Back-off Rate Research Articles

Comprehensive Throughput Analysis of Unslotted ALOHA for Low-Power Wide-Area Networks

Unslotted ALOHA has been often employed by several low-power wide-area networks (LPWANs) for Internet of Things (IoT) as a random access (RA) protocol. This work analyzes the performance of unslotted ALOHA systems in terms of throughput and RA delay, and investigates their optimization. Our analysis consists of: 1) two- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heterogeneous</i> user case, whose backoff rate and packet length are different; 2) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">homogeneous</i> user case, whose backoff rate and packet length are identical; and 3) homogeneous users of infinite population model. In the two-user case, we investigate the throughput region of unslotted ALOHA by using a multiobjective optimization problem (MOOP) and derive the Laplace Stieltjes transform (LST) of the probability density function (PDF) of RA delay. For <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> -homogeneous user case, we show how the throughput behaves according to the population size, packet length, and backoff rate. Our work may provide a comprehensive analytical framework for unslotted ALOHA systems.

Transmission State Prediction from MAC in Cognitive Radio via Optimized Deep Learning Architecture

Cognitive Radio (CR) is the hottest network paradigm, which permits the Secondary Users (SUs) like wireless devices/users for intelligent accessing of unallocated radio spectrum. Such accessing happens by enabling interference-free transmission of Primary Users (PUs), who are allotted with some deserved radio spectrum portions. This radio communication paradigm has effective usage in vehicular networks, where communication should be established from vehicles to static stations (vehicle-to-infrastructure) or within vehicles (vehicle-to-vehicle), without allotting dedicated frequencies. Nevertheless, the major issue in designing CR is that it must be built to aid in efficient transmitting and sensing of data through the available radio spectrum channels. This paper proposes a Model Predictive Control (MPC)-based prediction model, via a Deep Learning approach. Here, a Deep Belief Network (DBN) allows in predicting the PU transmission state as idle or busy. Moreover, this paper comes out with a new optimization concept that achieves more accurate and precise prediction. The weight of DBN is optimally selected to pave way for effective performance. Further, a new hybrid algorithm named as Cuckoo Search-Grasshopper Optimization Algorithm (CS-GOA) is proposed. The performance of the proposed model is compared over the other conventional models, in terms of channel utilization and back off, and proved for supremacy. The throughput of the proposed model, even at 50 SUs is better, when compared to other methods. CS-GOA achieved better channel utilization and backoff rate, as compared to ProMAC and NN, when the numbers of SUs and PUs in the architecture are varied with time.

PROLEMus: A Proactive Learning-Based MAC Protocol Against PUEA and SSDF Attacks in Energy Constrained Cognitive Radio Networks

Malicious users can exploit vulnerabilities in cognitive radio networks (CRNs) and cause heavy performance degradation by denial of service (DoS) attacks. During operation, cognitive radios (CRs) spend a considerable amount of time to identify idle (free) channels for transmission. In addition, CRs also need additional security mechanisms to prevent malicious attacks. Proactive model predictive control (MPC)-based medium access control (MAC) protocols for CRs can quicken the idle channel identification by predicting future states of channels in advance. This provides enough time for CRs to carry out other calculations like DoS attack detection. However, such external detection techniques use additional power that makes them inappropriate for energy constrained applications. As a solution, this paper proposes a proactive learning-based MAC protocol (PROLEMus) that shows immunity to two prominent CR-based DoS attacks, namely, primary user emulation attack (PUEA) and spectrum sensing data falsification (SSDF) attack, without any external detection mechanism. PROLEMus shows an average of 6.2%, 8.9%, and 12.4% improvement in channel utilization, backoff rate, and sensing delay, respectively, with low prediction errors (≤1.8%) saving 19.65% energy, when compared to recently proposed MAC protocols like ProMAC aided with additional DoS attack detection mechanism.

