| 1. |
- Bengtsson, Fredrik, 1989, et al.
(författare)
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LQG Control for Systems with Random Unbounded Communication Delay
- 2016
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Ingår i: Proceedings of the 55th IEEE Conference on Decision and Control (CDC 2016); Las Vegas; United States; 12-14 December 2016. - 0743-1546. - 9781509018376 ; , s. Art no 7798406, Pages 1048-1055
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Konferensbidrag (refereegranskat)abstract
- In this paper LQG control over unreliable communication links is examined. That is to say, the communication channels between the controller and the actuators and between the sensors and the controller are unreliable. This is of growing importance as networked control systems and use of wireless communication in control are becoming increasingly common. A proposed approach is to use tree codes to turn lossy channels into ones with a random delay. The problem of how to optimize LQG control in this case is examined, and it is found that to optimize LQG control previous control signals must also be used. Only the situation where communication between the components is done with acknowledgments is examined. An optimal solution is derived for finite horizon discrete hold-input LQG control for this case. The solution is compared with standard LQG control in simulations, which demonstrate that a significant improvement in the cost can be achieved when the probability of delay is high.
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| 2. |
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| 3. |
- Bosch, David, 1997, et al.
(författare)
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Precise Asymptotic Analysis of Deep Random Feature Models
- 2023
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Ingår i: Proceedings of Machine Learning Research. - 2640-3498. ; 195, s. 4132-4179
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Konferensbidrag (refereegranskat)abstract
- We provide exact asymptotic expressions for the performance of regression by an L−layer deep random feature (RF) model, where the input is mapped through multiple random embedding and non-linear activation functions. For this purpose, we establish two key steps: First, we prove a novel universality result for RF models and deterministic data, by which we demonstrate that a deep random feature model is equivalent to a deep linear Gaussian model that matches it in the first and second moments, at each layer. Second, we make use of the convex Gaussian Min-Max theorem multiple times to obtain the exact behavior of deep RF models. We further characterize the variation of the eigendistribution in different layers of the equivalent Gaussian model, demonstrating that depth has a tangible effect on model performance despite the fact that only the last layer of the model is being trained.
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| 4. |
- Khina, Anatoly, et al.
(författare)
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Control Over Gaussian Channels With and Without Source-Channel Separation
- 2019
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Ingår i: IEEE Transactions on Automatic Control. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0018-9286 .- 1558-2523. ; 64:9, s. 3690-3705
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Tidskriftsartikel (refereegranskat)abstract
- We consider the problem of controlling an unstable linear plant with Gaussian disturbances over an additive white Gaussian noise channel with an average transmit power constraint, where the signaling rate of communication may be different from the sampling rate of the underlying plant. Such a situation is quite common since sampling is done at a rate that captures the dynamics of the plant and that is often lower than the signaling rate of the communication channel. This rate mismatch offers the opportunity of improving the system performance by using coding over multiple channel uses to convey a single control action. In a traditional, separation-based approach to source and channel coding, the analog message is first quantized down to a few bits and then mapped to a channel codeword whose length is commensurate with the number of channel uses per sampled message. Applying the separation-based approach to control meets its challenges: first, the quantizer needs to be capable of zooming in and out to be able to track unbounded system disturbances, and second, the channel code must be capable of improving its estimates of the past transmissions exponentially with time, a characteristic known as anytime reliability. We implement a separated scheme by leveraging recently developed techniques for control over quantized-feedback channels and for efficient decoding of anytime-reliable codes. We further propose an alternative, namely, to perform analog joint source-channel coding, by this avoiding the digital domain altogether. For the case where the communication signaling rate is twice the sampling rate, we employ analog linear repetition as well as Shannon-Kotel'nikov maps to show a significant improvement in stability margins and linear-quadratic costs over separation-based schemes. We conclude that such analog coding performs better than separation, and can stabilize all moments as well as guarantee almost-sure stability.
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| 5. |
- Khina, Anatoly, et al.
