for this kind of problems resides in Sensor Networks [1]. A Sensor Network is a ... sary to equip them with highly flexible communication ca- pabilities, as the ones ..... York, NY, USA, 2000. [9] Simon Haykin, âCognitive radio: brain-empoweres.
COGNITIVE ANTENNAS ARCHITECTURE FOR DISTRIBUTED SENSOR NETWORKS M. Briasco, A.F. Cattoni, G. Oliveri, M. Raffetto and Carlo S. Regazzoni Department of Biophysical and Electronic Engineering - University of Genova via Opera Pia 11A I-16145 Genova, Italy e-mail: {briasco, cattoni, oliveri, raffetto, carlo}@dibe.unige.it ABSTRACT In the paper, the possibility of joining Wireless Sensor Node and Cognitive Radio paradigms to obtain a new kind of Cognitive devices called Wireless Cognitive Sensor Nodes (WCSNs) will be considered. The paper will be focused on the design process of a sub-component of the WCSN: the Cognitive Antenna System. The architecture of WCSN together with the design process and antenna characteristics for Cognitive Antennas will be shown. The design characteristics considered for the PRIN-SMART, a project financed by the Italian Ministry of the University and Research (MIUR), operative framework will be pointed out. 1. INTRODUCTION In the last years, research efforts have been expended in order to solve the problem of sensing and monitoring spatially and temporally distributed phenomena. A possible solution for this kind of problems resides in Sensor Networks [1]. A Sensor Network is a hierarchical structure composed by simple sensing and processing elements called nodes. If the nodes are able to communicate through radio channels, they are called Wireless Sensor Networks (WSNs) [2],[3]. A WSN is composed of small devices generally deployed in large scale (from tens to thousands) to sense the physical world either inside the phenomenon or very close to it. WSNs can have thousand of applications, that can be categorized into four main classes: safety and security, environmental monitoring and protection, health and care monitoring, domestic automation. Nowadays safety and security, especially after September 11th, is one of the most demanding field: surveillance applications [4], Nuclear Biological and Chemical (NBC) attack detection [5], system fault detection and vehicular safety are possible applications in which the support of WSNs is determinant. Also in environmental applications the WSNs are very useful: the studied variables in this field are usually spatially distributed over large areas, or they are situated in not-easily accessible zones, for these reasons the absence of infrastructure and the wide modularity of WSNs allows to study phenomena like earthquake and chemical clouds.
In medical world, exploitation of WSNs is considered essential for monitoring the health status of the patient. This application is feasible thanks to embedded sensors, directly inserted into the body, able to compose a flexible and distributed network for a continuous control of the subject. The last but not less important field is represented by domestic automation (domotic). In this case, sensor networks are structured as heterogeneous systems able to create a fullyinterconnected structure in smart indoor spaces. Even if recent advances in electronics has enabled the development of low-cost, low-power, multi-functional sensors, there still remain open issues in hardware/software design of nodes. One of the most important of these problems is still the communication process of the gathered information through radio frequency channels. Radio links are, in fact, time and space-varying and very noisy and they can compromise the efficiency of all the network. For example, it’s possible to imagine a scenario where hundreds of sensors are simultaneously trying to transmit context information by the same communication standard and in the same bandwidth. The previously described problem is worsened by the presence, in the ElectroMagnetic (EM) spectrum, of a wide number of communication standards already used for other applications. Luckily, not all the already licensed frequency are simultaneously used in every time instant and in every place. A study, commissioned by the U.S. Federal Communication Commission (FCC) [6], confirmed this phenomenon. Hence, to improve the performances of WSNs it’s necessary to equip them with highly flexible communication capabilities, as the ones provided by Software Defined Radios (SDRs) and Cognitive Radios (CRs) [7],[8],[9]. These capabilities allow to built up a network with high performances overcoming the inefficient use of the radio resources (spectrum, modulation, standard, protocols). A brief overview on SDRs and CRs will be presented in the next section, together with the resulting architecture for this new kind of sensor node.
2. WIRELESS COGNITIVE SENSOR NODE The development of devices able to work, simultaneously, according to different standards (2G, 2.5G, 3G, Beyond 3G ...) represents one of the main technological challenges for the next years. Nowadays, in fact, the number of wireless communication standards is significantly increased. Hence, in order to operate multi-standard modes, research efforts have recently considered the possibility of exploiting a new approach based on a universal terminal. This single device must to be able to administrate multi-wireless communication modes by a partial or total software modules reprogramming. This innovative idea is the principal target for Software Radio (SR) or Software Defined Radio (SDR) [8], [10]. A classical SR architecture in shown in Figure 1(a). Based on SDRs and its natural evolution, the CR paradigm, defined the first time by J. Mitola [7], foresees devices able to adapt themselves to radio environment and, more in general, to external environment. They also are able to learn, as a biological cognitive process, from the past experiences how to carry out this adaptation. CR brings to the definition of a completely reconfigurable physical layer where communication features, by sensing the radio environment, can change in relation to the conditions of the wireless channel, to the communication traffic status and to the users’ requirements.
