Probability Of Successful Transmission Research Articles

Modeling and Analysis of Long-Term Average User Throughput in Mobile Ultra Dense Networks

In the existing literatures, the joint coverage probability is used to analyze the dependence in coverage probability over different time slots. However, these researches concentrate on the correlation caused by the stability of channels, and ignored the effect of the fluctuation caused by randomness from mobility and small-scale fading. In this letter, the exact expression of long-term average throughput is derived based on a novel analytical framework, which simultaneously calculate the average transmission success probability considering correlation and fluctuation descended from the joint coverage probability. Specifically, the fluctuation in network performance is characterized by incorporating the distance variation and signal attenuation of mobile users into the statistics of channel gains. In addition, the temporal correlation coefficient of the received signal is measured under the mobility model. Simulations are also conducted with the analysis results, which shows that the temporal correlation increases with increasing path-loss factor, the long-term average user throughput decreases with increasing mobility model factor.

Transmission capacity analysis of relay-assisted D2D cellular networks with M2M coexistence

Two trending technologies envisaged to provide huge prospects towards the realization of the fifth generation cellular systems are device-to-device (D2D) and machine-to-machine (M2M) communications. D2D facilitates direct connectivity between devices in proximity while M2M facilitates autonomous communication of smart devices. Since a massive number of M2M devices are set to be deployed in the future, this paper conceptualizes that M2M devices can relay data for D2D devices which are not in proximity. In this context, a probabilistic model is introduced for modelling the availability of M2M devices. Thorough investigations are made on transmission capacity (TC), TC gains and trade-offs involved with respect to both underlay and D2D-overlay modes. Using stochastic geometry, we derive the successful transmission probabilities for all associated links to determine the TCs in these scenarios and present computable expressions for the TC gains achieved. Furthermore, an exposition is provided on how the density and transmit power of MTDs in the network affect the D2D TC performance.

Optimal Caching Designs for Perfect, Imperfect, and Unknown File Popularity Distributions in Large-Scale Multi-Tier Wireless Networks

Most existing caching solutions for wireless networks rest on an unrealistic assumption that the file popularity distribution is perfectly known. In this paper, we consider optimal caching designs for perfect, imperfect and unknown file popularity distributions in large-scale multi-tier wireless networks. First, in the case of perfect file popularity distribution, we formulate the optimization problem to maximize the successful transmission probability (STP), which is a nonconvex problem. We develop an efficient parallel iterative algorithm to obtain a stationary point using parallel successive convex approximation (SCA). Then, in the case of imperfect file popularity distribution, we formulate the robust optimization problem to maximize the worst-case STP. To solve this challenging maximin problem, we transform it to an equivalent complementary geometric programming (CGP), and develop an efficient iterative algorithm which is shown to converge to a stationary point using SCA. To the best of our knowledge, this is the first work explicitly considering the estimation error of file popularity distribution in the optimization of caching design. Next, in the case of unknown file popularity distribution, we formulate the stochastic optimization problem to maximize the stochastic STP (i.e., the STP in the stochastic form), which is a challenging nonconvex stochastic optimization problem. Based on stochastic parallel SCA, we develop an efficient iterative algorithm to obtain a stationary point, by exploiting structural properties of the stochastic STP and making full use of instantaneous file requests. As far as we know, this is the first work considering stochastic optimization in a large-scale multi-tier wireless network. Finally, by numerical results, we show that the proposed solutions achieve significant gains over existing schemes in all three cases.

