A Methodology for Design of Scalable Architectures in Software Radio ...

J Sign Process Syst (2013) 73:315–323 DOI 10.1007/s11265-013-0767-x

A Methodology for Design of Scalable Architectures in Software Radio Networks: a Unified Device and Network Perspective Maurizio Colizza · Marco Faccio · Claudia Rinaldi · Fortunato Santucci

Received: 18 October 2012 / Revised: 1 February 2013 / Accepted: 24 April 2013 / Published online: 18 May 2013 © Springer Science+Business Media New York 2013

Abstract This paper proposes the Tissue methodology as a novel methodology for analysis, design and synthesis of networked embedded systems and subsequent development of distributed architectural frameworks. The proposed method aims at reducing the development time through the use of reconfigurable HW/SW components and the application of automatic code generation techniques. We devise the usefulness of the proposed methodology in the context of mobile ad-hoc networks (MANET) which exploit Software Radio (SR) technology for reconfigurability issues. Drawbacks of current design and simulation tools and advantages coming from the application of the TM are discussed in the paper. Keywords Software architecture · Simulation · Automatic code generation · Tissue methodology · MANETs · Software Defined Radio

1 Introduction The recent development of advanced applications running on interconnected systems has pushed increasing demands for evolution of communication systems, with the wireless M. Colizza · M. Faccio · C. Rinaldi () · F. Santucci Center of Excellence DEWS, University of L’Aquila, L’Aquila, Italy e-mail: [email protected]

domain being interested by an unprecedented pressure for supporting several services through different protocol stacks and in different bands. This has motivated the research and industrial communities to undertake the investigation of algorithms even for dynamical resource access [1] as the basis for dynamical configuration of communication systems. The consequent trend of moving processing from the hardware, to the software, has lead to the Software Defined Radio (SDR) paradigm [2]. To satisfy the challenges introduced by this communication technology, one of the possible solutions proposed in literature is given by the Software Communications Architecture (SCA) [3]. SCA is a non-proprietary, open architecture framework that tells designers how elements of hardware and software have to operate in harmony within a software defined radio. It is a set of design constraints. If a developer designs a system according to these design rules, the system will be portable to other SCA implementations regardless of what operating system or hardware that implementation utilizes. The main features of SCA are summarized as follows: – –

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M. Colizza e-mail: [email protected] M. Faccio e-mail: [email protected] F. Santucci e-mail: [email protected]

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it is an architecture with a layered structure; at the top level, an Operative Environment (OE) is available which provides support for a component based design; the Operative environment is accessible by means of a middleware; each component may be developed by using any language, such as Java, C++; a system results in a set of components which exchanges data and remote procedures call, by means of a communication service of type client-server, that is provided by CORBA (Common Object Request Broker Architecture) within the Operative Environment; the client-server communications are managed by means of the ORB (Object Request Broker) bus;

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SCA describes only the behavior expected from components, waveforms, and operational environments; it does not provide implementation details; in the SCA platform any aspect of a target platform, such as processors, FPGA or DSP resources, Real Time Operative Systems (RTOS), is hidden by the OE to the top level.

3. tools for sound and easy support of tracking projects requirements into the developed code are not available; 4. tools for supporting automatic generation of reports that rely on qualitative and quantitative performance assessment are not satisfactory; 5. high level cross verification for performance analysis (e.g. logic trigger) is not addressed.

Furthermore, the reconfiguration ability covers all layers of a protocol stack. This high degree of reconfigurability opens new possibilities in terms of dynamical access to services, while bringing new problems due to the dynamicity introduced in the protocol stack. Indeed the design and even the simulation of wireless systems is non trivial, because the number of nodes is usually large, different radio technologies are involved, different traffic types (i.e. voice, data, multimedia) are involved, various mobilities, channel and propagation models are assumed, and there are various available bandwidths. In particular MANETs, with high degree of adaptivity and ability to (re)configure in application scenarios, motivate research efforts in developing methods and tools for supporting a complete and sound design flow that encompasses:

