Teaching telecommunication standards: bridging the gap between theory and practice

©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Telecommunication standards have become a reliable mechanism to strengthen collaboration between industry and research institutions to accelerate the evolution of communications systems. Standards are needed to enable cooperation while promoting competition. Within the framework of a standard, the companies involved in the standardization process contribute and agree on appropriate technical specifications to ensure diversity and compatibility, and facilitate worldwide commercial deployment and evolution. Those parts of the system that can create competitive advantages are intentionally left open in the specifications. Such specifications are extensive, complex, and minimalistic. This makes telecommunication standards education a difficult endeavor, but it is much demanded by industry and governments to spur economic growth. This article describes a methodology for teaching wireless communications standards. We define our methodology around six learning stages that assimilate the standardization process and identify key learning objectives for each. Enabled by software-defined radio technology, we describe a practical learning environment that facilitates developing many of the needed technical and soft skills without the inherent difficulty and cost associated with radio frequency components and regulation. Using only open source software and commercial of-the-shelf computers, this environment is portable and can easily be recreated at other educational institutions and adapted to their educational needs and constraints. We discuss our and our students' experiences when employing the proposed methodology to 4G LTE standard education at Barcelona Tech.Peer ReviewedPostprint (author's final draft

Deployment and management of SDR cloud computing resources: problem definition and fundamental limits

Software-defined radio (SDR) describes radio transceivers implemented in software that executes on general-purpose hardware. SDR combined with cloud computing technology will reshape the wireless access infrastructure, enabling computing resource sharing and centralized digital-signal processing (DSP). SDR clouds have different constraints than general-purpose grids or clouds: real-time response to user session requests and real-time execution of the corresponding DSP chains. This article addresses the SDR cloud computing resource management problem. We show that the maximum traffic load that a single resource allocator (RA) can handle is limited. It is a function of the RA complexity and the call setup delay and user blocking probability constraints. We derive the RA capacity analytically and provide numerical examples. The analysis demonstrates the fundamental tradeoffs between short call setup delays (few processors) and low blocking probability (many processors). The simulation results demonstrate the feasibility of a distributed resource management and the necessity of adapting the processor assignment to RAs according to the given traffic load distribution. These results provide new insights and guidelines for designing data centers and distributed resource management methods for SDR clouds.Peer ReviewedPostprint (published version

SDR in undergraduate engineering education

Postprint (published version

Resource management for software-defined radio clouds

Software-defined radio (SDR) clouds combine SDR concepts with cloud computing technology for designing and managing future base stations. They provide a scalable solution for the evolution of wireless communications. The authors focus on the resource management implications and propose a hierarchical approach for dynamically managing the real-time computing constraints of wireless communications systems that run on the SDR cloud

Tool support for logics of programs

SIGLEAvailable from British Library Document Supply Centre-DSC:8723.247(CU-CL-TR--406) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Cognitive resource management: for all wireless access layers

Peer ReviewedPostprint (published version

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.Peer Reviewe

Processing-to-amplifier power ratio for energy efficient communications

The energy consumption of modern communications systems is dominated by the signal processing and shaping circuitry. Assuming a rate-independent processing power consumption, the energy per bit spent for signal processing can be effectively reduced by increasing the transmission power. The optimal relation between processing and transmission power for energy efficient communications is derived. This relation is called processing-to-amplifier power ratio (PrAPR).Peer Reviewe

Energy-efficient water-filling with order statistics

This paper proposes a methodology based on order statistics to study the energy-efficient (EE) power allocation for wireless communication transmitters. Water-filling power allocation maximizes the EE when transmitting and processing power consumption is considered. Solutions are typically presented in an iterative form, which complicates analysis and adaptive implementation. Time-consuming simulations are rather required to assess their performance. Order statistics allows analyzing the solution without knowledge of the channel realization. We analytically show how the EE depends on the system parameters. The computing efficiency of our proposal, which is executing one or two orders of magnitude faster than the state-of-the-art algorithms with a performance loss of less than 1 dB, facilitates its applicability in vehicular environments.Peer Reviewe

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.Peer Reviewe