Signal-to-interference-and-noise Ratio Model Research Articles

UAV swarm communication reliability based on a comprehensive SINR model

Communication reliability is one of the most important factors in ensuring the safe operation of unmanned aerial vehicles (UAVs). The reliability of wireless communication for a UAV swarm is typically characterized by the signal-to-interference-and-noise ratio (SINR). However, previous work on UAV swarm communication reliability did not use a comprehensive SINR model. In this paper, we derived two novel closed-form approximations – lognormal and generalized beta prime (GBP) - of UAV swarm communication reliability based on a comprehensive SINR model. The model includes shadowing, multipath fading, the dependence of fading on external factors, which are the probability of line-of-sight (LoS) and the physical environment, as well as all possible interference within the system, which may be from the UAVs and the ground control station (GCS). For typical swarm heights between 60 – 300 m and up to 32 UAVs, comparisons of the approximate and simulated reliabilities for UAV swarms with a radius up to a critical value (which increases with the number and transmit power of the UAVs) show that only the lognormal approximation is accurate for all uplink and downlink communications, up to a critical horizontal distance from the GCS (which increases with height). Benchmarking of the lognormal approximation shows that neglecting either shadowing or multipath fading leads to high approximation errors. An example of how the lognormal approximation can be used to evaluate and maintain the communication reliability of a UAV swarm during deployment is given. Furthermore, it can be used to derive other SINR-dependent performance metrics, such as ergodic capacity and symbol error rate, which are useful in UAV network design and monitoring.

Sparse Backbone and Optimal Distributed SINR Algorithms

We develop randomized distributed algorithms for many of the most fundamental communication problems in wireless networks under the Signal to Interference and Noise Ratio (SINR) model of communication, including (multi-message) broadcast, local broadcast, coloring, Maximal Independent Set, and aggregation. The complexity of our algorithms is optimal up to polylogarithmic preprocessing time. It shows—contrary to expectation—that the plain vanilla SINR model is just as powerful and fast (modulo the preprocessing) as various extensions studied, including power control, carrier sense, collision detection, free acknowledgements, and geolocation knowledge. Central to these results is an efficient construction of a constant-density backbone structure over the network, which is of independent interest. This is achieved using an indirect sensing technique, where message non-reception is used to deduce information about relative node-distances.

On Resource Management in Load-Coupled OFDMA Networks

To improve the spectral efficiency in long-term evolution systems, the resource blocks (RBs) are shared among different cells/base stations (BSs) resulting in interference among the cells/BSs on each RB, although all the sub-carriers (SCs) in an RB may not be used in a cell. Defining the load of a given BS per RB as the fraction of the active SCs in that RB, in this paper, we present a generalized signal-to-interference-and-noise-ratio (SINR) model for downlink users on a given RB. This model considers both the transmit powers of the BSs and the loads of the cells over that RB. Under this load-coupled SINR model, to study the feasibility of a given rate demand vector for users, we formulate an optimization problem of minimizing the total load of the BSs on the RBs. Then, for two different scenarios of feasible and infeasible demand vectors, respectively, we study the load management problem (i.e., minimizing the total load of the BSs on the RBs) and admission control problem (i.e., finding the sub-set of users with maximum cardinality whose demands can be concurrently satisfied), respectively. Our theoretical investigations, which provide guidelines for designing radio resource management methods for load-coupled OFDMA networks, are complemented through Monte Carlo simulations.

Distributed Spanner Construction With Physical Interference: Constant Stretch and Linear Sparseness

This paper presents the first distributed algorithm to construct a spanner for arbitrary ad hoc networks under the physical signal-to-interference-and-noise-ratio (SINR) interference model. Spanner construction is one of the most important techniques for topology control in wireless networks, which intends to find a sparse topology in which only a small number of links need to be maintained, without substantially degrading the path connecting any pair of the nodes in the network. Due to the non-local property of interference, constructing a spanner is challenging under the SINR model, especially when a local distributed algorithm is desired. We meet this challenge by proposing an efficient randomized distributed algorithm that can construct a spanner in $O(\log n\log \Gamma )$ timeslots with a high probability, where $n$ is the total number of nodes and $\Gamma $ describes the ratio of the maximum distance to the minimum distance between nodes. The constructed spanner concurrently satisfies two most desirable properties: constant stretch and linear sparseness. Our algorithm employs a novel maximal independent set (MIS) procedure as a subroutine, which is crucial in achieving the time efficiency of spanner construction. The MIS algorithm improves the best known result of $O(\log ^{2}n)$ [33] to $O(\log n)$ and is of independent interest as the algorithm is applicable also to many other applications. We conduct simulations to verify the proposed spanner construction algorithm, and the results show that our algorithm also performs well in realistic environments.

