Cognitive radio: making software radios more personal

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.  Making Software Radios More Personal J O S E P H MITOLA 111 A N D GERALD Q. M A G U I R E , J R . R O Y A L INSTITUTE OF TECHNOLOGY 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 lanThis raise.$ a host of questions about the control of such complex adaptive agents, network ,stabiliQ, and the like. 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 Authorized licensed use limited to: CHONGQING UNIVERSITY. Downloaded on April 8, 2009 at 21:50 from IEEE Xplore. Restrictions apply. ___ ................. ............ .. Cognition -.