Software radios arc cmcrging as platforms for multiband multimode personal communications systems. Radio etiqucttc is the set of RF bands, air interfaces, protocols, and spatial and temporal patterns that modcrate the use of the radio spectrum. Cognitive radio extends the software radio with radio-domain model-based rcasoning about such ctiquettes. Cognitivc radio enhances the flexibility of personal services through a Radio Knowlcdge Representation Language. This language reprcsents knowledge of radio etiquettc, devices, software modules, propagation, nctworks, user needs, and application scenarios in a way that supports automated reasoning about thc needs of the user. This empowers software radios to conduct expressivc ncgotiations among peers about the use of radio spectrum across fluents of space, time, and uscr context. With RKRL, cognitivc radio agents may actively manipulate the protocol stack to adapt known etiquettes to better satis$ the LISCT'S nccds. This transforms radio nodes from blind executors of predefined protocols to radio-domain-aware intclligent agents that scarch out ways to dclivcr the services thc uscr wants even if that uscr docs not know how to obtain them. Softwarc radio [l] provides an idcal platform for thc rcalization of cognitive radio. Cognitive Radio: Making Software Radios More Personal Global System for Mobile Communications (GSM) radio's equalizer taps reflcct the channel multipath structure. A network might want to ask a handset, "How many distinguishable multipath components are you seeing?" Knowledge of thc internal states of the equalizer could be useful bccause in some reception areas, thcrc may be little or no multipath and 20 dB of extra signal-to-noisc ratio (SNR). Software radio processing capacity is wasted running a computationally intcnsive equalizer algorithm when no cqualizer is necessary. That processing capacity could be diverted to better use, or part of the processor might be put to sleep, saving battery life. In addition, the radio and network could agree to put data bits in the superfluous embedded training scquence, enhancing the payload data rate accordingly.' Two problems arise. First, the network has no standard language with which to posc a question about cqualizer taps. Sccand, the handset has the answer in the time-domain structure of its equalizer taps, but cannot access this information. It has no computational description of its own structure. Thus, it does not "know what it knows." Standards-setting bodies have been gradually making such internal data available to networks through specific air interfaces, as the needs of the technology dictate. This labor-intensive process takes ycars to accomplish. Radio Knowledge Represcntation Language (RKRL), on the other hand, provides a standard languagc within which such unanticipated data exchanges can be defined dynamically. Why might the need for such unanticipatcd exchanges arise? Debugging new software radio downloads might require access to internal software parameters. Creating personal services that diffcrentiate one servicc provider from another might be enhanced if the provider does not need to expose new ideas to the competition in the standards-setting process. And the time to deploy those personalized services could be reduced. Cognitive radio, through RKRL, knows that the natural lan- guage phrase equalizer taps refers to specific parameters of a tapped delay-line structure. This structure may be implemented in an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or an algorithm in a software radio. Since a cognitive radio has a model of its own internal structurc, it can check the model to find out how the equalizer has been implemented. It then may retrieve thc register values from the ASIC (e.g., using a JTAG port) or find the taps in the proper memory location of its software implementation. A radio that knows its own internal structure to this degree does not have to wait for a consortium, forum, or standards body to define a level H33492.x7 radio as one that can access its equalizcr taps. The network can pose such an unanticipated question in (a standard) RKRL, and any RKRL-capable radio can answer it. To enable such a scenario, cognitive radio has an RKRL model of itself that includes the equalizer's structure and function, as illustrated in Fig. 1 . In this example, the radio hardware consists of the antenna, the radio frequency (RF) conversion module, the modem, and the other modules shown in the hardware part of the figure. The baseband processor includes a baseband modem and a back-end control protocol stack. In addition, this processor contains a cognition cngine and a set of computational models. The models consist of RKRL frames that describe the radio itself, including the equalizer, in the context of a comprehensive ontology, also written in RKRL. Using this ontology, the radio can track the user's environment over time and space. Cognitive radio, then, matches its internal models to cxternal observations to understand what it means to commute to and from work, take a business trip to Europe, go on vacation, and so on. Clearly, significant memory, computational resources, and communications bandwidth are needed for cognitive radio, so this technology might not be deployable for somc time. In addition, a collcction of cognitivc radios may not require human intervention to develop their own protocols. Initially, intervention will be required in order to ensurd that networks of such radios remain stable (or that we know who to blame if this is not the case). Networks of such radios are complex adaptive systems [ 2 ] , the study of which is an cmerging discipline concerned with the nonlincar behavior of large collections of adaptive entities