With the motivation of bringing improvements in the depicted technical framework, we are involved in several projects, e.g. ARTEMIS PRESTO http://www. presto-embedded.eu/ and FP7 NoE HYCON2 http://www. hycon2.eu/ both co-funded by the European community. Although the main research problems are different, both projects are conceived with the aim of riding over limitations due to the currently used technologies. Specifically, the PRESTO project is mainly focused on:

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1. waveform/protocol specification; 2. validation through accurate simulations and early stage testing; 3. rapid code development on selected target platform. Despite those needs of high degree of complexity, it can be observed that state-of-the-art software design methodologies, e.g. RUP [4], are too complex and computationally intensive to be used in the field of networks protocol stacks. Therefore, we aim at defining a methodology that: – –

– –

allows the implementation of distributed architectures through the use of design patterns; is based on design pattern using a reduced set of components, each of which may be hardware (HW) or software (SW); allows to put together the design patterns using a technique to exchange messages; enables easy traceability among system requirements, architectures and code.

Moving from inspections of simulation environment, a (non exhaustive) list of current weaknesses in the available tools (e.g. NSx, OPNET, OMNET) can be provided as follows: 1. simulators are not typically conceived to offer the opportunity to reuse the developed code for subsequent implementation on the target device; 2. the problem of inserting and removing measurement code inside business code is not typically addressed;

1. the improvement of test-based embedded software development and validation procedure, while considering the constraints of industrial development processes; 2. the definition of functional and performance analysis with platform optimization at an early stage of the development process. The project also intends to explicitly consider some industrial development constraints: simplified use of tools, smooth integration in current design processes, framework of tools that is flexible enough to adapt to different process methodologies, design languages and integration test, platform modeling for early comparison of results with real scenarios and fast prototyping. Through the WP6, HYCON2 also pursues research advances in developing methods and tools for analysis and design in the broad range of complex and networked embedded systems. In this paper we focus on the definition and development of a set of tools (suite) to support sound design, appropriate verification/test and development of embedded software for SDR systems. Specifically, we are defining a workflow whose qualifying features are as follows: 1. the design of a system or subsystem in a network protocol stack is model-based; 2. the amount of manually written code (firstly for simulation) is minimized, while the code can be obtained from the model through a set of procedures for automatic code generation; 3. the probes for measurement may be placed in the model; they can be automatically switched off when the model is used to produce code for target devices; 4. the model holds true independently of the target device; 5. when a probe for measurement is selected, the generated track can be automatically added to a technical report.


The suite is intended to provide the designer with the capabilities of: 1. developing a protocol model through the composition of library components; 2. generating code for simulation and test in a network simulator starting from the model; 3. generating code for a target device, starting from the model; 4. integrating protocol models with application related models, e.g. those encountered in the context of networked control systems. The paper will report on already achieved results in terms of developed models and simulation environments. The paper is structured as follows: in Section 2 we describe the proposed architecture, with Subsections related to the illustration of TM, the requirements of an environment supporting TM, the basic Tissue Patterns (TPs) and a case study; in Section 3 TM is used to define a simulator architecture and finally Section 4 concludes the paper and outlines future works.

2 Architecture for Design 2.1 Methodology and Architecture Definition Data structures are critical factors when dealing with the reusability of simulation code on target devices. This is due to the fact that the target device hardware has limited resources with respect to the host platform. Code traceability, with respect to requirements, reporting on design, development and test are other critical factors for treatment of protocol stacks such as those in MANETs. This section proposes an implementation of a new methodological approach, namely Tissue Methodology (TM), [5] whose aim is to define an architecture taking into account the previous critical factors. The methodology proposed in [5] emphasizes the following modeling paradigms: 1. 2. 3. 4.

modular programming [6–8]; patterns programming [9]; events oriented programming [10]; fractal programming [11].

