for the IEEE 802.15.3c standard by considering the directional antenna , hybrid MAC and queue. The exact queue models ( M / G /1 and M / D /1 queue) have been considered for the Regular Contention Access Period (RCAP) and Channel Time Allocation Period (CTAP) areas. The number of beam sectors and path loss model for the directional antenna is considered in the presented model. The most essential analytical parameters including throughput, delay, reliability, queue size, and system instability of each RCAP and CTAP area for the different number of sectors, RCAP area length, nodes, traffic and room size, have been investigated. Maximum throughput has been determined for the optimum value of these parameters. OPNET simulation validates our represented analytical model and shows that it is more accurate than other states of the art articles.