Mean-field limits for multi-hop random-access networks

Recent years have seen wireless networks increasing in scale, interconnecting a vast number of devices over large areas. Due to their size these networks rely on distributed algorithms for control, allowing each node to regulate its own activity. A popular such algorithm is Carrier-Sense Multi- Access (CSMA), which is at the core of the well-known 802.11 protocol. Performance analysis of CSMA-based networks has received significant attention in the research literature in recent years, but focused almost exclusively on saturated networks where nodes always have packets available. However, one of the key features of emerging large-scale networks is their ability to transmit packets across large distances via multiple intermediate nodes (multi-hop). This gives rise to vastly more complex dynamics, and to phenomena not captured by saturated models. Consequently, performance analysis of multi-hop random-access networks remains elusive. Based on the observation that emerging multi-hop networks are typically dense and contain a large number of nodes, we consider the mean-field limit of multihop CSMA networks. We show that the equilibrium point of the resulting initial value problem provides a remarkably accurate approximation for the pre-limit stochastic network in stationarity, even for sparse networks with few nodes. Using these equilibrium points we investigate the performance of linear networks under different back-off rates, which govern how fast each node transmits. We find the back-off rates which provide the best end-to-end throughput and network robustness, and use these insights to determine the optimal back-off rates for general networks. We confirm numerically the resulting performance gains compared to the current practice of assigning all nodes the same back-off rate.

Enhancing 4G Co-existence with Wi-Fi/IoT using cognitive radio

The advanced cellular network, long term evolution (LTE) that presently operates in licensed spectrum has been extended to unlicensed LTE (U-LTE) to improve data rate and spectral efficiency by utilizing unlicensed spectrum. Carrier aggregation of 3GPPLTE-A supports the aggregation of licensed and unlicensed spectrum in small and femto cells to provide better user experience. The proposed work consists of two objectives, first to accomplish the listen-before-talk (LBT) regulatory requirement of radio communication in U-LTE and the second to enhance their co-existence with Wi-Fi/IoT users in a non-interference style by reducing the back-off rate of Wi-Fi. The importance of spectrum utilization by the incumbent users of unlicensed band for the upcoming Internet of Things communications is also a key consideration in this work. The recently evolved intelligent technology viz. Cognitive radio (CR) is applied in the proposed system model to meet the objectives. A ground research is done in a simulation environment of LTE signals and 5 GHz band to evaluate the back-off rate of Wi-Fi. A comparative performance analysis between proposed and existing systems are also done and presented in this paper.

Explicit Back-Off Rates for Achieving Target Throughputs in CSMA/CA Networks

CSMA/CA networks have often been analyzed using a stylized model that is fully characterized by a vector of back-off rates and a conflict graph. Further, for any achievable throughput vector $\vec \theta$ the existence of a unique vector $\vec \nu(\vec \theta)$ of back-off rates that achieves this throughput vector was proven. Although this unique vector can in principle be computed iteratively, the required time complexity grows exponentially in the network size, making this only feasible for small networks. In this paper, we present an explicit formula for the unique vector of back-off rates $\vec \nu(\vec \theta)$ needed to achieve any achievable throughput vector $\vec \theta$ provided that the network has a chordal conflict graph. This class of networks contains a number of special cases of interest such as (inhomogeneous) line networks and networks with an acyclic conflict graph. Moreover, these back-off rates are such that the back-off rate of a node only depends on its own target throughput and the target throughput of its neighbors and can be determined in a distributed manner. We further indicate that back-off rates of this form cannot be obtained in general for networks with non-chordal conflict graphs. For general conflict graphs we nevertheless show how to adapt the back-off rates when a node is added to the network when its interfering nodes form a clique in the conflict graph. Finally, we introduce a distributed chordal approximation algorithm for general conflict graphs which is shown (using numerical examples) to be more accurate than the Bethe approximation.

An Efficient Channel Access Scheme for Vehicular Ad Hoc Networks

Vehicular Ad Hoc Networks (VANETs) are getting more popularity due to the potential Intelligent Transport Systems (ITS) technology. It provides many efficient network services such as safety warnings (collision warning), entertainment (video and voice), maps based guidance, and emergency information. VANETs most commonly use Road Side Units (RSUs) and Vehicle-to-Vehicle (V2V) referred to as Vehicle-to-Infrastructure (V2I) mode for data accessing. IEEE 802.11p standard which was originally designed for Wireless Local Area Networks (WLANs) is modified to address such type of communication. However, IEEE 802.11p uses Distributed Coordination Function (DCF) for communication between wireless nodes. Therefore, it does not perform well for high mobility networks such as VANETs. Moreover, in RSU mode timely provision of data/services under high density of vehicles is challenging. In this paper, we propose a RSU-based efficient channel access scheme for VANETs under high traffic and mobility. In the proposed scheme, the contention window is dynamically varied according to the times (deadlines) the vehicles are going to leave the RSU range. The vehicles with shorter time deadlines are served first and vice versa. Simulation is performed by using the Network Simulator (NS-3) v. 3.6. The simulation results show that the proposed scheme performs better in terms of throughput, backoff rate, RSU response time, and fairness.

