Browsing by Author "Valkama, Mikko"

Now showing 1 - 4 of 4
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    Dataflow Modeling and Design for Cognitive Radio Networks
    (8th International Conference on Cognitive Radio Oriented Wireless Networks, 2013-10-01) Wang, Lai-Huei; Bhattacharyya, Shuvra S.; Vosoughi, Aida; Cavallaro, Joseph R.; Juntti, Markku; Boutellier, Jani; Silven, Olli; Valkama, Mikko; CMC
    Cognitive radio networks present challenges at many levels of design including configuration, control, and crosslayer optimization. In this paper, we focus primarily on dataflow representations to enable flexibility and reconfigurability in many of the baseband algorithms. Dataflow modeling will be important to provide a layer of abstraction and will be applied to generate flexible baseband representations for cognitive radio testbeds, including the Rice WARP platform. As RF frequency agility and reconfiguration for carrier aggregation are important goals for 4G LTE Advanced systems, we also focus on dataflow analysis for digital pre-distortion algorithms. A new design method called parameterized multidimensional design hierarchy mapping(PMDHM) is presented, along with initial speedup results from applying PMDHM in the mapping of channel estimation onto a GPU architecture.
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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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    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.
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    Toward Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification
    (IEEE, 2023) Shen, Guanxiong; Zhang, Junqing; Marshall, Alan; Valkama, Mikko; Cavallaro, Joseph R.
    Radio frequency fingerprint identification (RFFI) can classify wireless devices by analyzing the signal distortions caused by intrinsic hardware impairments. Recently, state-of-the-art neural networks have been adopted for RFFI. However, many neural networks, e.g., multilayer perceptron (MLP) and convolutional neural network (CNN), require fixed-size input data. In addition, many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the RFFI performance in such scenarios is often unsatisfactory. In this paper, we analyze the reason why MLP- and CNN-based RFFI systems are constrained by the input size. To overcome this, we propose four neural networks that can process signals of variable lengths, namely flatten-free CNN, long short-term memory (LSTM) network, gated recurrent unit (GRU) network, and transformer. We adopt data augmentation during training which can significantly improve the model’s robustness to noise. We compare two augmentation schemes, namely offline and online augmentation. The results show the online one performs better. During the inference, a multi-packet inference approach is further leveraged to improve the classification accuracy in low SNR scenarios. We take LoRa as a case study and evaluate the system by classifying 10 commercial-off-the-shelf LoRa devices in various SNR conditions. The online augmentation can boost the low-SNR classification accuracy by up to 50% and the multi-packet inference approach can further increase the accuracy by over 20%.