Cooperative packet-forwarding strategies in mobile ad hoc networks with unreliable channels: An evolutionary game approach

In mobile ad hoc networks, network nodes accomplish a target task usually by cooperative packet forwarding from the source to the destination. It is a challenge to enforce their mutual cooperation for a node’s self-interest. In this article, we focus on cooperative packet forwarding in a one-hop unreliable channel, which leads to packet loss and retransmission. We model the process of packet forwarding with the nodes’ remaining energy and reputation value. We propose a packet-forwarding non-cooperative game model reflecting the utilities of different packet-forwarding strategies, in which an incentive mechanism is introduced to enforce cooperation of packet forwarding. Furthermore, we analyze the packet-forwarding game with replicator dynamics and derive and prove three theorems. If the conditions of the theorems are met, the evolutionarily stable strategies can be attained. Three inferences also reveal how convergence speed to evolutionarily stable states is affected by the cooperative incentive, the probability of successful packet transmissions, and the upper limit of the retransmission number. The simulation results support the proposed theorems and inferences. In addition, we show that our game model with a reputation value and the mechanism of incentive cooperation can improve the probability of successful packet transmissions, and reduce the network overhead.

Harmonious Cross-Technology Coexistence With Heterogeneous Traffic in Unlicensed Bands: Analysis and Approximations

The dramatic growth in demand for mobile data has prompted mobile network operators (MNOs) to explore spectrum sharing in unlicensed bands. MNOs have been allowed recently to operate their LTE services over the 5 GHz Unlicensed National Information Infrastructure (U-NII) bands, currently occupied by Wi-Fi. The unlicensed LTE operation has been standardized by 3GPP under the name Licensed Assisted Access (LAA). Unlicensed 5G New radio (NR) operation over the U-NII bands, a.k.a., NR-Unlicensed (NR-U), is also being explored. To support applications with diverse Quality-of-Service (QoS) requirements, LAA, NR-U, and Wi-Fi technologies offer multiple priority classes with different contention parameters for accessing an unlicensed channel. How these different classes affect the interplay between coexisting MNOs and Wi-Fi systems is still a relatively under-explored topic. In this paper, we develop a simple yet efficient framework for fair coexistence between LTE MNOs and Wi-Fi systems, each with multiple priority classes. We derive approximated closed-form solutions for the probability of successful transmission (PST), average contention delay, and average throughput when coexisting LAA and Wi-Fi devices serve traffic with different priority classes. LTE and Wi-Fi operators can fit our solutions to estimate the performance of both systems in offline or online fashions, and use them to further optimize their system throughput and latency. Our results reveal that PSTs computed with our approximate models are within 5% difference with those obtained via close-to-real simulation under dense network deployments and high traffic loads.

Adaptive Deployment for UAV-Aided Communication Networks

Unmanned aerial vehicle (UAV) as an aerial base station is a promising technology to rapidly provide wireless connectivity to ground users. Given UAV's agility and mobility, a key question is how to adapt UAV deployment to best cater to the instantaneous wireless traffic in a territory. In this paper, we propose an adaptive deployment scheme for a UAV-aided communication network, where the UAV adapts its displacement direction and distance to serve randomly moving users' instantaneous traffic in the target cell. In our adaptive scheme, the UAV does not need to learn users' exact locations in real time, but chooses its displacement direction based on a simple majority rule by flying to the spatial sector with the greatest number of users in the cell. To balance the service qualities of the users in different sectors, we further optimize the UAV's displacement distance in the chosen sector to maximize the average throughput and the successful transmission probability, respectively. We prove that the optimal displacement distance for average throughput maximization decreases with the user density: the UAV moves to the center of the chosen sector when the user density is small and the UAV displacement becomes mild when the user density is large. In contrast, the optimal displacement distance for success probability maximization does not necessarily decrease with the user density and further depends on the target signal-to-noise ratio (SNR) threshold. Extensive simulations show that the proposed adaptive deployment scheme outperforms the traditional non-adaptive scheme, especially when the user density is not large.