Distributed Medium Access Over Time-Varying Channels

Recent studies on MAC scheduling have shown that carrier sense multiple access (CSMA) algorithms can be throughput optimal for arbitrary wireless network topology. However, these results are highly sensitive to the underlying assumption on ‘static’ or ‘fixed’ system conditions. For example, if channel conditions are time-varying, it is unclear how each node can adjust its CSMA parameters, so-called backoff and channel holding times, using its local channel information for the desired high performance. In this paper, we study ‘channel-aware’ CSMA (A-CSMA) algorithms in time-varying channels, where they adjust their parameters as some function of the current channel capacity. First, we assume that backoff rates can be arbitrary large and show that the achievable rate region of A-CSMA equals to the maximum rate region if and only if the function is exponential. Furthermore, given an exponential function in A-CSMA, we design updating rules for their parameters, which achieve throughput optimality for an arbitrary wireless network topology. They are the first CSMA algorithms in the literature which are proved to be throughput optimal under time-varying channels. Moreover, we also consider the case when back-off rates of A-CSMA are restricted compared to the speed of channel variations, and characterize the throughput performance of A-CSMA in terms of the underlying wireless network topology. Our results not only guide a high-performance design on MAC scheduling under highly time-varying scenarios, but also provide new insights on the performance of CSMA algorithms in relation to their backoff rates and underlying network topologies.

Explicit Back-off Rates for Achieving Target Throughputs in CSMA/CA Networks

CSMA/CA networks have often been analyzed using a stylized model that is fully characterized by a vector of back-off rates and a conflict graph. We present an explicit formula for the unique vector of back-off rate needed to achieve any achievable throughput vector provided that the network has a chordal conflict graph. These back-off rates are such that the back-off rate of a node only depends on its own target throughput and the target throughput of its neighbors and can be determined in a distributed manner. We also introduce a distributed chordal approximation algorithm for general conflict graphs which is shown (using numerical examples) to be more accurate than the Bethe approximation.

ProMAC: A proactive model predictive control based MAC protocol for cognitive radio vehicular networks

Cognitive Radio (CR) is a recent network paradigm that allows Secondary Users (SUs), such as, wireless devices/users, to intelligently access portions of the radio spectrum not allocated to it, without interfering with the transmission of licensed users (Primary Users (PUs)) who are allocated certain dedicated portions of the radio spectrum. This paradigm in radio communication has been successfully used in vehicular networks wherein communication can be established within vehicles (vehicle-to-vehicle) or vehicles to static stations (vehicle-to-infrastructure) without allocating dedicated frequencies. However, the challenge in CR design lies in building intelligence that helps in efficiently sensing and transmitting data through available radio spectrum channels. This paper proposes a Model Predictive Control (MPC) based Proactive Medium Access Control protocol (ProMAC) for the SUs in a CR network. To the best of our knowledge this is the first proactive MAC reported in the literature for CR. Employing ProMAC in a architecture where the number of SUs and PUs were constant, we achieved 20%,13.5% and 12% improvement in channel utilization, backoff rate and sensing delay respectively as compared to the recently proposed PO-MAC protocol, which is so far the best reported in the literature. In an architecture where the numbers of SUs varied with time, ProMAC achieved 21% and 13.17% improvement in channel utilization and backoff rate, respectively, as compared to PO-MAC. The proposed protocol is based on a self-learning engine that can evolve and improve its prediction accuracy even after deployment on field.