SINR Model With Best Server Association for High Availability Studies of Wireless Networks

The signal-to-interference-and-noise ratio (SINR) is of key importance for the analysis and design of wireless networks. For addressing new requirements imposed on wireless communication, in particular high availability, a highly accurate modeling of the SINR is needed. We propose a stochastic model of the SINR distribution where shadow fading is characterized by random variables. Therein, the impact of shadow fading on the user association is incorporated by modification of the distributions involved. The SINR model is capable to describe all parts of the SINR distribution in detail, especially the left tail which is of interest for studies of high availability.

Joint topology control and routing for multi-radio multi-channel WMNs under SINR model using bio-inspired techniques

Multi-channel communication in a wireless mesh network (WMN) equipped with multi-radio routers can significantly enhance the network capacity. Channel allocation, power control and routing are three main issues involved in the performance of multi-channel multi-radio WMNs. In this paper, the joint optimization of channel allocation, power control and routing under signal-to-interference-and-noise ratio (SINR) model for multi-channel multi-radio WMNs is investigated. It is proven to be NP hard. As we know, no optimal polynomial time solutions have been proposed in the previous literatures. In order to tackle this problem, we apply bio-inspired optimization techniques for channel allocation and power control, and use linear programming for routing optimization. To reflect the cross-layer interaction property among these three issues, the routing optimization is further defined as the fitness value of a chromosome in bio-inspired optimization. Further, we propose an effective joint optimization framework, in which two representative bio-inspired optimization methods (genetic algorithm and particle swarm optimization algorithm) are hybridized to enhance the searching ability. The detailed evolution processes for both genetic algorithm and particle swarm optimization algorithm are demonstrated. Extensive simulation results show that the proposed algorithm converges fast and approaches the sub-optimal solution effectively.

Conflict graphs and the SINR-capacity of the mean power scheme

The Capacity problem in wireless networks is to select a maximum cardinality feasible subset of a set of transmission requests or links, where a set of links is feasible if the corresponding transmissions can be done simultaneously without collisions. We consider two models of feasibility: the SINR (Signal to Interference and Noise Ratio) model and a special conflict graph model. We show that if a special power assignment method is used (called the mean power scheme), then the solutions of the Capacity problem in the two models differ by at most a constant factor for any set of links in a doubling metric space of small dimension. This is not true for other power assignment methods in general. This result, besides showing that the mean power scheme is scalable between different models (as opposed to other power schemes), also has the potential to yield new results in the SINR model, using tools from graph theory, which we demonstrate in several examples.

A genetic approach on cross-layer optimization for cognitive radio wireless mesh network under SINR model

Due to the limited spectrum resources and the differences of link loads, how to obtain maximum network throughput through cross-layer design under signal-to-interference-and-noise ratio (SINR) model is recognized as a fundamental but hard problem. For this reason, the throughput maximization problem jointly with power control, channel allocation and routing under SINR model is researched. First, by formulating the optimization model and digging up its special structure, we show that the throughput maximization problem can be decomposed into two sub-problems: a channel allocation and power control sub-problem at the link-physical layer, and a throughput optimization sub-problem at the network layer. As to the link-physical layer sub-problem, since the joint optimization on channel allocation and power control is NP hard, we apply genetic algorithm for searching the optimal solution. As to the network layer sub-problem, we use linear programming technique for throughput optimization. To reflect the interplay property among these three layers, the fitness of each individual in the genetic algorithm is evaluated by solving the network layer sub-problem. Therefore, an effective cross-layer optimization framework based on genetic algorithm is obtained, which can find optimized power control, channel allocation and route selection in polynomial time. In order to enhance the convergence process during evolution, the integer based representation scheme and corresponding genetic operators are well designed with appropriate constraint control mechanisms. Extensive simulation results demonstrate that the proposed scheme obtains higher network throughput compared to the pervious works with comparable computational complexity.