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Fig. 1. Macro block diagram for SDRs (a) and macro block diagram for CRs (b) In Figure 1 is evidenced the strict relationship between a SDR architecture and a CR one: by adding an artificial intelligence module to a SDR architecture, is feasible to obtain
an adaptive, flexible device able to learn independently and to react to the external stimuli in a suitable manner. One of the most used definition of Cognitive, among the researchers, is derived from the Oxford English Dictionary: cognitive is pertained to cognition, or to the action or process of knowing and cognition is the action or faculty of knowing taken in its widest sense, including sensation, perception, conception, etc., as distinguished from feeling and volition. As a direct consequence of this definition, the CR can be defined as a terminal able to Sense the external world, Analyze the gathered data and compute them in order to Decide how to Act in relation to the external environment end itself. These tasks can be summarized in what Mitola calls Radio Cognitive Cycle [8], during which a cognitive radio continually observes the environment, orients itself, creates plans, decides, and then acts.
Fig. 2. Cognitive cycle for CRs In Figure 2 the Radio Cognitive Cycle is shown: the Observation (or Sensing) and Action modules represent the interfaces of the CR with the real world. The Analysis and Decision modules compose the inner part of the system, the intelligence which govern the entire CR: the Cognitive Engine. By fusing this new paradigm into the common wireless node architecture (see Figure 3(a)) it’s possible both to extend the sensorial capabilities to the electromagnetic RF environment and to work on different levels of the ISO-OSI networking stack (Figure 3(b)). In fact in Figure 3(a) and (b) are evidenced the differences between these two architectures: while the WSN uses the transceiver only as a communication mean, by fixing a priori protocol, mode and physical parameters, the WCSN transforms the communication device in a sensor extended to the EM spectrum and it is able to reconfigure itself in order the adapt the communication capabilities to the current requirements. The Context Intelligence, embedded in WCSNs, behaves by following the previously described Radio Cognitive Cycle (Figure 2). The Observation capabilities, in this case, are related to both the EM sensor and physical sensor. In this paper, the attention will be focused only on the sensing capabilities related to the RF spectrum, represented by Mode Identification and Spectrum Monitoring techniques [11].
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which is the proper node reconfiguration for the current context and to pilot the drives which allow this reconfiguration. In order to perform all these actions, the Cognitive system, which is substantially composed by software modules, requires very high computational capabilities. This requirement collides with the main characteristics a wireless node should have: small in size, low cost, with low power consumption. A possible solution, to make WCSNs feasible, is to distribute the intelligence of the Cognitive Engine on all the already available computational hardware. 4. COGNITIVE ANTENNAS The demand for higher frequency reuse in wireless networks has motivated a lot of recent research activities in the study of the techniques for the exploitation of the Space Diversity concept [18], namely Spatial Division Multiple Access - SDMA. Due to their high flexibility, the so-called Smart Antennas [18], [19] (Figure 4 (a)) have received increasing interest within wireless radio systems.