Analysis and Optimization of Caching in Fog Radio Access Networks

Caching is a promising approach to address the backhaul traffic congestion problem and boost throughput in fog radio access networks. In this correspondence paper, we investigate a proactive probabilistic caching optimization in wireless fog radio access network where multiple users request different files from multiple base stations (BSs). To assess the performance, we first derive the analytical results of successful transmission probability (STP) using tools of stochastic geometry. With the derived closed-form STP expressions, our objective is to optimize the proactive probabilistic caching distribution to maximize the STP. To reduce the computational complexity, we specially discuss the optimization in high signal-to-noise ratio (SNR). We propose a projection gradient method to get a local optimal solution. Simulation results show that the caching placement optimized by our proposed algorithm increases the STP by about $18\%$ over the best existing caching distribution when ${\rm SNR}=10$ dB with user density $\lambda _u=0.09$ , BS density $\lambda _b=0.1$ and file number $N=100$ .

Distributed NOMA-Based Multi-Armed Bandit Approach for Channel Access in Cognitive Radio Networks

Usually, multiple secondary users (SUs) utilize the opportunistic spectrum access (OSA) in cognitive radio networks to communicate with the secondary base station (sBS). In uplink transmission, each SU can only select one channel from ${C}$ primary channels to sense, access, and transmit data packets with the constraints of hardware and energy. Distributed non-orthogonal multiple access-based multi-armed bandit (D-NOMA-MAB) approach is proposed to solve the problem of the channel selection and the power level selection, which alleviates the impact of the collision in the same channel. D-NOMA-MAB approach improves the energy efficiency of the system and the successful transmission probability obviously by introducing 2-D selection for the arms and the factor of energy efficiency in the policy index of the restless MAB problem.

Multi-Objective Optimization Assisted Network Condition Aware QOS-Routing Protocol for MANETS: MNCQM

The exponential rise in wireless communication systems and allied applications has revitalized academiaindustries to achieve more efficient data transmission system to meet Quality-of-Service (QoS) demands. Amongst major wireless communication techniques, Mobile Ad-hoc Network (MANET) is found potential to provide decentralized and infrastructure less communication among multiple distributed nodes across network region. However, dynamic network conditions such as changing topology, congestion, packet drop, intrusion possibilities etc often make MANET’s routing a tedious task. On the other hand, mobile network feature broadens the horizon for intruders to penetrate the network and causes performance degradation. Unlike classical MANET protocols where major efforts have been made on single network parameter based routing decision, this research paper proposes a novel Elitist Genetic Algorithm (EGA) Multi-Objective Optimization assisted Network Condition Aware QoS-Routing Protocol for Mobile Ad-hoc Networks (MNCQM). Our proposed MNCQM protocol exhibits two phase implementation where at first it performs node-profiling under dynamic network topology for which three factors; irregular MAC information exchange, queuing overflow and topological variations have been considered. Towards this objective node features like Packet Forwarding Probability (PFP) at the MAC layer, Success Probability of Data Transmission (SPDT) of a neighboring node, and Probability of Successful Data Delivery (PSDD) have been obtained to estimate Node-Trustworthiness Index (NTI), which is further used to eliminate untrustworthy nodes. In the second phase of implementation, a novel Evolutionary Computing assisted nondisjoint best forwarding path selection model is developed that exploits node’s and allied link’s connectivity and availability features to identify the quasi-sub-optimal forwarding paths. EGA algorithm intends to reduce hop-counts, connectivity-loss and node or link unavailability to estimate best forwarding node. One key feature of the proposed model is dual-supplementary forwarding path selection that enables alternate path formation in case of link outage and thus avoids any iterative network discovery phase.

Design of Multi-Carrier LBT for LAA&WiFi Coexistence in Unlicensed Spectrum

To relieve the shortage problem of licensed spectrum, the aggregation of licensed and unlicensed carriers has emerged as an attractive solution for opportunistically increasing the throughput of LTE systems while enhancing the utilization of unlicensed spectrum. As one of the representative aggregation technologies, LAA is in favor of the harmonious coexistence with WiFi systems by leveraging the LBT mechanism and different pre-defined access priorities. In this article, we present a general overview of the attractive benefits and technical challenges for aggregating licensed carriers with unlicensed carriers. We then introduce some fundamental features of the LBT based LAA, including transmission modes, ED threshold, random backoff within the contention window, and access priority classes. Under multiple unlicensed channels, we propose two novel multi-carrier LBT mechanisms to improve the transmission behavior and coexistence performance of LAA and WiFi. It is verified in terms of successful transmission probability and carrier utilization rate that two proposed multi-carrier LBT mechanisms can achieve much better performance than the conventional counterpart using random carrier selection.