The design pattern used in this methodology is called TP. The TP has the aim of enabling the design by using a basic module which is able to: 1. receive and generate events (H); 2. process events (P);

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3. store a state space or other information (S); 4. increase their ”skills” through interaction with other units or reconfiguration. The growth of tissues is achieved through the repetition of basic units, as well as the fractal structures; the link between H, S and P is represented by functional calls, an access to remote resources, or any communication protocol. The basic units can be used to build macro structures that can be in turn used for growing a tissue. Following this approach, a protocol stack can be rethought as shown in Fig. 1 where a model made up of a basic TP is presented; each of the H modules receives events from other layers and, at the same time, it generates events towards other layers. In every layer each event is processed by the P module. The data exchanged between two layers, or the data needed by a P module, are stored in the S module. It is worthwhile mentioning that each S module represents a system to store data (e.g. a bank of memories, a remote data source). Both the P module and the H module may retrieve a specific data set through an identification code. Through this code, the module, which needs to use the data set, receives a handle; this handle enables the use of the data set in read/write mode (Fig. 2). The data set could be composed by basic types (e.g. boolean, int, double) or structured data type. If the designer defines all data types required by the project, the S module may be implemented through automatic code generation. Moreover, using standardized data structures, or rather data structures gathered from predefined ones, the measure code may be generated in automatic mode too and it may be automatically switched off when the model is used to produce code for the target device. Along with this vision, it is important to define techniques in order to: 1. exchange information between different layers, or rather, between different modules of type H; 2. index each data set; 3. manage events generation and subsequent processing. In next sections we suggest a SW implementation for these techniques. 2.2 Requirements of the Tissue Integrated Development Environment This section deals with the definition of requirements that relate to the Tissue Methodology. The first step consists in isolating the main characteristics of each paradigm that is at the basis of the Tissue Methodology. The modular programming is the first concept to be taken into account. In order to be able to build a system through input/output functionalities and memory partitioning, the environment has

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Figure 1 Use of the basic TP to model a protocol stack.

SAP6

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to guarantee a support for the creation of the module. The latter one has to be supplied with input/output ports for receiving and generating events (Req.1). Moreover, a handle has to be provided by each module to allow interaction with other modules (Req.2). In order to exchange events, a communication protocol (Req.3) is also required. One of the possible protocols to be used is the Message Passing Interface (MPI) [12]; this protocol is particularly interesting because of the existence of a version for real time systems, named Real Time MPI. This is appealing for our purposes, because the environment we want to propose is intended to enable the designer to simulate the architecture that is going to be implemented on the target device (Req.4). With this design choice, starting from architectural models, it is possible to automate, through automatic code generation, the implementation of the model either for simulation or for prototyping activities, without any need for porting, any redesign and new testing activity (Req.5). Basing on previously described requirements, the event based simulator OMNET++ has been chosen. The next section is devoted to describe the Sequence diagram of the design pattern which is depicted in Fig. 1.

ID

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The issue of designing a protocol layer using TM can be approached by the use of the TP shown in Fig. 3. This pattern represents the basic configuration to build a system, where the tasks to deal with are shared among different entities, that can be classified in logical layers. However, if a cross layer interaction is required, the architecture depicted in Fig. 1 inherently supports this interaction. This concept can be explained with the help of Fig. 4, where other elements are used in addition to the Data Set, i.e. the operation set, containing the handles for cache functionality, and the event set, containing the handles to the events. Each data set is characterized by two coordinates, the handle of the reference storage ’sHandle’ and the handle of the specific data ’handleDk’ with k referring to indexes of data types in the following ranges: –

[1, N ] for the data set;

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[1, M] for the operation set;

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[1, L] for the event set.

P

H Figure 2 Data set.

H

2.3 TP for Protocol Design

S

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handleN Figure 3 Basic TP.


sHandle

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Table 2 802.15.4 modulations.

H Identify events Answer To EventN

Exchange data Data Set ID-Data ID handleD handle IDD1 handleD1

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Binary PSK at 868 MHz Offset Quadrature PSK at 2400 MHz

EventN Call event operation execute operations

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Event Set ID-Event ID Operation handle IDE1 handleP1

IDEN

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Figure 4 Sequence Diagram for a Basic TP.

It has also to be noticed that: 1. all data set can be moved from one storage module to another; 2. any data set can be handled from any P module or H module; indeed, it is possible to use the pair (sHandle,handleDk) from any module. Moreover, the exploitation of previous advantages facilitates measurement operations. Before going into the development of techniques to automate code generation for TP implementation, and hence the protocol stack, the feasibility of these methods needs to be assessed. The next section proposes an application of TP to a 802.15.4 physical layer.