Interference-constrained coverage algorithms in the protocol and SINR models

A wireless network's design must include the optimization of the area of coverage of its wireless transmitters--mobile and base stations in cellular networks, wireless access points in WLANs, or nodes on a transmit schedule in a wireless ad-hoc network. Furthermore, with increasing densities of wireless network deployments, paucity of spectrum, and new developments like whitespace devices and cognitive networks, there is a need to study the computational efficiency of managing interference and optimizing coverage. This work presents new algorithms for computing and optimizing interference-limited coverage of wireless networks under protocol and Signal-to-Interference-and-Noise Ratio (SINR) models. For the protocol model we demonstrate lower bounds on computation of the coverage area for an $$n$$n transmitter topology. We first show that any offline computation has a run-time of $$\varOmega (n\log {n})$$Ω(nlogn), and any dynamic update takes $$\varOmega (\log {n})$$Ω(logn) time to locate transmitters whose coverage is modified and $$\varOmega (k)$$Ω(k) time to update $$k$$k affected coverage regions. We then demonstrate an extension of an offline algorithm to a dynamic algorithm that achieves the lower bound. For coverage in the SINR model, we demonstrate the difficulty of geometric direct computation, and report a flexible coverage area estimation method. We then propose a Random Hill Climbing method for optimizing the coverage area measure, and demonstrate the efficacy of this method by experimental comparison with the Nelder---Mead and exhaustive search optimization methods.

Maximizing Capacity in Multihop Cognitive Radio Networks under the SINR Model

Cognitive radio networks (CRNs) have the potential to utilize spectrum efficiently and are positioned to be the core technology for the next-generation multihop wireless networks. An important problem for such networks is its capacity. We study this problem for CRNs in the SINR (signal-to-interference-and-noise-ratio) model, which is considered to be a better characterization of interference (but also more difficult to analyze) than disk graph model. The main difficulties of this problem are two-fold. First, SINR is a nonconvex function of transmission powers; an optimization problem in the SINR model is usually a nonconvex program and NP-hard in general. Second, in the SINR model, scheduling feasibility and the maximum allowed flow rate on each link are determined by SINR at the physical layer. To maximize capacity, it is essential to follow a cross-layer approach, but joint optimization at physical (power control), link (scheduling), and network (flow routing) layers with the SINR function is inherently difficult. In this paper, we give a mathematical characterization of the joint relationship among these layers. We devise a solution procedure that provides a (1- \varepsilon ) optimal solution to this complex problem, where \varepsilon is the required accuracy. Our theoretical result offers a performance benchmark for any other algorithms developed for practical implementation. Using numerical results, we demonstrate the efficacy of the solution procedure and offer quantitative understanding on the interaction of power control, scheduling, and flow routing in a CRN.

Performance Analysis of Two-Hop Cellular Systems in Transparent and Non-transparent Modes

In this paper, we evaluate the downlink performance of Transparent mode (T-mode) and Non-Transparent mode (NT-mode) in a two-hop cellular system based on IEEE 802.16j. In particular, we evaluate the performance in terms of the system capacity, optimal resource allocation, and outage probability using Monte Carlo simulation with various system parameters such as different Frequency Reuse Factors (FRFs) and the distance between Base Station (BS) and Relay Station (RS). To analyze the Signal to Interference and Noise Ratio (SINR) of the access and relay links, an SINR model is introduced for cellular multihop systems considering intra- and inter-cell interferences. Then, we present a method of optimal resource allocation for the Access Zone (AZ) and Relay Zone (RZ) to maximize the system capacity. Consequently, the simulation results provide an insight into choosing the appropriate RS position and optimal resource allocation. Through numerical examples, it is found that the FRFs of two and three are good choices to achieve the highest capacity with low outage in T- and NT-modes, respectively.