Low power implementation of digital predistortion filter on a heterogeneous application specific multiprocessor

Ghazi, Amanullah; Boutellier, Jani; Abdelaziz, Mahmoud; Lu, Xiaojia; Anttila, Lauri; Cavallaro, Joseph R.; Bhattacharyya, Shuvra S.; Valkama, Mikko; Juntti, Markku

doi:10.1109/icassp.2014.6855227

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…With the exception of those, the proposed classification algorithm has been tested on all available actors, 381 in total. In particular, the set of applications contains various video decoders (H.265 part2, H.264 PHiP, H.264 CBP and MPEG-4 SP, AVS), the JPEG and JPEG2000 image compression codecs, for telecommunications the ZigBee transmitter baseband description and a digital predistortion filter [11], a set of basic digital filters, a cryptographic library, a WAV audio player, a GZIP decompressor and implementations of several CHSTONE benchmark suite applications.…”

Section: Methodsmentioning

confidence: 99%

Executing dataflow actors as kahn processes

Tretter¹,

Boutellier²,

Guthrie³

et al. 2015

2015 International Conference on Embedded Software (EMSOFT)

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Programming models which specify an application as a network of independent computational elements have emerged as a promising paradigm for programming streaming applications. The antagonism between expressivity and analysability has led to a number of different such programming models, which provide different degrees of freedom to the programmer. One example are Kahn process networks (KPNs), which, due to certain restrictions in communication, can guarantee determinacy (their results are independent of timing by construction). On the other hand, certain dataflow models, such as the CAL Actor Language, allow non-determinacy and thus higher expressivity, however at the price of static analysability and thus a potentially less efficient implementation. In many cases, however, non-determinacy is not required (or even not desired), and relying on KPN for the implementation seems advantageous.In this paper, we propose an algorithm for classifying dataflow actors (i.e. computational elements) as KPN compatible or potentially not. For KPN compatible dataflow actors, we propose an automatic KPN translation method based on this algorithm. In experiments, we show that more than 75% of all mature actors of a standard multimedia benchmark suite can be classified as KPN compatible and that their execution time can be reduced by up to 1.97x using our proposed translation technique. Finally, in a manual classification effort, we validate these results and list different classes of KPN incompatibility.

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Section: Methodsmentioning

confidence: 99%

Executing dataflow actors as kahn processes

Tretter¹,

Boutellier²,

Guthrie³

et al. 2015

2015 International Conference on Embedded Software (EMSOFT)

Self Cite

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“…Based on the analysis in [3], where a similar dataflow model is constructed for the DPD algorithm in [10], most of the computation and energy consumption is concentrated in the filter actors. Thus, in this paper, we map only the filter actors to hardware, and focus our design optimization processes on the filter actors.…”

Section: Adaptive Dataflow-based Dpd Architecturementioning

confidence: 99%

“…In the frequency domain of the transmitted signal, the effects of these impairments are translated as power leakage into adjacent channels. Digital predistortion (DPD) is a widely investigated technique (e.g., see [1][2][3][4][5]) to counteract such impairments by applying carefully-calculated distortion to the signal prior to transmission.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Multiobjective Optimization for Adaptive Dataflow-based Digital Predistortion Architectures

Li¹,

Ghazi²,

Boutellier³

et al. 2017

EAI Endorsed Transactions on Cognitive Communications

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In wireless communication systems, high-power transmitters suffer from nonlinearities due to power amplifier (PA) characteristics, I/Q imbalance, and local oscillator (LO) leakage. Digital Predistortion (DPD) is an effective technique to counteract these impairments. To help maximize agility in cognitive radio systems, it is important to investigate dynamically reconfigurable DPD systems that are adaptive to changes in the employed modulation schemes and operational constraints. To help maximize effectiveness, such reconfiguration should be performed based on multidimensional operational criteria. With this motivation, we develop in this paper a novel evolutionary algorithm framework for multiobjective optimization of DPD systems. We demonstrate our framework by applying it to develop an adaptive DPD architecture, called the adaptive, dataflow-based DPD architecture (ADDA), where Pareto-optimized DPD parameters are derived subject to multidimensional constraints to support efficient predistortion across timevarying operational requirements and modulation schemes. Through extensive simulation results, we demonstrate the effectiveness of our proposed multiobjective optimization framework in deriving efficient DPD configurations for runtime adaptation.

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“…There are some previous work of DPD implementations on FPGA [17], and transport trigger architecture (TTA) [18], which is an application-specific architecture like ASIC. Although those implementations can achieve good data rate performance and energy efficiency, they lack the design flexibility to easily reconfigure adaptive DPD parameters.…”

Section: Introductionmentioning

confidence: 99%

Parallel Digital Predistortion Design on Mobile GPU and Embedded Multicore CPU for Mobile Transmitters

Ghazi

Tarver

et al. 2017

J Sign Process Syst

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Digital predistortion (DPD) is a widely adopted baseband processing technique in current radio transmitters. While DPD can effectively suppress unwanted spurious spectrum emissions stemming from imperfections of analog RF and baseband electronics, it also introduces extra processing complexity and poses challenges on efficient and flexible implementations, especially for mobile cellular transmitters, considering their limited computing power compared to basestations. In this paper, we present high data rate implementations of broadband DPD on modern embedded processors, such as mobile GPU and multicore CPU, by taking advantage of emerging parallel computing techniques for exploiting their computing resources. We further verify the suppression effect of DPD experimentally on real radio hardware platforms. Performance evaluation results of our DPD design demonstrate the high efficacy of modern general purpose mobile processors on accelerating DPD processing for a mobile transmitter.

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Low power implementation of digital predistortion filter on a heterogeneous application specific multiprocessor

Cited by 8 publications

References 14 publications

Executing dataflow actors as kahn processes

Executing dataflow actors as kahn processes

Evolutionary Multiobjective Optimization for Adaptive Dataflow-based Digital Predistortion Architectures

Parallel Digital Predistortion Design on Mobile GPU and Embedded Multicore CPU for Mobile Transmitters

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