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Fig. 3. Architecture of a Wireless Sensor Node (WSN) (a) and of a Wireless Cognitive Sensor Node (WCSN) (b) 3. SPECTRUM SENSING AND ALLOCATION Spectrum Allocation is the process through which a terminal is able to perform highly flexible communications by an accurate management of the available frequencies in a certain time instant and in a certain place. The first and the most sensitive of the stages the Spectrum Allocation process is composed of, is the so-called Mode Identification and Spectrum Monitoring (MISM) stage, which is can be considered the basic process for CRs. In the State of the Art, different solutions to implement MISM have already been proposed. The simplest and oldest solution is the usage of Radiometer [12],[13]: it is based on an energy measurements in each sub-band of the considered spectrum range, in order to discriminate which bands are currently used. This method is characterized by a very low computational load, but it is not sufficient to identify signals superimposed in the same bandwidth. Other solutions are based on learning machines like Radial Basis Functions (RBF) neural networks [14],[15] or Support Vector Machines (SVM) [11]. Other techniques, which consider also superimposed communications, are based on a cyclostationarity analysis [16],[17]. Sensing is not the only stage involved in dynamic spectrum allocation: in fact the Cognitive Engine has to perform also the analysis of the environmental context, it has to decide
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Fig. 4. (a) Beamformer architecture and (b) example of Adaptive beamforming radiation pattern A Smart Antenna System [20], [21] is able to provide, if compared with existing technologies, higher system capacity, improved quality of service, suppress interferences, improved power consumption and higher frequency reuse. From a practical point of view, a Smart Antenna System combines an antenna array with digital signal processing techniques (adaptive beamforming techniques, direction of arrival procedures, etc.) in order to obtain a software steerable antenna pattern and direct the radiated power in (or receive from) the desired direction only [18],[19] (Figure
4(b)). Due to the presence of computational hardware already embedded in the antenna equipment (Figure 4(a)), as previously mentioned (Section 3), it’s possible to exploit all the computational resources by providing the antenna device with part of the Cognitive Intelligence usually processed by an high-cost DSP core. It’s hence possible to decrease the load of the central processor, allowing a choice, in designing phase, of a cheaper DSP hardware. A Cognitive Antenna is substantially an antenna array able to provide a spatial-temporal scanning of the radio environment, and it is able to reconfigure itself in order to perform the best communication capabilities. It’s hence clear that it’s possible to extract and process context information directly on the antenna gear.
fact the deployment of sensors can be studied on the basis of maps generated by explorative nodes. A spatial planning can be also useful in dynamic scenarios where mobile nodes try to reach the spatial point which allows the best energy saving respect to the communication requirements. In Figure 5 the logical architecture for the communication part of a WCSN is shown: it’s evidenced how the Radio Cognitive Cycle is distributed on the various logical processing modules. It’s clear that the Sensing and Action stages directly involve the antenna equipment. Also for this reason it’s possible to design antenna equipments for WCSN by considering also how the antenna should behaves related to the Cognitive Cycle. It’s clear that the designing process for Cognitive Antennas should be faced with a Cross-Layer Approach: in fact knowledge about of electromagnetic fields, signal processing and pattern recognition are required to design all the stages of the device. A joint design process allows to foresee in a more exact way all the behaviors of the device. An accurate joint optimization of both physical and logical parameters leads to a more efficient device, thanks to a reduction of the complexity of the algorithms. A WCSN can be considered a Cognitive Entity completely immersed in a physical environment. As a biological Cognitive Entity, the WCSN should have an internal representation of the physical world which is surrounded of. This knowledge can be hence summarized in the Observation Maps extracted by the Cognitive Antenna. This is the reason why it is important to extract maps of the environment: they help, at any level of the cognitive process, in defining the current physical context in a more precise way. 5. OPERATIVE FRAMEWORK
Fig. 5. Logical architecture of a WCSN Besides to the distribution of the computational complexity on all the present hardware, other and more interesting effects can be evidenced by the usage of Cognitive Antennas: by exploiting the variable directionality of the Smart Antenna joined with the complex analysis operated by the Cognitive Engine, it’s possible to extract precise space-timefrequency information about the environment: i.e. a time varying map of the physical space where the observed variable is the bandwidth occupation. Hence spectrum allocation policies, which exploits also Spatial Division Multiple Access (SDMA) techniques in order to grant a more efficient spectrum re-usage, can be thought. Complementary, also space occupation policies could be implemented in order to improve the performances of the entire system: in
A possible operative framework (Fig. 6) is the one considered by the PRIN-SMART project, financed by the Italian Ministry of the University and Research (MIUR). The project foresees the hardware/software integrated design of a mobile sensor network for road and traffic information gathering.
Fig. 6. Scenario considered in PRIN-SMART project
Each vehicle is provided by a set of sensors managed by a Cognitive System. Information about vehicle status, road conditions, traffic conditions is acquired: each vehicle can hence be considered as a mobile cognitive node. The aim of the network is to exchange the gathered information between each sensor and a Master Node, labeled as CBTS in Figure 6. The Master Node is designed as a Cognitive Base Transceiver Station (CBTS) which works also as interface between the local sensor network and the other global telecommunication networks. In the PRIN-SMART scenario, different European communication and navigation technologies have been considered: • GSM/GPRS : Considered Bandwidth: 820-960 MHz, 1710-1880 MHz • T/S-UMTS: Considered Bandwidth: 1920-1980 MHz, 2110-2170 MHz • GPS/Galileo: Considered Bandwidth: 1150-1250 MHz, 1550-1600 MHz • WLAN/Wi-Max: Considered Bandwidth: 2300-2500 MHz, 3400-3600 MHz Each CBTS is considered equipped with a Cognitive Antenna. The design process of this antenna architecture is considered as one of the hot topics of the project. In fact, the antenna equipment, besides being provided with all the adaptation functionalities of a Cognitive Antenna, it should also operate in a very wide bandwidth, and satisfy strict temporal requirements of high speed mobility foreseen by the considered scenario (such as for an highway).
