| 1. |
- Bertilsson, Erik, et al.
(author)
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A Scalable Architecture for Massive MIMO Base Stations Using Distributed Processing
- 2016
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In: 2016 50TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS. - Washington : IEEE COMPUTER SOC. - 9781538639542 ; , s. 864-868
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Conference paper (peer-reviewed)abstract
- Massive MIMO-systems have received considerable attention in recent years as an enabler in future wireless communication systems. As the idea is based on having a large number of antennas at the base station it is important to have both a scalable and distributed realization of such a system to ease deployment. Most work so far have focused on the theoretical aspects although a few demonstrators have been reported. In this work, we propose a base station architecture based on connecting the processing nodes in a K-ary tree, allowing simple scalability. Furthermore, it is shown that most of the processing can be performed locally in each node. Further analysis of the node processing shows that it should be enough that each node contains one or two complex multipliers and a few complex adders/subtracters operating at some hundred MHz. It is also shown that a communication link of some Gbps is required between the nodes, and, hence, it is fully feasible to have one or a few links between the nodes to cope with the communication requirements.
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| 2. |
- Bezati, Endri, et al.
(author)
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High-level system synthesis and optimization of dataflow programs for MPSoCs
- 2017
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In: 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. - 9781538639542 ; , s. 417-421
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Conference paper (peer-reviewed)abstract
- The growing complexity of digital signal processing applications make a compelling case the use of high-level design and synthesis methodologies for the implementation on reconfigurable and embedded devices. Past research has shown that raising the level of abstraction of design stages does not necessarily gives penalties in terms of performance or resources. Dataflow programs provide behavioral descriptions capable of expressing both sequential and parallel algorithms and enable natural design abstractions, modularity, and portability. In this paper, a tool implementing dataflow programs onto embedded heterogeneous platforms by means of high-level synthesis, software synthesis and interface synthesis is presented for MPSoCs platfroms.
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| 3. |
- Björnson, Emil, Professor, 1983-, et al.
(author)
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Massive MIMO with Imperfect Channel Covariance Information
- 2016
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In: 2016 50TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS. - : IEEE COMPUTER SOC. - 9781538639542 ; , s. 974-978
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Conference paper (peer-reviewed)abstract
- This work investigates the impact of imperfect statistical information in the uplink of massive MIMO systems. In particular, we first show why covariance information is needed and then propose two schemes for covariance matrix estimation. A lower bound on the spectral efficiency (SE) of any combining scheme is derived, under imperfect covariance knowledge, and a closed-form expression is computed for maximum-ratio combining. We show that having covariance information is not critical, but that it is relatively easy to acquire it and to achieve SE close to the ideal case of having perfect statistical information.
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| 4. |
- Chan, Wai Ming, et al.
(author)
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Subspace Estimation and Hybrid Precoding for Wideband Millimeter-Wave MIMO Systems
- 2016
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In: 2016 50th Asilomar Conference on Signals, Systems and Computers. - : IEEE Computer Society. - 9781538639542 ; , s. 286-290
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Conference paper (peer-reviewed)abstract
- There has been growing interest in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, which would likely employ hybrid analog-digital precoding with large-scale analog arrays deployed at wide bandwidths. Primary challenges here are how to efficiently estimate the large-dimensional frequency-selective channels and customize the wideband hybrid analog-digital precoders and combiners. To address these challenges, we propose a low-overhead channel subspace estimation technique for the wideband hybrid analog-digital MIMO precoding systems. We first show that the Gram matrix of the frequency-selective channel can be decomposed into frequency-flat and frequency-selective components. Based on this, the Arnoldi approach, leveraging channel reciprocity and time-reversed echoing, is employed to estimate a frequency-flat approximation of the frequency-selective mmWave channels, which is used to design the analog parts. After the analog precoder and combiner design, the low-dimensional frequency-selective channels are estimated using conventional pilot-based channel sounding. Numerical results show that considerable improvement in data-rate performance is possible.
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| 5. |
- Elvander, Filip, et al.
(author)
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Time Recursive Multi-Pitch Estimation Using Group Sparse Recursive Least Squares
- 2017
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In: 50th Asilomar Conference on Signals, Systems, and Computers, 2016. - 9781538639542 ; , s. 369-373
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Conference paper (peer-reviewed)abstract
- In this work, we propose a time-recursive multi-pitch estimation algorithm, using a sparse reconstruction framework, assuming only a few pitches from a large set of candidates to be active at each time instant. The proposed algorithm utilizes a sparse recursive least squares formulation augmented by an adaptive penalty term specifically designed to enforce a pitch structure on the solution. When evaluated on a set of ten music pieces, the proposed method is shown to outperform state-of-the-art multi-pitch estimators in either accuracy or computational spe
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| 6. |
- Gharanjik, A., et al.
