Sistemi di Radiocomunicazione From Software Defined Radio to Cognitive Radio

1. Prof. C.S. Regazzoni DIBE Sistemi di RadiocomunicazioneFrom Software Defined Radioto Cognitive Radio

2. Outline Introduction From Software Defined Radio to Cognitive Radio Software Defined Radio vs Cognitive Radio Cognitive Radio Cognitive Cycle MItola’s CC, Haykin’s CC, simplified CC Knowledge representation Embodied Cognition Bio-inspired Model

3. Outline Application examples in CR E2R Infomobility framework Cognitive Cycle in practice Analysis Phase Decision Phase Results Remarks

4. Introduction

5. From SDR to CR – SDR Technologies Historically, radios have been designed to perform a given task As upgrades were desidered to increase capability, reduce life cycle costs, and so forth, software was added to the system design for increased flexibility In 2000 a SDR has been defined by FCC as: A communication device whose attributes and capabilities are developed and/or implemented in software

6. From SDR to CR – SDR Technologies • The required additional flexibility and addiotional capabilities have been provided step by step • The radio system capabilities can evolve to accomodate a much broader range of awareness, adaptivity and learning

7. Software Capable Radio A software capable radio has the following characteristics Fixed modulation capabilities Manage a small range of frequencies Limited data rate Ability to handle data under software control

8. Software Programmable Radio A software programmable radio has been designed upon a software capable radio and it has the following additional characteristics: Ability to add new functionalities through software Advanced networking capabilities

9. Software Defined Radio Software Defined Radio (SDR) systems main characteristic is the complete adjustability through software of all radio operating parameters. Required reconfigurability is provided thanks to software management of the considered system It is a practical reality today, thanks to the convergence of two key technologies: digital radio, and computer software.

10. Aware, Adaptive and Cognitive Radios Radio that sense all or part of their surrounding environment are considered aware systems A radio must additionally autonomously modify its operating parameters to be considered adaptive If a radio is reconfigurable, aware, adaptive and learns COGNITIVE RADIO

11. Aware Radio It is equipped with sensors able to gather environmental information In general many kind of sensors can be considered in Aware Systems, e.g. antenna, microphone, camera, probes, etc. The key characteristic that raises a radio to the level of aware is the consolidation of environmental information not required to perform simple comms

12. Adaptive Radio Frequency, istantaneous bandwidth, modulation scheme, error correction coding, channel mitigation strategies, data rate, transmit power, etc, are operating parameters that may be adapted Example: A FHSS radio is not considered adaptive because once programmed for a hop sequence it is not changed. A FHSS radio that changes hop pattern to avoid/reduce collisions may be considered adaptive.

13. Cognitive Radio A CR has the following characteristics: Sensors creating awarness of the environment Actuators enabling interaction with the environment Memory and model of the environment Learning capability that helps to select a specific action or adaption to reach a specific goal Autonomy in action (unsupervised system)‏ An engine able to take constrained decisions

14. Comparison: from SDR to CR

15. Cognitive Radio

16. Cognitive Radio Cognitive radio designed upon SDR, has been proposed as the means to promote the efficient use of the spectrum by exploiting the existence of opportunities

17. Cognitive Radio According to the Encyclopedia of Computer Science cognition encompasses the following steps: Mental states and processes intervene between input stimuli and output responses The mental states and processes are described by algorithms The mental states and processes lend themselves to scientific investigations.

18. Cognitive Radio Moreover, the interdisciplinary study of cognition is concerned with exploring general principles of intelligencethrough a synthetic methodology termed learning by understanding. Putting these ideas together and bearing in mind that cognitive radio is aimed at improved utilization of the radio spectrum, Haykin offers the following definition for cognitive radio

19. Definition CR is an intelligent wireless communication system that is aware of its surrounding environment, and uses the methodology of understanding-by-building to learn from the environment and adapt its internal states to variations in the perceived RF stimuli by making corresponding changes in operating parameters in real-time, with two primary objectives in mind: highly reliable communications whenever and wherever needed; efficient utilization of the radio spectrum.