(författare)
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Multi-Rate Control over AWGN Channels via Analog Joint Source Channel Coding
- 2016
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Ingår i: 2016 IEEE 55th Conference on Decision and Control, CDC 2016. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509018376 ; , s. 5968-5973
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Konferensbidrag (refereegranskat)abstract
- We consider the problem of controlling an unstable plant over an additive white Gaussian noise (AWGN) channel with a transmit power constraint, where the signaling rate of communication is larger than the sampling rate (for generating observations and applying control inputs) of the underlying plant. Such a situation is quite common since sampling is done at a rate that captures the dynamics of the plant and which is often much lower than the rate that can be communicated. This setting offers the opportunity of improving the system performance by employing multiple channel uses to convey a single message (output plant observation or control input). Common ways of doing so are through either repeating the message, or by quantizing it to a number of bits and then transmitting a channel coded version of the bits whose length is commensurate with the number of channel uses per sampled message. We argue that such "separated source and channel coding" can be suboptimal and propose to perform joint source channel coding. Since the block length is short we obviate the need to go to the digital domain altogether and instead consider analog joint source channel coding. For the case where the communication signaling rate is twice the sampling rate, we employ the Archimedean bi-spiral-based Shannon Kotel'nikov analog maps to show significant improvement in stability margins and linear-quadratic Gaussian (LQG) costs over simple schemes that employ repetition.
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| 6. |
- Panahi, Ashkan, 1986, et al.
(författare)
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A Universal Analysis of Large-Scale Regularized Least Squares Solutions
- 2017
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Ingår i: Advances in Neural Information Processing Systems. - 1049-5258. ; , s. 3382-3391
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Konferensbidrag (refereegranskat)abstract
- A problem that has been of recent interest in statistical inference, machine learning and signal processing is that of understanding the asymptotic behavior of regularized least squares solutions under random measurement matrices (or dictionaries). The Least Absolute Shrinkage and Selection Operator (LASSO or least-squares with \ell_1 regularization) is perhaps one of the most interesting examples. Precise expressions for the asymptotic performance of LASSO have been obtained for a number of different cases, in particular when the elements of the dictionary matrix are sampled independently from a Gaussian distribution. It has also been empirically observed that the resulting expressions remain valid when the entries of the dictionary matrix are independently sampled from certain non-Gaussian distributions. In this paper, we confirm these observations theoretically when the distribution is sub-Gaussian. We further generalize the previous expressions for a broader family of regularization functions and under milder conditions on the underlying random, possibly non-Gaussian, dictionary matrix. In particular, we establish the universality of the asymptotic statistics (e.g., the average quadratic risk) of LASSO with non-Gaussian dictionaries.
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| 7. |
- Rezvani, Behrooz, et al.
(författare)
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Letting robocars see around corners: using several bands of radar at once can give cars a kind of second sight
- 2022
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Ingår i: IEEE Spectrum. - 0018-9235. ; 59:2, s. 36-41
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- An autonomous car needs to do many things to make the grade, but without a doubt, sensing and understanding its environment are the most critical. A self-driving vehicle must track and identify many objects and targets, whether they're in clear view or hidden, whether the weather is fair or foul. Today’s radar alone is nowhere near good enough to handle the entire job—cameras and lidars are also needed. But if we could make the most of radar’s particular strengths, we might dispense with at least some of those supplementary sensors
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| 8. |
- Soltanalian, Mojtaba, et al.
(författare)
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Training Signal Design for Correlated Massive MIMO Channel Estimation
- 2017
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Ingår i: IEEE Transactions on Wireless Communications. - : IEEE Press. - 1536-1276 .- 1558-2248. ; 16:2, s. 1135-1143
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we propose a new approach to the design of training sequences that can be used for an accurate estimation of multi-input multi-output channels. The proposed method is particularly instrumental in training sequence designs that deal with three key challenges: 1) arbitrary channel and noise statistics that do not follow specific models, 2) limitations on the properties of the transmit signals, including total power, per-antenna power, having a constant-modulus, discrete-phase, or low peak-to-average-power ratio, and 3) signal design for large-scale or massive antenna arrays. Several numerical examples are provided to examine the proposed method.
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| 9. |
- Vikalo, Haris, et al.
(författare)
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Efficient joint maximum-likelihood channel estimation and signal detection
- 2006
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Ingår i: IEEE Transactions on Wireless Communications. - 1536-1276 .- 1558-2248. ; 5:7, s. 1838-1845
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Tidskriftsartikel (refereegranskat)abstract
- In wireless communication systems, channel state information is often assumed to be available at the receiver. Traditionally, a training sequence is used to obtain the estimate of the channel. Alternatively, the channel can be identified using known properties of the transmitted signal. However, the computational effort required to find the joint ML solution to the symbol detection and channel estimation problem increases exponentially with the dimension of the problem. To significantly reduce this computational effort, we formulate the joint ML estimation and detection as an integer least-squares problem, and show that for a wide range of signal-to-noise ratios (SNR) and problem dimensions it can be solved via sphere decoding with expected complexity comparable to the complexity of heuristic techniques.
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