Janne J. Lehtomäki

Device-To-Device (D2D) Communication in Cellular Network - Performance Analysis of Optimum and Practical Communication Mode Selection

In a cellular network system one way to increase its capacity is to allow direct communication between closely located user devices when they are communicating with each other instead of conveying data from one device to the other via the radio and core network. The problem is then when the network shall assign direct communication mode and when not. In previous works the decision has been done individually per communicating device pair not taking into account other devices and the current state of the network. We derive means for getting optimal communication mode for all devices in the system in terms of system equations. The system equations capture information of the network such as link gains, noise levels, signal-to-interference-and-noise-ratios, etc., as well as communication mode selection for the devices. Using the derived equations performance bounds for the cellular system where D2D communication is an additional communication mode are illustrated via simulations. Further, practical communication mode selection algorithms are used to evaluate their system performance against the achievable bounds. Analysis show the usability of the system equations and the potential of having D2D operation integrated into a cellular system when there is enough local communication occurring.

Clustering Concept Using Device-To-Device Communication in Cellular System

We study how direct communication within a group of devices, cluster, can improve the performance of a conventional cellular system. The clusters are formed from devices that are close and communicating with each other, for example, sharing data. The clusters share the radio resources among other devices in the system thus creating a mixed network system comprising directly communicating devices and devices having radio links to and from the base stations. In this kind of a system the additional challenge is to decide when clusters shall use direct communication and when conventional cellular radio links to communicate with each other. Here, in addition to clustering concept description we provide new means to analyse achievable system performance when clustering communication is integrated into a cellular network and especially into an interference limited system.

On the Selection of the Best Detection Performance Sensors for Cognitive Radio Networks

In cooperative spectrum sensing, information from several cognitive radios (CRs) is used for detecting the primary user. To reduce sensing overhead and total energy consumption, it is recommended to cooperate only with the CRs that have the best detection performance. However, the problem is that it is not known a priori which of the CRs have the best detection performance. In this letter, we are proposing three methods for selecting the CRs with the best detection performance based only on hard (binary) local decisions from the CRs. Simulations are used to evaluate and compare the methods. The results indicate that the proposed CR selection methods are able to offer significant gains in terms of system performance.

Queueing analysis of opportunistic access in cognitive radios

This paper presents queueing analysis of opportunistic access in cognitive radios. The primary (licensed user) has priority over the secondary user and it does not need to care about the secondary user transmissions. A time slotted system is assumed, so that the secondary user can perform spectrum sensing at the beginning of the slot to know if it is occupied by primary or not. If the slot is free, it can be utilized for secondary transmissions. This leads to no interference with primary user communication, assuming perfect sensing. Finding waiting time and queue length of this type of system has not, according to our best knowledge, been performed before. We perform theoretical analysis by applying M/D/1 priority queueing scheme. The results were used to evaluate the performance of the cognitive network. Simulation are used to validate the results, and simulation results demonstrate a high degree of accuracy for the derived expressions. Results indicate that the performance of the secondary user depends on the data traffic characteristics of the primary user, and under high arrival rate for the primary, the average waiting and average queueing length of the secondary user grow especially when the combined arrival rate approach the queue utilization factor.

Spectrum Sensingwith Forward Methods

New technologies will require effective spectrum use. Opportunistic spectrum usage that is one application of so called cognitive radio techniques enables the use of unused frequencies. One possible way to locate these free frequency bands is to use so called spectrum sensing. In this paper, energy detection based spectrum sensing methods called the forward consecutive mean excision (FCME) and forward cell averaging (CA) methods are studied in the situations where the noise power is unknown. The detection and false alarm probabilities of the studied methods are also of interest. Numerical results show that the investigated approaches have good performance

Threshold setting strategies for a quantized total power radiometer

We analyze the impact of a uniform quantizer on the false-alarm probability of a total power radiometer. Different possibilities to set the detection threshold are discussed. The main emphasis is on methods that use the estimated noise level. In particular, we analyze the cell-averaging (CA) constant false-alarm rate threshold setting strategy. The numerical results show that the CA strategy offers the desired false-alarm probability.

CFAR strategies for channelized radiometer

The channelized radiometer is a well-known intercept receiver. We analyze its performance when different constant false alarm rate (CFAR) strategies are used to set a detection threshold based on values of some reference cells. The studied conventional methods are cell-averaging (CA) and order statistics. The proposed iterative methods for setting the detection threshold are forward consecutive mean excision (FCME) with the CA scaling factors in (final) detection decision (FCME+CA), backward consecutive mean excision (BCME) with the CA scaling factors in detection (BCME+CA) and a method that uses the CA scaling factors in both censoring and detection (CA+CA). The results show that iterative CFAR methods can improve detection performance compared to the baseline methods.

Double-threshold based narrowband signal extraction

A localization algorithm based on double-thresholding (LAD) is a computationally simple method for localizing narrowband signals in the frequency domain. The method does not need any a priori information about the narrowband signal. The localization is based on two thresholds. The lower threshold is used to compose adjacent signal samples into clusters whereas the upper threshold is used to detect signals. The LAD can be applied in narrowband signal detection as well as in interference suppression. The simulation results show that the LAD gives quite good localization accuracy and the LAD is able to determine correct number of narrowband signals even over 95% of the cases.

Spectrum Sensing with LAD-Based Methods

Opportunistic spectrum usage would enable enhancing the efficiency of existing and emerging wireless communication systems. One of the key issues related to those systems is spectrum opportunity estimation. In this paper, we are using a technique utilized earlier in narrowband signal detection, namely the localization algorithm based on double-thresholding (LAD), for sensing the existence of primary user signals in a cognitive radio systems. The LAD method requires no a priori information on the primary user statistics and it has a low computational complexity that will enable a low-cost real-time implementation. The LAD method is able to estimate the number of narrowband signals and their characteristics, including bandwidth and power. A simplified version of the LAD method which uses normalized thresholds (NT) as well as an enhancement of the scheme that uses adjacent cluster combining (ACC) are proposed. Simulation results show that the simplified version of the LAD method is useful in the considered situations, and the enhanced version of the LAD method improves the performance of the LAD and LAD NT methods significantly.

CFAR Outlier Detection With Forward Methods

Separation or classification of signal-present samples from noise-only samples is studied. The false-alarm probability implies how many noise-only samples are wrongly classified as outliers, and typically it should be smaller than some upper limit. The noise distribution parameters are not known a priori and have to be estimated. Multiple outliers have a strong influence to that estimation and may lead to uncontrollable false-alarm probability. The false-alarm probability control can be improved by robust estimators and/or by forward-detection methods. In this article, the false-alarm probability of the forward methods is analyzed. The forward consecutive mean excision (FCME) algorithm is enhanced to allow better false-alarm control. It is proposed that the forward method using the cell-averaging (CA) constant false-alarm rate (CFAR) technique can be applied for locating the outliers. The results show that its false-alarm probability stays close to the required value even in the presence of multiple outliers.