Network-Wide Throughput Optimization for Highway Vehicle-To-Vehicle Communications

This letter analyzes the meta-distribution of the transmission success probability (TSP) for vehicular networks based on a one-dimensional Poisson point process (PPP). We also propose a method to maximize the throughput across the vehicular network. Compared to the conventional spatial average performance assessment, the meta-distribution reveals the fraction of vehicles that operate at a target success rate of transmission across the highway. To this end, we propose a per-vehicle rate selection scheme to keep a target quality of service (QoS) level for all vehicles. The results reveal that operating at the spatially-averaged maximum throughput may lead to excessive variation in the performance of individual vehicles. However, with the proposed meta-distribution-aware rate selection scheme, the throughput variation among the vehicles can be significantly reduced (e.g., up to a 60 % reduction).

Analysis and Optimization of Caching and Multicasting for Multi-Quality Videos in Large-Scale Wireless Networks

Efficient dissemination of videos is an important problem for mobile telecom carriers. In this paper, to facilitate massive video dissemination, we study joint caching and multicasting for multi-quality videos encoded using two video encoding techniques, namely, scalable video coding (SVC) and HEVC or H.264 as in dynamic adaptive streaming over HTTP (DASH) respectively, in a large-scale wireless network. First, for each type of video, we propose a random caching and multicasting scheme, carefully reflecting the relationship between layers of an SVC-based video or descriptions of a DASH-based video. Then, for each type of video, we derive tractable expressions for the successful transmission probability in the general and high user density regions, respectively, utilizing tools from stochastic geometry. The analytical results reveal that in the high user density region, the marginal increase of the successful transmission probability with respect to the caching probability of a video with a certain quality reduces when the caching probability increases. Next, for each type of video, we consider the maximization of the successful transmission probability in the high user density region, which is a convex problem with an exceedingly large number of optimization variables. We propose a two-stage optimization method to obtain a low-complexity near-optimal solution by solving a relaxed convex problem and a related packing problem. The optimization results reveal the impact of the caching gain of a layer for an SVC-based video or a description for a DASH-based video on its caching probability. Finally, we show that the proposed solutions for SVC-based and DASH-based videos achieve significant performance gains over baseline schemes in the general and high user density regions, and demonstrate their respective operating regions, using numerical results based on real video sequences.

Economical Caching for Scalable Videos in Cache-Enabled Heterogeneous Networks

We develop the optimal economical caching schemes in cache-enabled heterogeneous networks, while delivering multimedia video services with personalized viewing qualities to mobile users. By applying scalable video coding (SVC), each video file to be requested is divided into one base layer (BL) and several enhancement layers (ELs). In order to assign different transmission tasks, the serving small-cell base stations (SBSs) are grouped into $K$ clusters. The SBSs are able to cache and cooperatively transmit BL and EL contents to the user. We analytically derive expressions for successful transmission probability and ergodic service rate, and then the closed-form expression for EConomical Efficiency (ECE) is obtained. In order to enhance the ECE performance, we formulate the ECE optimization problems for two cases. In the first case, with equal cache size equipped at each SBS, the layer caching indicator is determined. Since this problem is NP-hard, after the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm approximation, the discrete optimization variables are relaxed to be continuous, and this relaxed problem is convex. Next, based on the optimal solution derived from the relaxed problem, we devise a greedy-strategy based heuristic algorithm to achieve the near-optimal layer caching indicators. In the second case, the cache size for each SBS, the layer size, and the layer caching indicator are jointly optimized. This problem is a mixed integer programming problem, which is more challenging. To effectively solve this problem, the original ECE maximization problem is divided into two subproblems. These two subproblems are iteratively solved until the original optimization problem is convergent. Numerical results verify the correctness of the theoretical derivations. Additionally, compared to the most popular layer placement strategy, the performance superiority of the proposed SVC-based caching schemes is testified.