In order to verify the feasibility of this methodology and to spotlight the differences with a traditional design, like OOP (Object Oriented Programming), we started from an existing project and we redesigned this project through the reuse of existing code in order to produce a TM compliant implementation. The project is part of a framework developed to simulate MANET networks through OMNET++. The framework is called INETMANET, and we focused on the physical layer 802.15.4 implementation. The conversion has been achieved through the following steps: 1. definition of data types to cover all data managed into the PHY layer; 2. association of a unique identification code to each data type; 3. association of a unique handle to each data type. In Table 1 we show the implementation of the handles for each data type, based on the “map” data structure of C++ STL (Standard Library) and their informal description. All these methods are located in the P module. This closes the implementation of the basic TP of Fig. 4. Now, by resorting to Software Defined Radio paradigms, the reprogramming of a device has to be taken into account. According to the Tissue architecture, an example of reconfigurability is shown. Let us consider Table 2, wherein we

Table 1 Implemented methods and abstract references. Function

Abstract reference

Description

select802154Data set802154Dat hs802154PHY hS802154PH fCSend fCSelfMsg fCancEvent deleteSelfMsg updateDisplayString handlePrimitive handleUpperMs handleSelfMsg handleLowerMsgStart bufferMsg

F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13 F14

it returns the handle specified by the typeData ID it adds a new data structure In order to retrieve the handle of the storage module In order to retrieve the handle of the storage module to control the generation of events in the H module to set internal events (e.g. Timer) to cancel an event that is expired or has been processed to cancel an internal event that is expired or has been processed to display information at run time to manage the primitives exchange between 802.15.4 PHY and MAC layers to manage messages originated from the mac layer to manage internal messages to manage messages coming from lower layers to manage the queue of the air frames Protocol Data Units

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Java storage

tuple type

e.g. routing table

position

time

nodo node

Figure 7 Java storage class.

3 Architecture for Simulation Figure 5 Example of dynamic TP reconfiguration.

assume that two modules of type P are defined for implementation of two modulation formats at the physical layer: specifically, P1 for the BPSK format, and P2 for O-QPSK, [13]. Let us assume that the early (default) implementation includes the module P1; when the H module of the PHY layer identifies an event which encodes a command for switching the modulation format, it starts a reconfiguration procedure which includes the following steps: –

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The H module of PHY layer forwards the detected event to a H module devoted to the management of reconfigurations; The latter H module performs the following operations: – – –

It receives the message and identifies the modules, P2 and S2, devoted to process the message; It produces an instance of S2 and P2; It updates the Tissue Pattern of the physical layer as shown in Fig. 5.

H

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complete traceability of algorithm behaviours running on the single device and network wide behaviours; scalability both in time (e.g. increasing of the number of nodes) and high data streaming; reconfigurability of check points.

According to TM, a simulation of an N-nodes network can be represented with the architecture shown in Fig. 6. The Simulation Architecture consists of N instances of the Node Architecture (Fig. 1), which is in turn composed by basic TPs. It is worth noting that each Node Architecture may be characterized by one or more storages. On a higher level of abstraction the set of all storages of each node can be viewed as a network storage exchanging data with a Distributed file System (DFS), as in Fig. 6. All storages can be viewed as sources of data streams contributing to a single data flow. The latter one has to be

S

PHY

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H

Once the code of a protocol stack is produced, network simulations for a set of nodes is carried out to evaluate its performances (e.g. end-to-end delay, throughput, power consumption, reliability etc.). We assume that the code to be simulated is the same code used for the target device; this implies that the accuracy of the protocol stack code is particularly complex and so is the simulation, both for the execution time and the amount of generated data. Indeed, a network data acquisition system has to guarantee:

P

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DT

tuple type 1

Figure 6 Network Storage concept.