Just-in-Time Orders Delivery Mixed Outsourcing Problem

In a competitive environment, service guarantees would affect customer back-off rate. On time delivery plays an important role on time-based competition. If orders cannot be delivered on time, those orders should be outsourced in order to gain a good reputation. However, if a cargoes distribution company has it own trucks, it would gain more profits if the orders were not outsourced. Thus, JIT orders delivery mixed outsourcing problem (JITMOP) is a difficult decision making problem in a cargoes distribution company. This research constructed a Mixed Integer Programming (MIP) model for maximizing operational profits of the company. The study proposed a Genetic Algorithm with Tabu (GAWT) to solve the model. For validation, the proposed approach was comparing to Random Hybrid Tabu (RHT) and Ransom Search (RS); also found GAWT outperformed RS by an average of 54%. It represented that a cargoes distribution company would gain more than 54% of profits if they schedule orders well, part of orders outsourcing to collaborative companies, and using GAWT heuristics. Future research suggests expanding this research to include more discussion if some assumptions are changed.

Analytical models for understanding space, backoff, and flow correlation in CSMA wireless networks

In wireless networks employing carrier-sense multiple-access with collision avoidance (CSMA/CA), correlations between the service processes of different nodes arise as a result of competition for common wireless channels and the dependencies between upstream and downstream traffic flows. These dependencies make the development of tractable performance models extremely difficult. To address this purpose, we present a new continuous-time model for CSMA wireless networks where we combine a node model and a channel model in order to capture correlation. Simplification methods are presented that make our models computationally tractable for large networks with minimal loss of accuracy. The model can be used for both single and multi-hop wireless networks and takes into account non-saturated queues, backoff-stage dependence of collision probabilities, and the correlation between departure processes and arrival processes of adjacent nodes. The model can be used to compute probabilistic quality of service guarantees to optimize end-to-end throughput and end-to-end delay by adjusting arrival and backoff rates along various paths.

Achieving target throughputs in random-access networks

Random-access algorithms such as CSMA provide a popular mechanism for distributed medium access control in large-scale wireless networks. In recent years, tractable stochastic models have been shown to yield accurate throughput estimates for CSMA networks. We consider a saturated random-access network on a general conflict graph, and prove that for every feasible combination of throughputs, there exists a unique vector of back-off rates that achieves this throughput vector. This result entails proving global invertibility of the non-linear function that describes the throughputs of all nodes in the network. We present several numerical procedures for calculating this inverse, based on fixed-point iteration and Newton’s method. Finally, we provide closed-form results for several special conflict graphs using the theory of Markov random fields.

Analytical models for understanding misbehavior and MAC friendliness in CSMA networks

Nodes using contention-based CSMA protocols are susceptible to the misbehavior of other nodes and also have little flexibility in controlling quality of service. To address the misbehavior problem, continuous-time protocols are proposed. The effects of “misbehavior” on the QoS of all nodes in the system caused by changing the cheater’s arrival rate and backoff rate are studied. The problem of flexibility in controlling QoS is addressed by introducing the concept of “MAC friendliness” where nodes can adjust arrival rates and backoff rates while maintaining a fixed share of the channel. The continuous-time system is modeled using an iterative method and matrix exponential distributions. Collision probabilities are determined both by the channel utilization of the entire system, as well as the actual stage within the backoff process. The model itself is a key contribution because it is accurate over all ranges of traffic loads and models both queueing within nodes and contention for the channel.

Distributed opportunistic scheduling for ad hoc communications with imperfect channel information

Distributed opportunistic scheduling is studied for wireless ad-hoc networks, where many links contend for one channel using random access. In such networks, distributed opportunistic scheduling (DOS) involves a process of joint channel probing and distributed scheduling. It has been shown that under perfect channel estimation, the optimal DOS for maximizing the network throughput is a pure threshold policy. In this paper, this formalism is generalized to explore DOS under noisy channel estimation. In such cases, the transmission rate needs to be backed off from the estimated rate to reduce outages. It is shown that the optimal scheduling policy remains threshold-based, and that the rate threshold turns out to hinge on the variance of the estimation error and be a functional of the backoff rate function. Since the optimal backoff rate is intractable, we devise suboptimal linear backoff schemes that back off the estimated signal-to-noise ratio (SNR) and hence the rate. The corresponding optimal backoff ratios and rate thresholds can be obtained via iterative algorithms. Finally, simulation results are provided to illustrate the tradeoff between increased training time to improve channel estimation and probing efficiency.