OCA-MAC: A Cooperative TDMA-Based MAC Protocol for Vehicular Ad Hoc Networks.

Cooperative communication is an effective method of improving the transmission performance for vehicular ad hoc networks. However, the rapid movement of vehicles leads to frequent changes in network topology and reduces the probability of successful data transmission on the medium access control (MAC) layer. In this paper, we propose an Optimal Cooperative Ad hoc MAC protocol (OCA-MAC) based on time division multiple access (TDMA). OCA-MAC utilizes multiple cooperative nodes to forward data, so as to improve the probability of successful data transmission. It chooses cooperative nodes according to direct successful transmission probability, communication range between potential helper node and destination node, and available time slot. Meanwhile, in order to avoid excessive transmission redundancy caused by multiple cooperative forwarding, the optimal number of cooperative forwarding nodes is obtained through analysis of a probabilistic model. Simulation results show that OCA-MAC improves the successful data transmission rate and reduces the number of transmission times and transmission delay compared to the multichannel TDMA MAC protocol (VeMAC) and the cooperative ad hoc MAC protocol (CAH-MAC).

Slot Jamming Effect and Mitigation Between LTE-LAA and WLAN Systems With Heterogenous Slot Durations.

To improve spectrum sharing between long-term evolution (LTE) license assisted access (LAA) and incumbent systems such as wireless local area networks (WLANs) in unlicensed spectrum, listen before talk (LBT) has been proposed as a candidate for LAA channel access. To allow for a robust spectrum sensing performance, LBT may use a backoff-slot duration that is substantially larger than its WLAN counterpart. There is potential for an unknown backoff slot-jamming (SJ) effect, which may significantly decrease channel access probability (CAP) and throughput of LAA-LBT links. In this paper, we study the SJ effect and propose an effective anti-SJ (ASJ) LBT scheme. To gain theoretical insight, we develop a new performance analysis approach on coexisting systems with different slot durations. We model the LAA backoff process with super-counters, provide an in-depth analysis of the backoff process, and derive key performance indicator (KPI) statistics. These KPIs include backoff hold time, successful transmission probability, CAP, and throughput. Simulation results thoroughly validate our analytical results, and show that the ASJ-LBT scheme is effective in mitigating the SJ effect. These results fill a major technical gap in spectrum sharing research and may be extended to support system optimization and coexistence analysis of other heterogeneous systems.

Cooperative Learning for Spectrum Management in Railway Cognitive Radio Network

High-speed rail public broadband wireless access requires communication terminals with high mobility to perform seamless cell switching in a harsh and fast time-varying environment. However, frequent switching caused by fast motion inevitably leads to unpredictable high volatility of the available spectrum, resulting in inefficient wireless communication. The application of highly mobile cognitive radio technology may address this challenge. This paper first analyzes the physical structure of the existing railway wireless communication network, and determines the characteristics of chainlike distribution and cascade operation of the base stations along the railway. Furthermore, using Bayesian reinforcement learning and multi-agent theory, a distributed cognitive base station model suitable for railway wireless environment is constructed. Based on the joint spectrum management of cascaded base station groups, this model proposes a multi-agent coordination algorithm with the goal of global optimal communication performance of the entire rail line. Finally, this paper evaluates the network performance of various test scenarios, and proves that the cognitive base station multi-agent cascade cooperative system can significantly increase the probability of successful data transmission and greatly reduce wireless spectrum handovers. This proposed scheme provides a brand-new solution for solving the problem of low wireless spectrum efficiency in the typical railway wireless network.

