Abstract-Wireless communication standards are developed at an ever-increasing rate of pace, and significant amounts of effort is put into research for new communication methods and concepts. On the physical layer, such topics include MIMO, cooperative communication, and error control coding, whereas research on the medium access layer includes link control, network topology, and cognitive radio. At the same time, implementations are moving from traditional fixed hardware architectures towards software, allowing more efficient development. Today, field-programmable gate arrays (FPGAs) and regular desktop computers are fast enough to handle complete baseband processing chains, and there are several platforms, both opensource and commercial, providing such solutions. The aims of this paper is to give an overview of five of the available platforms and their characteristics, and compare the features and performance measures of the different systems.
Novel cognitive radio platforms such as IMECs COgnitive Baseband RAdio (COBRA) should ensure the feasibility of multiple streams and their reconfigurability and scalability during run-time. The control over those tasks should be dedicated to a run-time controller that (re)allocates the resources on the platform. E.g., when user starts a new stream or the channel conditions change requiring switch to different modulation and coding scheme. The current transaction level models are too detailed for rapid exploration of all run-time options and the high-level data-flow frameworks (such as Kahn process networks) lack the dynamism and reconfigurability that is essential for the exploration. In this paper we propose the DAtaflow for Run-Time (DART), the high-level dynamic data-flow platform model framework, suited for rapid run-time control development. We sketch also how to use this framework to develop such a controller in the reactive and more challenging, proactive way.
Novel cognitive radio platforms such as IMECs COgnitive Baseband RAdio (COBRA) should ensure the feasibility of multiple streams and their reconfigurability and scalability during run-time. The control over those tasks should be dedicated to a run-time controller that (re)allocates the resources on the platform. E.g., when user starts a new stream or the channel conditions change requiring switch to different modulation and coding scheme. The current transaction level models are too detailed for rapid exploration of all run-time options and the high-level data-flow frameworks (such as Kahn process networks) lack the dynamism and reconfigurability that is essential for the exploration. In this paper we propose the DAtaflow for Run-Time (DART), the high-level dynamic data-flow platform model framework, suited for rapid run-time control development. We sketch also how to use this framework to develop such a controller in the reactive and more challenging, proactive way.
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