(author)
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Max-min transmit beamforming via iterative regularization
- 2017
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In: Conference Record - Asilomar Conference on Signals, Systems and Computers. - : IEEE Computer Society. - 9781538639542 ; , s. 1437-1441
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Conference paper (peer-reviewed)abstract
- This paper introduces an iterative optimization framework to tackle the multi-group multicast Max-Min transmit beamforming problem. In each iteration, the optimization problem is decomposed into four sub-problems, all of which can be solved using computationally efficient algorithms. The advantage of proposed method lies in its ability to handle different types of signal constraints like total power and unimodularity; a feature not exhibited by other techniques. The proposed technique outperforms the well-known semidefinite relaxation method in terms of quality of solutions.
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| 7. |
- Gianelli, Christopher, et al.
(author)
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One-Bit Compressive Sampling with Time-Varying Thresholds : Maximum Likelihood and the Cramer-Rao Bound
- 2016
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In: 2016, 50Th Asilomar Conference On Signals, Systems And Computers. - : IEEE COMPUTER SOC. - 9781538639542 ; , s. 399-403
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Conference paper (peer-reviewed)abstract
- This paper considers the problem of estimating the parameters of a noisy signal which has been quantized to one-bit via a time-varying thresholding operation. An expression for the Fisher information matrix (FIM) is derived for a generic deterministic signal parameterized by a vector beta when the noise is independent and identically distributed (i.i.d.) Gaussian with either known or unknown variance. The case of single sinusoidal parameter estimation is considered as a particular example, and the Cramer-Rao bounds (CRB) for amplitude, frequency, and phase estimators are computed for a variety of parameter values. A maximum likelihood (ML) estimator for the sinusoidal signal parameters is proposed, and its performance is compared with the CRB as a function of the number of observations.
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| 8. |
- Hu, Sha, et al.
(author)
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Massive MIMO via Cooperative Users
- 2017
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In: 50th Asilomar Conference on Signals, Systems and Computers, 2016. - 9781538639542 ; , s. 177-182
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Conference paper (peer-reviewed)abstract
- We consider a case where K closely located users communicate with a base station in the uplink of a wireless communication system. Each user has a certain number of assigned time-frequency slots at its disposal. At a small cost, which we in this paper consider as negliable, the users can communicate with each other and share their data. Thus, they can cooperate in the uplink in order to save transmit power. When many single-antenna users cooperate tightly to enhance their communication using maximum-ratio transmission, they essentially form a massive MIMO array. We investigate the case with a single receiving base station antenna, but the same general principle applies with multiple receiving antennas. We formulate a game theoretic model for this, and study what savings are possible, and to what extent users are willing to take part in the cooperation. Our findings are that large gains are possible, and that all users cooperate. Further, users are able to establish their transmit powers without any moderator.
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| 9. |
- Ingemarsson, Carl, et al.
(author)
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Hardware Architecture for Positive Definite Matrix Inversion Based on LDL Decomposition and Back-Substitution
- 2016
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In: 2016 50TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS. - : IEEE COMPUTER SOC. - 9781538639542 ; , s. 859-863
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Conference paper (peer-reviewed)abstract
- In this paper we propose an efficient hardware architecture for computation of matrix inversion of positive definite matrices. The algorithm chosen is LDL decomposition followed directly by equation system solving using back substitution. The architecture combines a high throughput with an efficient utilization of its hardware units. We also report FPGA implementation results that show that the architecture is well tailored for implementation in real-time applications.
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| 10. |
- Kronvall, Ted, et al.
(author)
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Hyperparameter-free sparse regression of grouped variables
- 2017
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In: Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. - 9781538639542 ; , s. 394-398
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Conference paper (peer-reviewed)abstract
- In this paper, we introduce a novel framework for semi-parametric estimation of an unknown number of signals, each parametrized by a group of components. Via a reformulation of the covariance fitting criteria, we formulate a convex optimization problem over a grid of candidate representations, promoting solutions with only a few active groups. Utilizing the covariance fitting allows for a hyperparameter-free estimation procedure, highly robust against coherency between candidates, while still allowing for a computationally efficient implementation. Numerical simulations illustrate how the proposed method offers a performance similar to the group-LASSO for incoherent dictionaries, and superior performance for coherent dictionaries.
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