| 1. |
- Andersson, Jens A, et al.
(författare)
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Hybrid Data Communication Lab – a Case Study
- 2023
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Ingår i: Proceedings från LTHs 12:e Pedagogiska inspirationskonferensen. ; , s. 80-82
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Konferensbidrag (refereegranskat)abstract
- Due to restrictions following the Covid-19 pandemic,major changes regarding teaching have been called for. Sincestudents’ presence in lab classrooms has been very restricted alsophysical labs have had to be transformed into e.g. simulationsperformed at home or via remote connections. After lifting of therestrictions, hybrid learning where both physical and simulatedlab environment are combined have been introduced. This reportdescribes such a case regarding a lab in data communication forelectrical engineering students on bachelor level. The result showsadded value to the learning outcome of the lab.
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| 2. |
- Sarajlic, Muris, et al.
(författare)
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Power Scaling Laws for Radio Receiver Front Ends
- 2021
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Ingår i: IEEE Transactions on Circuits and Systems I: Regular Papers. - 1549-8328. ; 68:5, s. 2183-2195
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we combine practically verified results from circuit theory with communication-theoretic laws. As a result, we obtain closed-form theoretical expressions linking fundamental system design and environment parameters with the power consumption of analog front ends (AFEs) for communication receivers. This collection of scaling laws and bounds is meant to serve as a theoretical reference for practical low power AFE design. We show how AFE power consumption scales with bandwidth, SNDR, and SIR. We build our analysis based on two well established power consumption studies and show that although they have different design approaches, they lead to the same scaling laws. The obtained scaling laws are subsequently used to derive relations between AFE power consumption and several other important communication system parameters, namely, digital modulation constellation size, symbol error probability, error control coding gain, and coding rate. Such relations, in turn, can be used when deciding which system design strategies to adopt for low-power applications. For instance, we show how AFE power scales with environment parameters if the performance is kept constant and we use these results to illustrate that adapting to fading fluctuations can theoretically reduce AFE power consumption by at least 20x.
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| 3. |
- Sheikhi, Ashkan, et al.
(författare)
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Machine Learning Based Digital Pre-Distortion in Massive MIMO Systems : Complexity-Performance Trade-offs
- 2023
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Ingår i: 2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Proceedings. - 1525-3511. - 9781665491228 ; 2023-March
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Konferensbidrag (refereegranskat)abstract
- In this paper, we study the trade-off between complexity and performance in massive MIMO systems with neural-network based digital pre-distortion (NN-DPD) blocks at the base station. In particular, we consider a multi-user massive MIMO system with per-antenna NN-DPDs, each with an adjustable NN architecture in terms of the size and the number of NN hidden layers. We first analyze the system performance in terms of compensation of the non-linear hardware distortion for different levels of NN-DPD complexity and the number of antennas. We illustrate the required level of complexity in the trained NN-DPD blocks to approach the performance of an ideal conventional DPD. The statistics of the signal to interference and noise plus distortion ratio for a randomly located UE are selected as the performance metrics. We then assume a limited total digital computation power to be allocated among the NN-DPD blocks and propose to select the NN-DPD architecture of each TX branch based on the channel conditions of its corresponding antenna. To illustrate the importance of such a smart DPD resource allocation, we have analyzed the performance of a massive MIMO system with different NN-DPD architecture selection strategies. Numerical results indicate that by adopting the smart NN-DPD resource allocation, a significant boost in the system performance can be achieved, making room for reducing the overall system cost when scaling a massive MIMO system.
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| 4. |
- Sheikhi, Ashkan, et al.
(författare)
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Massive MIMO with Per-Antenna Digital Predistortion Size Optimization : Does it Help?
- 2021
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Ingår i: ICC 2021 - IEEE International Conference on Communications, Proceedings. - 1550-3607. - 9781728171227
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Konferensbidrag (refereegranskat)abstract
- In this paper, we study the effect of optimizing the per-antenna digital predistortion (DPD) sizes on the performance of the downlink massive MIMO system with residual hardware impairment. In particular, we first quantify the capacity after applying some per-antenna DPD with different numbers of coefficients at each antenna and then maximize the capacity by finding optimum values of the per-antenna DPD sizes under a constraint on the total number of DPD coefficients in the system. Two closed-form sub-optimal solutions are derived and numerical examples illustrate that their performance is very close to the optimal solution. It is shown that when the channel large-scale gains have high variation over the transmitter array, which is the case in practical scenarios, using our proposed optimized DPD sizes can improve the system capacity significantly. This allows us to scale down the massive MIMO system but still maintain performance. We also study the asymptotic behavior of the derived capacity and show that by using the optimized DPD sizes, the system performance can approach the asymptotic bound with a significantly smaller number of antennas.
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