Signal-to-Interference-Noise-Ratio Model Research Articles

SINR Model with Enhanced Direct Sequence Spread Spectrum (EDSSS) Method in LTE-Wi-Fi Integrated Networks

Wireless Fidelity (Wi-Fi) has been introduced in Third Group Partnership Project (3GPP) Release 12 as an alternative solution for offloading high traffic in LTE network. In addition, Wi-Fi has many advantages such as low operation cost, easy-installed systems and it is currently widely implemented in houses and offices. However, a phenomenon called co-channel interference may occur within LTE-Wi-Fi networks because of its low power transmission when compared to the macrocell. Hence, Wi-Fi users may suffer worst network degradation at frequency 2.4GHz due to its collision with LTE band 40. In order to solve this network issue, an interference management is needed as it affects users perceive experiences and services when using LTE-Wi-Fi integrated networks. In this paper, a method known as Pulse-Shaping Algorithm (PSA) has been integrated with Direct Sequence Spread Spectrum (DSSS) which is called Enhanced Direct Sequence Spread Spectrum (EDSSS) to model Signal-to-Interference-Noise Ratio (SINR). The introduction of this method is for reducing the occurrence of co-channel interference. Furthermore, a new coefficient named as a radius fraction (β) has been introduced in order to evaluate the SINR expression for User Equipments (UEs) in LTE-Wi-Fi networks. The simulation results discovered that β-coefficient with value of 0.2 has given the most optimum improvement of SINR for LTE and Wi-Fi users. By modifying the SINR expression with PSA method, the network performances for users have improved significantly.

Minimum latency data aggregation in the physical interference model

Data aggregation has been the focus of many researchers as one of the most important applications in Wireless Sensor Networks. A main issue of data aggregation is how to construct efficient schedules by which data can be aggregated without any interference. The problem of constructing minimum latency data aggregation schedules (MLAS) has been extensively studied in the literature although most of existing works use the graph-based interference model.In this paper, we study the MLAS problem in the more realistic physical model known as signal-to-interference-noise-ratio (SINR). We first derive an Ωlogn approximation lower bound for the MLAS problem in the metric SINR model. We also prove the NP-hardness of the decision version of MLAS in the geometric SINR model. This is a significant contribution as these results have not been obtained before for the SINR model. In addition, we propose two constant factor approximation algorithms whose latency is bounded by O(Δ+R) for the dual power model, where Δ is the maximum node degree of a network and R is the network radius. Finally we study the performance of the algorithms through simulation.

Hardness of Low Delay Network Scheduling

We consider a communication network and study the problem of designing a high-throughput and low-delay scheduling policy that only requires a polynomial amount of computation at each time step. The well-known maximum weight scheduling policy, proposed by Tassiulas and Ephremides (1992), has favorable performance in terms of throughput and delay but, for general networks, it can be computationally very expensive. A related randomized policy proposed by Tassiulas (1998) provides maximal throughput with only a small amount of computation per step, but seems to induce exponentially large average delay. These considerations raise some natural questions. Is it possible to design a policy with low complexity, high throughput, and low delay for a general network? Does Tassiulas' randomized policy result in low average delay? In this paper, we answer both of these questions negatively. We consider a wireless network operating under two alternative interference models: (a) a combinatorial model involving independent set constraints and (b) the standard SINR (signal to interference noise ratio) model. We show that unless NP ⊆ BPP (or P = NP for the case of determistic arrivals and deterministic policies), and even if the required throughput is a very small fraction of the network's capacity, there does not exist a low-delay policy whose computation per time step scales polynomially with the number of queues. In particular, the average delay of Tassiulas' randomized algorithm must grow super-polynomially. To establish our results, we employ a clever graph transformation introduced by Lund and Yannakakis (1994).

Distributed Computing Column 38

The signal-to-interference & noise ratio (SINR) model is one of the most commonly studied physical (or fading channel) models for wireless networks. We survey some recent studies aiming at achieving a better understanding of the SINR model and its structural properties and developing efficient design algorithms and communication protocols for it. Physical models and SINR Traditional (wired, point-to-point) communication networks can be described satisfactorily using a graph representation. A station s is able to transmit a message to another station s′ if and only if there is a wire connecting the two stations. This condition is independent of the locations, connections and activities of the two stations or of other nearby stations1. Accurately representing a wireless network is considerably harder, since it is nontrivial to decide whether a transmission by a station s is successfully received by another station s′; this may depend on the positioning and activities of s and s′, and on other nearby stations, whose activities might interfere with the transmission and prevent its reception This means that a transmission from s may reach s′ in some settings but fail to reach it under other settings. Moreover, the question of successful reception is more complex, since connections can be of varying quality and capacity. In fact, there are many other relevant factors, such as the presence of physical obstacles, the directions of the antennae at s and s′, the weather, and more. Obtaining an accurate solution taking all of those factors into account involves solving the corresponding Maxwell equations. Since this is usually far too complicated, the common practice is to resort to approaches based on approximation models. For instance, one way to predict the behavior of wireless systems in an urban environment is using a ray tracing model, based on the assumption that radio waves behave according to geometric optics where walls are modeled as reflective mirrors. The uncertainly involved in the dynamics of a radio channel can be modeled using a Markov process. For more information we refer the interested reader to Chapters 2 and 3 of [9]. For the purposes of the current discussion, we follow the approach of ignoring those complicating factors, and assuming a relatively clean abstract setting where the only players are the transmitting and listening ∗Partially supported by a gift from Cisco research center and by the Israel Science Foundation, grant 894/09. excepting certain types of wired local area networks. ACM SIGACT News 74 June 2010 Vol. 41, No. 2 stations, and the antennae are omni-directional. In this setting, the rules governing the reception quality of wireless transmissions can be described schematically by physical or fading channel models. Among those, one of the most commonly studied is the signal-to-interference & noise ratio (SINR) model. In this model, the energy of a signal fades with the distance to the power of the path-loss parameter α. When a station s transmits, the message is successfully received by a listening station s′ if and only if the strength of the signal received by s′, divided by the strength of the interferences from other simultaneous transmissions (plus the background noise N ), exceeds some threshold β. Hence for a collection S = {s1, . . . , sn} of simultaneously transmitting stations in the plane, it is possible to identify with each station si a reception region H(si) around it, consisting of the points where the transmission of si is received correctly. More precisely, denote by dist(p, q) the Euclidean distance between p and q, and assume that each station si transmits with power Ei. At an arbitrary point p, the transmission of station si is correctly received if Ei · dist(p, si) N + ∑ j 6=iEj · dist(p, sj)−α ≥ β . This formula represents a rather general model concerning the allowed transmission power, referred to as the power control model, in which each station can control the power with which it transmits. A simpler (and weaker) model is the uniform wireless network model, which assumes that all transmissions use the same amount of energy, i.e., Ei = 1 for every i.