20. Definition Common keyword in this context are: Spectrum awareness the understanding of what is happening in the electromagnetic spectrum Self Adaptation the capability to adapt the system parameter according to an evolving external scenario Intelligence or cognition the capability to learn from the interaction with the environment Efficiency The capability to efficiently exploit the available radio spectrum

21. Cognitive Radio - models In general the Cognitive Systems can be characterized by different cooperative phases, which, together with continous learning, are really powerful tools for all kind of applications. CR behavior can be modeled as a cognitive cycle In the literature different cognitive cycles have been provided in order to describe CR behavior

22. Mitola’s Cognitive Cycle According to Mitola it is possible to model the CR behavior as follow: Stimuli enter the CR as sensory interrupts, dispatched to the cognitive cycle for a response CR sequentially observes (senses and perceives) the environment, orients itself, creates plans, decides, and then acts.

23. Mitola’s Cognitive Cycle Observe (Sense and Perceive)‏ The CR observes its environment by parsing incoming RF stimuli. Orient determines the significance of an observation by binding the observation to a previously known set of stimuli Plan reasoning over time

24. Mitola’s Cognitive Cycle Decide selects among the candidate plans Act initiates the selected plans using actuators which access the external world or the CR’s internal states. Learning Learning information and experiences, together with decision, is the most important capability for a cognitive system

25. Mitola’s Cognitive Cycle

26. Haykin’s Cognitive Cycle Haykin focuses on three on-linecognitive tasks: Radio-scene analysis: estimation of interference temperature of the radio environment; detection of opportunities Channel identification: estimation of channel-state information (CSI); prediction of channel capacity Transmit-power control and dynamic spectrum management.

27. Haykin’s Cognitive Cycle The cognitive process starts with the passive sensing of RF stimuli and culminates with action. Tasks 1) and 2) are carried out in the receiver, and task 3) is carried out in the transmitter. the cognitive module in the transmitter must work in a harmonious manner with the cognitive modules in the receiver

28. Haykin’s Cognitive Cycle 1 3 2

29. Cognitive Cycle: a comparison Haykin defines the behavior of CR system through a Cognitive Cycle, similar to Mitola's one, but much more clustered in macro-phases Mitola is much more interested on the impact of the cognitive capabilities onto the communications market, Haykin faces the problem from a more general point of view. Conversely, both researches agree on the fact that the SDR systems are the natural platform for the implementation of CR devices.

30. While in the Mitola’s vision the CR is suited to realize the user’s preferences, in the Haykin’s one it is well explained a cognitive communication between a transmitter and a receiver. In both of the previous visions it is clear the effort to model the CR s an entity able to reason about and analyze the external world modify its internal configuration to reach the best solution Cognitive Cycle: a comparison 30

31. Cognitive Cycle for CR apps: A simplified vision ACTION DECISION SENSING ANALYSIS In general, the behavior of a Cognitive Systems can be characterized by foursequential cooperative phases, which, together with continuous learning, are really powerful tools for all kind of applications. These phases constitute the four main capabilities of the Cognitive Cycle: Physical World Learning 31 31

32. Cognitive Cycle: A simplified vision Sensing is a passive interaction component: the system has to continuously acquire knowledge about the interacting objects and its own internal status E.g. Sensing process can be view as the scan of the electromagnetic environment by an antenna or the acquisition of an image sequence by a camera 32 32

33. Cognitive Cycle: A simplified vision Analysis perceived raw data need an analysis phase to represent them and extract interesting filtered information E.g. Analysis process extracts information of interest like users’ positions in an angle-frequency map or features used for classification or tracking 33 33

34. Cognitive Cycle: A simplified vision Decision the intelligence of a CS is expressed by the ability to decide for the proper action, given a basic knowledge, experience and sensed data It is one of the most critical and complex phase of the cycle 34 34

35. Decision phase There are many different approaches to decision phase: • Rule Based algorithm  They are based on the paradigm IF …. THEN … • Semantic Networks  It’s a graph designed by an ensemble of nodes linked each other by arches: nodes represent objects, situation or events, while arches mean their relations • Decision Trees  It’s a framework designed on a tree where every internal node represents an adjective and every leave represents a label of class • Memory Based Reasoning  With this technique it is possible to classify basing on previous experiences We will focus on bio-inspired algorithms for knowledge representation and decision phase EMBODIED COGNITION APPROACH 35 35

36. Cognitive Cycle: A simplified vision Action expresses the active interaction the CS can take in relation to its decision. The system tries to influence its interacting entities to maximize the functional of its objective E.g. This phase represent how the CS interacts with the environment with actuators and communication systems likes antennas 36 36

37. Learning Learning information and experiences, together with decision phase, is the most important capacity for a cognitive system. There are many different approaches to this task that can be generally divided in: • Supervised Algorithms (Artificial Neural Network, Support Vector Machine, Bayesian Learning)‏ • Unsupervised Algorithms (Self Organizing Maps, Radial Basis Function Network, Reinforcement Learning)‏ We will focus on a bio-inspired learning algorithm AUTOBIOGRAPHICAL MEMORY 37 37