TM DFS

tuple type k tuple type

Data node

Figure 8 Tuple-time plane for node i.

chunk

t


321 Table 4 Storage cloud management.

Node file

data node process ID

data node server IP

node range

file list

Node i tuple type x tuple type DTx

– – –

time

Figure 9 Storage space.

stored and, due to its dimensions, a distributed file system may be helpful. This implies that the basic storage unit is given by a file. There are two main possibilities of extracting data streams that represent the file content. The first one is given by the introduction of a measure module, i.e. the observer, inside each node. The observer needs two types of data: the type of the tuple to be observed and the sampling time. These data are placed in an XML file. The second solution envisages the exploitation of a listener, that belongs to the simulation environment. The listener senses the events occurring during the simulation and gathers information coming from each event. In both cases the content of the file is achieved through the following steps: 1. the gathered information is placed inside objects which are instances of C++ classes; the use of C++ language is motivated by the fact that we are simulating in the OMNET++ environment; 2. a Java application is called whose inputs are given by clones of the previously described C++ objects; clones are necessary in order to avoid memory access conflicts and, since the Java application is managing C++ objects, Java Native Interface (JNI) are here used; 3. clones of C++ objects are copied inside Java objects by the Java application, as sketched in Fig. 7; 4. Java objects are serialized within the file; with this approach the data stream is projected into a plane time-tuple as shown in Fig. 8; 5. the file is closed when its dimension exceeds a specific threshold; the minimum value for the threshold is given by 64 MByte as established in [15]. The analysis of simulation results is equivalent to a research in a 3-dimensional space where dimensions are: time, tuple type and node ID (see Fig. 9). In this way the problem of information searching is orthogonalized, [14] with the following search rules: –

tuple-type based search;

Table 3 Index simulation table. tuple ID

tuple type

start

stop

filename

node ID

node ID based search; T based search; a combination of two or more previous criteria.

These criteria can be supported by an SQL database providing Tables 3 and 4. The use of those tables allows to support many queries. As an example, let us consider the problem of finding all tuples of a certain type (e.g. position, routing table, etc.) within a specific time interval for a specific node: 1. find all the tuples of the specific type for the specific node; 2. within the result of the previous query, find the tuples belonging to a certain time interval; 3. read the corresponding filenames; 4. retrieve the files associated with the previous filenames. An inverse operation is even possible. Indeed another Java application may receive the extracted files as inputs. For each file, the Java application has to provide for: – – – –

file reading (Java streaming acquisition); de-serialization of Java storages inside each file; extraction of data required from each Java storage; data forwarding to a viewer.

4 Conclusions and Future Works In this paper we faced the issue of the design and development of reconfigurable wireless networks. In order to meet advanced requirements in challenging contexts, in this paper we proposed a methodology, called Tissue Methodology, that is a combination of four modelling paradigms: modular programming, patterns programming, events oriented programming, fractal programming. Design patterns developed by the proposed methodology are called Tissue Patterns. They are built through the use of only three basic components (i.e. S, H, P) that can be connected using exchange messages techniques. Basing on this framework, distributed architectures can be developed. An example has been devised to validate the methodology, with particular emphasis on exploitation of modular design and the support of reconfigurability. Indeed the legacy code of the IEEE 802.15.4 PHY layer, belonging to the INETMANET framework, has been modelled using the Basic Tissue Pattern. The MPI has been used as exchange message technique.

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In addition, we extended the use of TM to the simulation level by proposing a method for simulation data storage on a distributed storage system. Ongoing work is focused on development of tools and validation of the TM methodologies in case studies proposed in out research projects (MANETs are a major concerns, as stated), with close links to industrial partners. Future works will expand toward the broader and emerging context of Software Defined Networks.

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Maurizio Colizza was born in L’Aquila, Italy, in 1977.He received the laurea degree in Electronic Engineering from the University of L’Aquila, Italy, in 2005, discussing a thesis entitled ”Study and Application of an Environment Codesign for Embedded System Design”. In 2008 he followed the second level Master in ”Technologies, Applications and Service in etherogeneous radio networks” at the University of L’Aquila. He is current a third year of PhD student at University of L’Aquila. Since 2006 he has collaborated with the University of L’Aquila as Research Engineer in European projects, such as ESSOR, HYCON2, PRESTO and CRAFTERS. In 2008 he received the Best IP Awards for a SDR IP during IP Conference in Grenoble, France. His current research activities regard the definition of a methodology to design, to simulate, and to test embedded system networks: the development of an enterprise application to support this methodology is one of the main goal.