that have complex interactions. Although there are many technical challenges, the opportunities for enhanced personal services motivate the development of cognitive radio. This article, thcrcfore, outlines the key technical ideas behind cognitive radio, RKRL, and related research at the Royal Institute of Technology (KTH), Sweden. The services enhancements to hc enabled by cognitive radio are motivatcd by a sct of use cases [3] that require the radio to have an advanced degrec of "undcrstanding" of topics illustrated in Fig. 2 . Next-generation personal communications scrviccs (PCS) will know the location of handsets and wireless personal digital assistants (PDAs) to within 125 m for emergency location reporting. Location-aware research [4] is creating technologies for location-aware services, such as flcxihlc directory services [SI. Cognitive radio adds locally sensed recognition of common objects, events, and local R F contcxt. Thus, for example, a cognitive radio can infer thc radio-rclated implications of a request for a taxi to a specific address. It can then tell the network its plan to move from its present location to "Grev Turgatan 16." The network then knows that this uscr (with high probability) will move across three cell sites into a fourth within the next tcn minutcs. If this uscr is headed for a confcrence center equipped with a local cellphone jammer, it is unlikely to offer the usual load to thc network aftcr the taxi ride. Such exchanges could reduce uncertainty about the load offered to a nctwork, potentially enhancing the efficiency of the use of radio resources. Software radios as presently conceived cannot have such an intelligent conversation with a network because they have no modelbased reasoning or planning capability and no language in which to express these things. For examplc, a software radio from the United States may have the RF access, memory, and processing resources to operate in Sweden. If it lacks compatibility with release lcvcl G of the host service provider, it will not work. A software radio cannot "discuss" its internal structure with the network to discovcr that it can he reconfigured to accept a download of the required software personality. Cognitive radio, however, employs a rich set of internal models useful for a wide range of such dialogs. In addition, the space-timc models of the user, network, radio resources, and services personalizc and enhance the consumer's experience. The analysis of such usc cases yielded a large set of models, conceptual primitives, and reasoning schema necessary for cognitive radio. Which computer languages should be uscd to express thcsc things? In addition to natural language, several computer-based languages are relevant t o the expression of radio knowledge (Table 1) . T h c International Telecommunication Union (ITU), for example, adopted thc Spccification and Description Language (SDL) in its 2.100 Recommendations. SDL readily expresses radio state machines, message sequencc charts, and related data dictionaries. The European Telecommunications Standards Institute (ETSI) recently adopted SDL as the normative expression of radio protocols, so onc cxpects SDL modeling of radio to continue to expand. SDL, however, lacks primitives for general ontological knowledge needed, for example, to reason about a travel itinerary. The Unified Modcling Languagc (UML) resulted from the unification of diverse object-oriented analysis, modcling, design, and dclivcry methods. This language readily expresses software objects, including attached procedures (methods), use cases, and the packaging of software for delivery. In principle, it can he used to model common-sense knowledge, including 14 Authorized licensed use limited to: CHONGQING UNIVERSITY. Downloaded on April 8, 2009 at 21:50 from IEEE Xplore. Restrictions apply.  T h e ontology pcrformative could invoke an existing ontology or could express the local context, as in this case. It normally would bc dcfaulted unlcss it changed. The other declarations are self cvident, which is one of the strengths of KQML. Like IDL, however, KQML is an interface language. Although, for cxample, one can cxpress rules from a knowledac base usine KOML. one must I -tianslate these rules into a convenicnt internal form (c.g., LISP or PROLOG) in ordei to usc thcm. In addition, thc expression of gencral ypatial knowlcdge, such as the threedimensional structure of adjaccnt city blocks, IS bettcr expressed in structured arrays than in KQML. KQML could be used to send changes plans, space, time, relationships, people -just about anything. In practice, it has a strong prcscnce in software design and devclopment, but is weak in thc modcling of hardwarc devices. In addition, although U M L can providc ii design framework for radio propagation modeling, the target languagcs are likely to be C or FORTRAN for computational efficiency in tracing tens of thousands of rays of radio waves. The. Common Object Requcst Broker Architecturc (CORBA) defines an Intcrfacc Definition Languagc (IDL) as an implementation-independent syntax for describing object encapsulations. In addition to the 700 companies that comprise the Object Management Group (OMG), IDL is being used by the Software-Defined Radio (SDR) Forum [I21 to reprcscnt interfaces among the internal componcnts of SDRs. Since this language is specifically dcsigned to dcclare encapsulations, it lacks the computational power of gencral languages such as C or Java. IDL excels at architecturc integration (e.g., the interface to an cqualizer ASIC), but not at expressing the functions and contributions of a component (e.g., the cnhancement of bit error rate, BER, at low SNR). The hardwarc description languages (HDLs), primarily Verilog HDL and