38. Knowledge representation and organization The cognitive cycle represents a general framework  It is necessary to specify how the knowledge is managed and processed within each stage of the cycle A knowledge representation and organization is necessary 38

39. Knowledge representation and organization In general the knowledge managed by the cognitive cycle can be: • an a-priori identification, at a symbolic level, of all the knowledge necessary to perform the different phases of the cycle; • acquired through experiences. It can be organized according to two principal models: • The former model tries to describe the knowledge in a symbolic and semantic way  i.e. the classical rule-based approach for AI (Expert Systems); • Embodied cognition. 39 39

40. Rule-based approaches A Rule-based expert system is a representation of the human beings natural reasoning and problem-solving paradigm. It models the human’s production system using the following modules: 40 40

41. Rule-based approaches Knowledge base - models a human’s long term memory as a set of rules. Working memory - models a human’s short term memory and contains problem facts both entered and inferred by the firing of the rules. Inference engine - models human reasoning by combining problem facts contained in the working memory with rules contains in the knowledge base to infer new information. 41 41

42. Embodied Cognition • Embodied Cognition approach takes inspiration from Robotics works of Rodney Brooks and looks at intelligence as to an emergent behavior of a set of agents. • This approach is based on a model of representation of the knowledge which describe in a priority manner the physical capabilities of action of the entity where happen decision and action. 42 42

43. Embodied Cognition Endo-sensors Entity Spatial internal map Action Decision • Knowledge can be viewed as organized following spatial maps centered on the entity, where information are represented as created by processes of analysis and decision at different semantic levels. • To mantain this separation between decision and action it is necessary a mechanism related to perception of events derived by actions (endo-sensors)‏ 43

44. Evolution following Embodied Cognition approach DecisionAction Command Evolution Command Internal sensor Decision Action Feedback Feedback Map of feedbacks Stage1: Decision and Action use only internal information (endo-sensors)‏ Map of commands 44 44

45. Evolution following Embodied Cognition approach Commands Internal Analysis Int. analysis Int. sensor Virtual internal sensor Decision Feedback Action Ext. analysis Ext. sensor Feedback External Analysis Map Map of commands Analysis map Stage2: Evolution leads to distinguish between internal and external state  Decision and Action use internal information (endo-sensors) and external (eso-sensors)‏ 45 45

46. Evolution following Embodied Cognition approach Stage3: Distinction between internal and external state leads to generation of a consciousness (distinction between itself and other from itself)‏ Externalspatialmap Command Feedback Interacting Entity 46 46

47. Embodied cognition: a possible definition Following Anderson definition: “it focuses the attention on the fact that most real-world thinking occurs in very particular (and often very complex) environments, is employed for very practical ends, and exploits the possibility of interaction with and manipulation of external props. It thereby foregrounds the fact that cognition is a highly embodied or situated activity and suggests that thinking beings ought therefore be considered first and foremost as acting beings” 47 47

48. Evaluation of Embodied Cognition Recent studies of neurophisiology have confirmed, ata biological level, the effectiveness of this approach. Eg. one of the primary goal of intelligent multicellular organisms evolving toward higher level organisms is to use contextual information obtained through sensing to move in the surrounding environment to reach a safer or a food reacher point. In the human brain, these kind of motions are generated by specific groups of neurons called Fixed Action Patterns (FAPs), whose output is able to modulate motor muscles actions according to a codified sequence of effector signals. Sequences are modulated by FAPs, basing on the contextual information acquired through the senses. 48 48

49. Embodied Systems The representation of the internal knowledge in embodied systems, and hence the description of context, is strictly linked with the motion possibilities of the entity itself. In general the body of the system has an important role in the evolution of the entity  The body can be considered not only as the instrument to perform the only action phase but this concept can be extended for every phase of the cycle. E.g. Make more fine the sensing phase lead to a different representation of the knowledge (more information to manage) respect to a coarse sensing phase 49 49

50. A Bio-inspired Model Up to now some fundamental concepts have been pointed out: • How a conscience can be represented Damasio’s approach core self, proto self, autobiographical memories, autobiographical self • The relations between actions and consciousness  Llinas approach FAP • How the knowledge representation can influence the cognition process embodied cognition • How can be represented a living entity related to its environment cognitive cycle 50