Research On Interference Range Of Wireless Networks Based On Stochastic Geometry

In wireless networks,interference is one of the main performance-limiting factors.Protocol interference model is a critical model to describe the interference in wireless networks,and is wildly used in the design of wireless network protocols.As the vital part of the protocol interference model,interference range has a direct influence on the accuracy of the protocol interference model.How to choose an appropriate interference range to make the protocol interference model more accurate needs to be further studied.As the interference range can reduce the interference between the transmitters and the receivers,it can improve the performance of the wireless networks in a certain degree.When choosing an extremely small interference range,the distance between the receivers and the transmitters may be too short.As a consequence,the receivers may be severely interfered,and the network performance may be deteriorated.However,an extremely large interference range can separate the interferers faraway,enlarge the channel reusing distance,and enormously reduce the density of active transmitters.Under this condition,the overall network can not achieve high performance.Based on the above analysis,we can know that the settings of the interference range are rather heuristic and remain an open problem.An appropriate interference range is of great significance to improve network performance.Success transmission probability is a wireless network performance that is most directly influenced by the interference range.The study of success transmission probability lays the foundation of the study of interference range.Bipolar point processes with pairwise transmitters and receivers are suitable for wireless network modeling and interference analysis.The existing bipolar networks can be divided into Poisson bipolar networks and Poisson cellular networks.In Poisson bipolar networks,the distance between the transmitter and the corresponding receiver is a constant.In Poisson cellular networks,the position of the transmitters and receivers are two independent point processes.The study of success transmission probability of bipolar network,which has both transceiver distance randomness and transceiver position relevance,remains blank.In order to improve the accuracy of the protocol interference model,a binary hypothesis testing can be used to construct the optimization problem.The optimal interference range can be achieved by minimizing the Bayes risk.The existing methods based on the binary hypothesis testing are only suitable for the Poisson networks.As for the finite wireless networks,this method needs to be further studied.In order to improve the network performance,the expression of network performance with respect to interference range can be constructed directly.Through the convex optimization theory,the optimal interference range corresponding to the highest network performance can be obtained.In the existing methods of maximizing the transmission capacity methods,the treatment of cumulative interference is not rigorous enough,and the consideration of the interference range of all receivers is not sufficient.Besides,the transmission capacity is a slightly rough network performance,which only considers the average performance of the reference link.Aiming at the drawback of the existing interference range research,this dissertation studies the success transmission probability of random distance bipolar networks,investigates the interference range that minimizes the Bayes risks of finite wireless networks,and explores the interference range that maximizes the spatial outage capacity.The main contents and contributions of this dissertation are as follows:(1)Research on the success transmission probability of random distance bipolar networks.Aiming at the limited consideration of transceiver distance and position relevance,a random distance bipolar network model with both transceiver distance randomness and transceiver position relevance is proposed.Furthermore,the success transmission probability of random distance bipolar network is derived.The distribution of the distance between the transmitter and the reference receiver is achieved via the Jacobian transformation.Using the direct approach,the success probability of the random distance bipolar network is derived.The Monte Carlo simulations results verify that the success transmission probability calculated with random distance bipolar network model is more accurate than that with Poisson bipolar network model.Moreover,the simulation results show that the success transmission probability of random distance bipolar network model converge to that of Poisson bipolar network model in large scale wireless networks.(2)Research on the interference range that minimizes the Bayes risks of finite wireless networks.To solve the problem of the existing Bayes methods which are only suitable for infinite Poisson networks,this dissertation proposes Bayes methods that can be applied to finite wireless networks.In the proposed Bayes Methods,we use the binominal point process and finite Poisson point process to model the binominal network and finite Poisson network,respectively.The conditional success probabilities of the finite wireless networks are derived with the direct approach.Then,the Bayes risk is derived according to the conditional success probability,and the optimal interference range that minimizes the Bayes risk is achieved via convex optimization.Furthermore,we demonstrate the proposed method converge to the existing method when the network range goes to infinity.Simulation results show that the Bayes methods with binominal point process and finite Poisson point process have better performance than those with infinite Poisson point process.Besides,introducing an interference range to the reference receiver can improve the performance of the reference receiver under the sacrifice of average network performance.(3)Research on the interference range that maximizes the spatial outage capacity of bipolar wireless networks.In the methods that maximize the transmission capacity,the cumulative interference and the interference range around all the receivers are not well considerated.The metric with transmission can not provide fine-grained information about the network.In order to overcome the deficiency of the interference range that maximizes the transmission capacity,this dissertation proposes a method to calculate the interference range that maximizes the spatial outage capacity.In this method,the wireless networks are modelled with bipolar point processes.Then the density of reliable link of the random distance bipolar network is derived.Afterwards,we consider the impact of interference range to the active probability,and derive the density of reliable link with consideration of the interference of all the receivers.Subsequently,the optimal interference range that maximizes the spatial outage capacity is derived.Simulation results show that the interference range can promote the spatial outage capacity of the dense Poisson bipolar networks,while the spatial outage capacity will be decreased when considering the interference range in the Poisson cellular networks and random distance bipolar networks.