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- Olfat, Ehsan, et al.
(author)
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A general framework for joint estimation-detection of channel, nonlinearity parameters and symbols for OFDM in IoT-based 5G networks
- 2020
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In: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 176
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Journal article (peer-reviewed)abstract
- Despite being a strong candidate also for future cellular networks, OFDM’s main drawback is the high peak-to-average power ratio. This requires transmitters to deploy high dynamic range power amplifiers which are difficult to manufacture and thereby expensive. It is particularly problematic in future IoT-based 5G networks, in which a lot of presumably low-cost low-power devices transmit data to a high-quality receiver. In order to make such transmitters as simple and cheap as possible, we consider receiver-side nonlinearity compensation and symbol detection. In particular, we study the problem of joint maximum-likelihood estimation-detection of channel and nonlinearity parameters and symbols using frequency-domain comb-type pilots in multi-path fading OFDM systems, and propose an iterative optimization algorithm to solve it. We also calculate the Cramér-Rao lower bound for a general type of memoryless nonlinearity and show that the proposed algorithm attains it, for high signal-to-noise ratios. We then show by numerical evaluations that the algorithm converges fast and the difference of its performance with the one of the genie-aided scenario, with perfect a priori knowledge of nonlinearity parameters and channel, is negligible.
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- Olfat, Ehsan, et al.
(author)
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Joint Channel and Clipping Level Estimation for OFDM in IoT-based Networks
- 2017
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In: IEEE Transactions on Signal Processing. - : IEEE. - 1053-587X .- 1941-0476. ; 65:18, s. 4902-4911
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Journal article (peer-reviewed)abstract
- We consider scenarios such as IoT-based 5G or IoTbased machine type communication, where a low-cost low-power transmitter communicates with a high-quality receiver. Then, digital predistortion of the nonlinear power amplifier may be too expensive. In order to investigate the feasibility of receiver-side compensation of the transmitter RF impairments, we study joint maximum-likelihood estimation of channel and clipping level in multipath fading OFDM systems. In particular, we propose an alternative optimization algorithm, which uses frequency-domain block-type training symbols, and prove that this algorithm always converges, at least to a local optimum point. Then, we calculate the Cramer-Rao lower bound, and show that the proposed estimator attains it for high signal-to-noise ratios. Finally, we perform numerical evaluations to illustrate the performance of the estimator, and show that iterative decoding can be done using the estimated channel and clipping level with almost the same performance as a genie-aided scenario, where the channel and clipping level are perfectly known.
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- Olfat, Ehsan, et al.
(author)
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Learning-based Pilot Precoding and Combining for Wideband Millimeter-wave Networks
- 2017
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In: 2017 IEEE 7TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP). - : IEEE.
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Conference paper (peer-reviewed)abstract
- This paper proposes an efficient channel estimation scheme with a minimum number of pilots for a frequency-selective millimeter-wave communication system. We model the dynamics of the channel's second-order statistics by a Markov process and develop a learning framework that finds the optimal precoding and combining vectors for pilot signals, given the channel dynamics. Using these vectors, the transmitter and receiver will sequentially estimate the corresponding angles of departure and arrival, and then refine the pilot precoding and combining vectors to minimize the error of estimating the small-scale fading of all subcarriers. Numerical results demonstrate near-optimality of our approach, compared to the oracle wherein the second-order statistics (not the dynamics) are perfectly known a priori.
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- Olfat, Ehsan
(author)
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Parameter Estimation of Nonlinearities in Future Wireless Systems
- 2018
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Doctoral thesis (other academic/artistic)abstract
- Nowadays, our every-day life is immersed with wireless communications.From our hand-held cell-phones to televisions to navigation systems in cars, all and all are using wireless communications. This usage will even be enormouslyexpanded due to the introduction of the era of 5G-based Internet-of-Things(IoT) which consists wearables, sensors and more smart appliances.Orthogonal frequency division multiplexing is a very well-known commu-nication method which has been utilized in modern standards and technolo-gies due to its high spectral efficiency, simple frequency-domain equalization,and robustness against inter-symbol interference. Nevertheless, the major do-wnside of OFDM systems is the large fluctuations of the amplitudes of theirsignals causing high peak-to-average-power-ratio (PAPR). This forces the po-wer amplifier (PA) in the transmitter’s RF front-end to work in its saturationregion, hence introducing nonlinear distortion to the transmitted signal. Thisis particularly challenging in low-cost and low-power (and even low-weight)devices where a high-quality PA with a large dynamic range is not affordable,using complex digital processing techniques to mitigate the PAPR or to line-arize the PA is not computationally feasible, and introducing input back-offto change the operating point of the PA is not desirable due to decreasingthe power efficiency of the PA, which can be problematic because of the shortbattery-life. On the other hand, there are more resources available for a high-quality base station (or IoT gateway) in terms of power, budget, space and computational complexity, which motivates transferring all the complexity and cost to them and implement receiver-side nonlinearity estimation and compensation algorithms.To compensate the effects of a nonlinear PA on the transmitted signal and lastly detect them correctly, an iterative detection algorithm has been proposed in the literature. However, to use this algorithm successfully, thereceiver first needs to estimate the nonlinearity parameters. The importanceof this is more noticeable in the 5G-based Internet-of-Things networks, inwhich presumedly, numerous low-cost and low-power devices aim to transmitdata to a base station (or an IoT gateway).The focus of this thesis is on estimating the nonlinearity parameters al-ong with channel estimation, nonlinearity distortion mitigation, and symboldetection in future wireless systems deploying OFDM. In particular, we firstconsider an OFDM system with a limiter (clipper) communicating over anAWGN channel, and derive a maximum-likelihood estimator of the clippingamplitude. Next, we consider OFDM systems tranceiving over multi-pathfading channels, and propose a joint channel and clipping amplitude esti-mation algorithm using block-type frequency-domain pilots. Furthermore, we propose a new packet-frame consisting time-domain and frequency-domain pilots to separately estimate channel and clipping amplitude. After, we consider a broader types of memory less nonlinear PA models, and propose a jointestimation-detection algorithm to jointly estimate the nonlinearity parame-ters and channel and detect symbols. Finally, the joint channel and clipping amplitude estimation algorithm is extended to SIMO-OFDM systems. The performance of all of these algorithms are verified by means of simulations
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