Ismael Gomez

Aloe: An open-source SDR execution environment with cognitive computing resource management capabilities

Future radio transceivers will offer more functionalities and system features for potentiating flexible and reconfigurable radio access networks. Since flexibility in this case comes at a price of computing resource overhead, we propose a conceptually simple though powerful framework for digital signal processing applications. The abstraction layer and operating environment (ALOE) is an open source execution environment for software- defined radios. It is essentially based on a hardware abstraction layer, a lightweight and time-driven software architecture, and a simple interface format. ALOE accounts for heterogeneous multiprocessor platforms. Its cognitive computing resource management capabilities enable flexible and dynamic management of SDR platforms and applications for distributed realtime execution of applications and dynamic reconfiguration of platforms.

Cognitive Resource Management: For All Wireless Access Layers

The radio communications spectrum has been commonly considered a scarce resource. Measurements, however, revealed that spectral resources are actually under used most of the time. Therefore, advanced radio resource management (RRM) techniques try to balance the spectrum occupation. Increasing the system capacity further requires the coordination of several factors in the wireless business and the convergence of standards and technologies.

Performance and overhead analysis of the ALOE middleware for SDR

Current Software-Defined Radio applications (waveforms) are tailored to specific hardware. Processor vendors frequently adapt internal OS mechanisms for its specific architecture (e.g. scheduling and synchronization). The Abstraction Layer and Operating Environment (ALOE) is an open source SDR operating environment that isolates platform architecture from the application design. An integrated resource manager is capable of automatically mapping waveform components to a network of heterogeneous processors while meeting the waveforms real-time requirements. This paper analyzes the ALOE performance for x86 and ARM processors. It presents computing histograms of UTRAN transceiver components, the maximum achievable throughput of a simple BPSK modem, interface latencies, and overhead measurements of the ALOE background processes.

ALOE-Based Flexible LDPC Decoder

Radio communications terminals and infrastructure tend to support an increasing range of algorithms and radio access technologies. Flexible processing platforms are therefore needed for supporting multi-standard or heterogeneous radios. Channel decoding is one of the most computing demanding digital signal processing blocks of a radio transceiver. At the same time, it provides a high degree of implementation flexibility as well as facilitates dynamic parameter adjustments. This paper presents a flexible LDPC decoder implemented on an FPGA device following the ALOE middleware design paradigm. We analyse the middleware efficiency in terms of flexibility versus resource requirements. The results show a relative middleware area overhead of 32 %.

Automatic computing resource awareness in resource managers for cognitive radios

With all the potential flexibility of software-defined radios, the flexibility of SDR terminals is currently limited to design time flexibility. The capacity of the platform in terms of processing resources and internal bandwidths is dimensioned for the range of supported functionalities. In a platform-independent design scenario, resource managers play the role of matching waveform demands with platform capabilities. Predicting the task execution times has been studied in grid and distributed computing contexts with different objectives and assumptions. Given the dynamic nature of waveform demands as a function of the radio environment, an accurate characterization of the consumed resources can increase the efficiency of resource management strategies. In the SDR context, this efficiency translates to less energy consumption and higher resource utilization. Based on our experience acquired during the development of an SDR execution environment, this work presents the metrics that are needed by computing resource managers.

Computing and Radio Resource Management Interactions in Flexible Radio Environments

Due to the wireless industry tendencies, different radio access technologies will coexist in a heterogeneous environment. Software defined radio, as a concept that tries to provide flexibility, might ease the integration of that environment. The goal of this work is to introduce the concept of computing resource management, that working in cooperation with the radio resource management strategies leading to minimize computing costs assuring the same or better QoS in such future heterogeneous systems. Furthermore, the action of computing management might help to bring balance over the communication load.