An Enhanced Random Access With Preamble-Assisted Short-Packet Transmissions for Cellular IoT Communications

We propose an enhanced random access (RA) with preamble-assisted short-packet transmissions to support cellular Internet-of-Things (IoT) communications. A key feature of the proposed scheme is that the base station (e.g., eNodeB in LTE networks) utilizes the RA preambles as uplink reference signals to estimate uplink channel state information of IoT devices, which subsequently provides additional opportunities to transmit short packets during the RA procedure without extra signaling (e.g., connection and scheduling requests). Through simulations, we evaluate the performance of the proposed scheme in terms of a channel estimation mean squared error, a bit error rate, a preamble collision probability, and a success probability of short-packet transmissions. Results show that the proposed scheme can support reliable and low-latency featured short-packet transmissions during the RA procedure by efficiently incorporating both multiple antenna and detection techniques.

Optimal secret sharing for wireless information security in the era of Internet of Things

Secret sharing is crucial to information security in wireless communications in the era of Internet of Things. In this paper, we study how to optimally share secrets between two and more users using the effect of wireless channel dynamics on the data link layer. For the two-user case, we formulate an optimization problem whose objective is to minimize the expectation of the probability that an eavesdropper receives all secret sharing packets. Our contributions are as follows: (i) we come up with the secret sharing mechanism that minimizes the aforementioned objective function and (ii) we perform analysis on our approach and derive the worst-case probability of the eavesdropper receiving all secret sharing packets. For the multi-user case, we focus on the scenario that three users within each other’s communication range try to establish a common secret. A novel multi-user secret sharing mechanism that allows all three users to contribute toward the common secret is developed and analyzed. Our approach has two nice properties: (i) it is able to reset itself in case of packet transmission error and (ii) its secret sharing efficiency is high when the probability of packet transmission success is not low. Our results are validated via simulations.

Slotted Secondary Transmission With Adaptive Modulation and Coding Under Interweave Cognitive Radio

In this paper, we propose a discrete-time Markov model to characterize slotted secondary transmission process under interweave cognitive implementation. Stationary distribution is solved in closed form to evaluate SU-to-PU collision probability and SU successful transmission probability. Assuming that SU transmission adopts adaptive modulation and coding (AMC), we then carry out the queuing analysis in terms of throughput, queuing delay, and packet drop probability, based on a two-dimensional finite-state Markov chain for small-size packet transmission. The optimal length of secondary slot is solved by maximizing throughput subject to an SU-to-PU collision probability constraint. For large-size packet transmission, the probability mass function of extended delivery time for secondary packet transmission is also derived. Selected numerical results are presented to illustrate the mathematical formulas and to validate our research results.

Optimization issues for data rate in energy harvesting relay-enabled cognitive sensor networks

This paper looks into an energy harvesting (EH) relay-enabled cognitive radio wireless sensor network (CR-WSN) considering power splitting (PS) architecture. More specifically, a relay (RU) harvesting energy from the signals transmitted from a secondary user transmitter (ST,) and using the harvested energy to forward the resulting signals to another sensor node subsequently is being investigated. This scheme can be broken down into two components, i.e., a sensor node physically placed near the transmitter (SPNT) and a sensor node physically placed far from the transmitter (SPFT). The closed-form expressions for the successful transmission probability (STP) and the achievable data rate in both cases can be derived analytically. In order to quantify the energy consumption, the system energy efficiency (EE) is examined. Furthermore, the achievable data rate was optimized in three possible scenarios, i.e., the trade-off between the sum data rate and the sum harvested energy (R-E), the achievable data rate at RU, and the joint optimization of the power allocation and PS ratio in case of SPNT. A Monte Carlo simulation has been performed to verify the theoretical analysis obtained, and to show the impact of different parameters on system performance.