Browsing by Author "Tarver, Chance"

Now showing 1 - 4 of 4
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    Enabling a “Use-or-Share” Framework for PAL–GAA Sharing in CBRS Networks via Reinforcement Learning
    (IEEE, 2019) Tarver, Chance; Tonnemacher, Matthew; Chandrasekhar, Vikram; Chen, Hao; Ng, Boon Loong; Zhang, Jianzhong; Cavallaro, Joseph R.; Camp, Joseph
    By implementing reinforcement learning-aided listen-before-talk (LBT) schemes over a citizens broadband radio service (CBRS) network, we increase the spatial reuse at secondary nodes while minimizing the interference footprint on higher-tier nodes. The federal communications commission encourages “use-or-share” policies in the CBRS band across the priority access license (PAL)-general authorized access (GAA) priority tiers by opportunistically allowing the lower-priority GAA nodes to access unused higher-priority PAL spectrum. However, there is currently no mechanism to enable this cross-tier spectrum sharing. In this paper, we propose and evaluate LBT schemes that allow opportunistic access to PAL spectrum. We find that by allowing LBT in a two carrier, two eNB scenario, we see upward of 50% user perceived throughput (UPT) gains for both eNBs. Furthermore, we examine the use of ${Q}$ -learning to adapt the energy-detection threshold (EDT), combating problematic topologies, such as hidden and exposed nodes. With merely a 4% reduction in primary node UPT, we see up to 350% gains in average secondary node UPT when adapting the EDT of opportunistically transmitting nodes.
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    Low-Complexity Subband Digital Predistortion for Spurious Emission Suppression in Noncontiguous Spectrum Access
    (IEEE, 2016) Abdelaziz, Mahmoud; Anttila, Lauri; Tarver, Chance; Li, Kaipeng; Cavallaro, Joseph R.; Valkama, Mikko
    Noncontiguous transmission schemes combined with high power-efficiency requirements pose big challenges for radio transmitter and power amplifier (PA) design and implementation. Due to the nonlinear nature of the PA, severe unwanted emissions can occur, which can potentially interfere with neighboring channel signals or even desensitize the own receiver in frequency division duplexing transceivers. In this paper, to suppress such unwanted emissions, a low-complexity subband digital predistortion solution, specifically tailored for spectrally noncontiguous transmission schemes in low-cost devices, is proposed. The proposed technique aims at mitigating only the selected spurious intermodulation distortion components at the PA output, hence allowing for substantially reduced processing complexity compared with classical linearization solutions. Furthermore, novel decorrelation-based parameter learning solutions are also proposed and formulated, which offer reduced computing complexity in parameter estimation as well as the ability to track time-varying features adaptively. Comprehensive simulation and RF measurement results are provided, using a commercial LTE-Advanced mobile PA, to evaluate and validate the effectiveness of the proposed solution in real-world scenarios. The obtained results demonstrate that highly efficient spurious component suppression can be obtained using the proposed solutions.
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    Low-complexity, Multi Sub-band Digital Predistortion: Novel Algorithms and SDR Verification
    (Springer, 2017) Tarver, Chance; Abdelaziz, Mahmoud; Anttila, Lauri; Cavallaro, Joseph R.
    The nonlinearities of power amplifiers combined with non-contiguous transmissions found in modern, frequency-agile, wireless standards create undesirable spurious emissions through the nearby spectrum of data carriers. Digital predistortion (DPD) is an effective way of combating spurious emission violations without the need for a significant power reduction in the transmitter leading to better power efficiency and network coverage. In this paper, an iterative, multi sub-band version of the sub-band DPD, proposed earlier by the authors, is presented. The DPD learning is iterated over intermodulation distortion (IMD) sub-bands until a satisfactory performance is achieved for each of them. A sequential DPD learning procedure is also presented to reduce the hardware complexity when higher order nonlinearities are incorporated in the DPD learning. Improvements in the convergence speed of the adaptive DPD learning are also achieved via incorporating a variable learning rate and interpolation of previously trained DPD coefficients. A WarpLab implementation of the proposed DPD is also shown with excellent suppression of the targeted spurious emissions.
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    Parallel Digital Predistortion Design on Mobile GPU and Embedded Multicore CPU for Mobile Transmitters
    (Springer, 2017) Li, Kaipeng; Ghazi, Amanullah; Tarver, Chance; Juntti, Markku; Boutellier, Jani; Abdelaziz, Mahmoud; Anttila, Lauri; Juntti, Markku; Valkama, Mikko; Cavallaro, Joseph R.
    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.