module linearly combines the various signals derived from the single antenna elements, in order to obtain the desired beam pattern. This module is driven by a module which contains the sensing policies for the considered physical environment: it is already part of the distributed Cognitive intelligence. Once obtained, for the considered beam pattern the signal, it is processed by three parallel modules: the first one is composed by normal de-noising and filtering software routines, the second one extracts the energy from the sub-bands of the modes considered in the project, in order to verify the spectrum occupancy of each sub-band. This feature is directly sent to the mode classifier. The third module extracts the Direction Of Arrival of each user. This feature is directly sent to the context processor, which tries to identify the current context. As previously described, all these processes are intended to extract the time varying maps described in Section 4. The map should contain all the information required to localize the user, the mode he is transmitting with, and if there are free frequencies in order to allocate new communications. Substantially, all the information necessary to define the current context. As previously cited, all the parameters which describe the behavior of these modules, from the beam-forming weights to the energy detection thresholds, have to be jointly evaluated with a cross-layer approach, consisting in the definition and minimization of a cost functional which keeps into account the desired behavior of the entire system: the performances and functional characteristics of each module influence the way of working of the following stages. A complete analysis of all the system allows to find the optimal solution for all the stages in a more satisfactory way. All the processing until here described can be performed on a low cost hardware or FPGA. The remaining modules [22], which require an heavier computational load, have to be programmed on a more expensive DSP core system. In the following section the simulative architecture will be presented together with some results obtained for a simplified framework. 6. SIMULATIVE FRAMEWORK
Fig. 7. Processing architecture designed for the SMARTPRIN project The Processing Architecture, derived from the requirements of the PRIN-SMART project application, in Figure 7 is shown: after an analog front-end, used to down-convert signal spectrum to Intermediate Frequency (IF) and a first rough signal filtering, the IF signal is digitalized. The Beamforming
As we are interested in the simulation of a complex system operating in a dynamic environment, we firstly consider a simplified simulative framework in which only the CBTS Sensing function is taken into account. The simulator logical structure is depicted in Figure 8. The considered application is represented by a portion of highway monitored by a CBTS equipped with a sensing antenna. The sensing antenna is represented by a linear array of equispaced identical dipoles, which are tuned to be resonant around the center of the frequency band of inter-
Fig. 8. The simulative framework of the sensing function of the cognitive antenna. est. The behavior of the vehicles which enter the portion of the highway monitored by the CBTS is simulated by the Context Generator module. In our simulative framework, this module is intended to simulate the complete context evolution (for example the vehicles movements and the dynamic of their communication modes) through a statistical engine. The information coming from the Context Generator are then exploited by the Electromagnetic Environment Simulator (EES) module. The EES is responsible for the simulation of the overall environment, and in particular for the processing performed by the sensing antenna which receives the signals generated by the various moving vehicles. The results of the processing of the environment are represented on ”space-frequency images” generated at discrete time intervals, which provide the radio environment evolution. Each image contains informations about the spectrumspace occupation which can be extracted and exploited by the subsequent modules. In particular, each pixel of the image can be thought of as representing the amount of power received at a given frequency, from a given angle for a certain time interval. As a visual example, in Figure 9 we report an image obtained for a context given by five users respectively positioned at about -60, -47, -30, -25 and 25 degrees with respect to the CBTS station. Each user transmits two close sinusoidal tones. It is interesting to see that, although some user overlap in space or in frequency, they can be easily recognized as different in the joined space-frequency domain. The subsequent processing modules are responsible for the extraction of the features of interest by the sequence of space-frequency images depicting the radio environment. The Sensing function itself is expected to be dynamically reconfigured by the Cognitive Engine on the basis of the extracted features (as an example, by refining the spatial resolution of the images in correspondence of close vehicles) and of other informations coming from other cognitive entities. 7. CONCLUSIONS The paper tries to face the problem of communications in Wireless Sensor Networks. A solution to this problem is
(a) Two-dimensional space-frequency image.
(b) Three dimensional representation of the same image.
Fig. 9. Example of space-frequency image obtained by the ESS module. The context is given by five users respectively positioned at about -60, -47, -30, -25 and 25 degrees with respect to the CBTS station. Each user transmits two close sinusoidal tones.
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