Marco Faccio is currently Associate Professor of Digital Electronics Systems at University of L’Aquila. His present research interests include the hardware design of algorithms and design methodologies but also CMOS image sensor technology, its interfaces and communications, the AtoD conversion for smart sensor and low voltage digital systems. He is co-author and author of ninety international papers in this research fields. He was born in Tortoreto, Italy in 1954 and he received the degree in Electrical Engineering from L’Aquila University, Italy in 1979. He was worked in industrial environment about microprocessor, data acquisition systems and IC design. Since 1989 he has been a Researcher of applied electronics at Department of Electrical Engineering of L’Aquila University and he was also visiting professor to ETH of Zurich in 1995.

J Sign Process Syst (2013) 73:315–323 Claudia Rinaldi was born in Rieti, Italy, in 1981. She got her University Degree in Electronic Engineering (cum laude) from the University of LAquila, at the Faculty Engineering, within the Course of Telecommunications. The Master Thesis (available both in Italian and in English) was developed at the Royal Institute of Technology (KTH) of Stockholm under the supervision of professor Karl Herik Johansson and professor Niels Moller. In 2006 she graduated in Trumpet at the Conservatory of Music A. Casella of LAquila and in 2013 she got a degree in Music and New Technologies at the same institute. In 2005 she participated to the exam for PhD application and obtained the best placement. She thus was PhD student from 2005 to 2009 at the Center of Excellence DEWS of The Faculty of Engineering of the University of LAquila where she got the PhD in 2009 with a thesis with the following title: Advanced Modeling for Wireless Communications and Distributed Control over Wireless Networks. In 2005 she had a contract by the Center of Excellence DEWS for research on modelling and optimization of transport techniques for adaptive wireless systems.Since 2005 she was a tutor for students thesis of both Bachelor and Master Degree. She also made seminars, exercises and exams for the courses of Signal Analysis and Elaboration, Wireless Communications, Electrical Communications. She made some lessons for a refresher course in Technolabs, LAquila, about the MAC level of WiMax Standard. She is the authors of papers published in various conference proceedings and international journals and she has been a reviewer for various international journals.Her main research activities are focused on design, modelling and optimization of communication algorithms with particular interest on the physical layer of the protocol stack, in the use of technology in artistic fields with particular emphasis on application of wireless sensor networks in electronic music and in the topics of the software defined radio for the development of transmission systems responding to cognitive radios paradigms.

323 ´ Fortunato Santucci (S93´ ´ of the IEEE) was M95-SM 00 ´ born at LAquila, Italy, in 1964. He received the laurea degree and the Ph.D. degree in Electronic Engineering from ´ the University of LAquila, Italy, in 1989 and 1994, respectively. In 1989 he was with Selenia Spazio S.p.a., Rome, working on VSAT networks design. In 1991-1992 he was at the Solid State Electronics Institute (I.E.S.S.) of the National Research Council (C.N.R.), Rome, doing research on superconductor receivers for millimeter wave satellite systems. Since 1994 he has been with the Department of Electrical Engineering, Uni´ versity of LAquila, where he currently holds the position of Associate Professor and is chairing the Telecommunications Engineering program. He was a visiting researcher at the Department of Electrical and Computer Engineering of the University of Victoria, BC. His current research activity is focused on communication theory, access control and radio resource management in future mobile radio systems, technologies and architectures for wireless embedded networks. He has participated in major national and european research programs in wireless mobile communications and coordinates research programs funded by industrial partners. Since 2000 he has served as an Editor for the IEEE Transactions on Communications and, more recently, also for Kluwer Telecommunications Systems and Hindawi. He is in the TPC of major international conferences and has been author and speaker of invited/plenary talks in several conferences. Dr. Santucci has organized national conferences and international workshops. He has been a guest editor for special issues in international journals. He is a Senior Member of the IEEE and is in the IEEE Communications Theory Committee. He is in the Board of Directors of the Center of Excellence DEWS at the University of L’Aquila, of the Radiolabs consortium, and also in the Technical Committee of CNIT. He is in the Executive Committee of the Network of Excellence HYCON. He has been a reviewer of research proposals for national and international funding agencies and is a co-founder and a member of the board of governors of the spin-off company WEST Aquila srl.