VHDL, rcadily express the internal structurc of ASICs and the personalities of FPGAs. Howcver, cognitive radio does not nccd the levcl of detail prcscnt in most H D L data sets. Moreover, it needs to know the functions and coiitributions r--to such arrays, however. Thc knowledge interchange format (KIF) provides an axiomatic framework for general knowlcdgc including sets, rclations, time-depcndent quantities, units, simplc geometry, and other domain-indcpendent conccpts. Its main contribution is strong axiomatization. It has a LISP-like structure and, like IDL and KQML, is not specifically dcsigned for internal use, like C or Java. Finally, most radio knowledge is reprcscnted in natural language. It lacks precision, but in some sense has ultimatc expressive power, particularly if one includcs graphics and multimedia in natural language. Natural language suffers from ambiguities and complexity that at present limit its use as a formal language. RKRL v. 0.1 was created to fill the voids in thc expressive power of computer languagcs while enforcing a modicum of structure on the usc of natural languagc. Chess Game RKRL is supposed to represent the domain of information scrvices that use software radios for mobile connectivity. Since a software radio has a choice of RF bands, air interfaces, data protocols, and prices to be paid, in competition with other uscrs, the so that it can make trade-offs, create plans, and reprogram itself. While the documentation package associated with H D L may providc some of this insight, the information is not in a computationally accessible form. T h e Knowlcdgc Query and Manipulation Languagc (KQML), o n the other hand, was explicitly designed to facilitate thc exchange of such knowlcdge. Based on performatives such as tell and ask, KQML readily expresses the dialog about equalizer taps and multipath by introducing a fcw new tags. The KQML plan to takc a taxi from the information kiosk to Grcv Turgatan 16 uses the tell performative to tell thc network of the plan, as shown in Fig. 3 . In this example, the radio also warns the network that its user is composing some e-mail and so will need either a Digital European Cordless Fig. 4 . T h e mcso-world o f RKKL 0.1 coiisists of the 41 microworlds sunitnarizcd in Fig. 5 . F a c h is structured according t o formal models, and descrihed in ii kiiowlcdgc hasc. Coinpctcnce comes from thc pattern matching and plan generation capahilities 0 1 a cognition cycle, mcdiatccl by the rclatctl infcrcncc cngincs. RKRL includes syntax and ontological reasoning as follows. RKRI, is a paralIcl framc language. hkh RKHL statcmcnt is a framc: The iratnc cxprcsscs ii rclationship bctwccn the handlc aiid body, in a given contcxt. The <modcl> part defines the exact rclationship heirig cxpresscd. Mandlcs should he thought of as iianics for things. If a thing contains other things, it can I)c viewed as an object. I1 not, it is a terminal constant. l'ranics arc iiiterprctcd i n p;irallcl, l i k e thc cclls in a sprcadshcct. For cxarnplc, the following RKRL statement says that South America is part of the glohal planc in the physical world inodcl of thc iiiiivcrsc of RKRL 0.1. Additional fi-amcs assert Europe a n d the rest into the global planc. Siiicc the global planc is part of the univcrsc, it can bc thought of 21s an <l>ody>, <context> I. attribute of universe, with a value that is a set of regions of the worltl. Ilowcver, it can also bc thought of a s just a list of the iianics of coontrics. 'These semantics (ohjcct, list, etc.) arc not p u t of thc scmantics of IlKRl ,. Instcad, thc s e m a n t i c s are explicitly tleclarcd and codctl in RKKL using formal computational modcls (l'able 2). For cxaniplc, the word contirins is a vcrh from natural Ianguagc, uscd in an obvious way. But contains is also a Cormal model dcl'ined in thc model's micro-world using the statcmcnts in Table 3 . Cont;iins, the modcl, is defined in terms of a <domain> (the micro-world of sets), a <range>, a <test> for mcmbcrship, and a <process> for finding inenibcrs in a local context. The l i n t framc says that Contains has a <process> callcd SctAccumulatc. The fourth line says that Contains is clcfincd over sets (which is a complete micro-world). l h c items in <brackets> arc defined i n thc mct;i micro-world. The <test> process consists of : I chunk of Excel Visual Basic for Applications (VBA) macro language that will tcst ii framc with a standard binding (modclVar is bound to the model part of thc framc). Thcsc string models tell the RKRI, interpreter (cmhryonic at prcscnt) tiow to use chunks of codc to construct programs that crc;itc and nianipiilatc ot?jccls, perform inodcl-IJWXI reasoning, and othcrwisc control tlic software radio platform. To attach an existing VUA macro to m entity, one -information. Parsing an KKRI, statciiieiit inclutlcs intcrpreting that statcmcnt in tcrins of the RKRI, radio o n t o logy aiid knowledge base. Thus, words, including KQMI, tags, have a mcaniiig that is fixcd in a given context (although a single word can liavc diffcrcnt meanings in different contcxts). Thus, thc scope of RKRL includes the formal modcls, knowledge hasc, inlercncc ciiginc, multiple syntaxes, and a radio ontology. The cxprcssion of syntax in RKRI, permits one to embcd knowlcdgc from external rcprcsentatioos (espccially SDL and UML). RKKL may now hc dcscrihcd syntactically. Instcad of attcmptiiig to replacc SDL, UML, IDI,, o r KQML, l1KRL integiatcs them through model-based rence
The integration of wireless transceivers is a major research area due to the take off of software defined radio (SDR) and cognitive radio (CR) REF solutions.
Cognitive radio: making software radios more personal
IEEE Personal Commun.
IEEE Personal Commun.
Computer Science