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The information content of symbolic sequences (such as nucleic- or amino acid sequences, but also neuronal firings or strings of letters) can be calculated from an ensemble of such sequences, but because information cannot be assigned to single seque nces, we cannot correlate information to other observables attached to the sequence. Here we show that an information score obtained from multivariate (multiple-variable) correlations within sequences of a training ensemble can be used to predict observables of out-of-sample sequences with an accuracy that scales with the complexity of correlations, showing that functional information emerges from a hierarchy of multi-variable correlations.
193 - Xiuhong Wei , Linglong Dai 2021
Extremely large-scale massive MIMO (XL-MIMO) is a promising technique for future 6G communications. The sharp increase of BS antennas leads to the unaffordable channel estimation overhead. Existing low-overhead channel estimation schemes are based on the far-field or near-field channel model. However, the far-field or near-field channel model mismatches the practical XL-MIMO channel feature, where some scatters are in the far-field region while others may locate in the near-field region, i.e., hybrid-field channel. Thus, existing far-field and near-field channel estimation schemes cannot be directly used to accurately estimate the hybrid-field XL-MIMO channel. To solve this problem, we propose an efficient hybrid-field channel estimation scheme by accurately modeling the XL-MIMO channel. Specifically, we firstly reveal the hybrid-field channel feature of the XL-MIMO channel. Then, we propose a hybrid-field channel model to capture this feature, which contains both the far-field and near-field path components. Finally, we propose a hybrid-field channel estimation scheme, where the far-field and near-field path components are respectively estimated. Simulation results show the proposed scheme performs better than existing schemes.
Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, wh en only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate.
138 - Yi Shen , Chenyun Yu , Yuan Shen 2021
We consider sparse representations of signals from redundant dictionaries which are unions of several orthonormal bases. The spark introduced by Donoho and Elad plays an important role in sparse representations. However, numerical computations of spa rks are generally combinatorial. For unions of several orthonormal bases, two lower bounds on the spark via the mutual coherence were established in previous work. We constructively prove that both of them are tight. Our main results give positive answers to Gribonval and Nielsens open problem on sparse representations in unions of orthonormal bases. Constructive proofs rely on a family of mutually unbiased bases which first appears in quantum information theory.
A ground-to-air free-space optical link is studied for a hovering unmanned aerial vehicle (UAV) having multiple rotors. For this UAV, a four-quadrant array of photodetectors is used at the optical receiver to alleviate the adverse effect of hovering fluctuations by enlarging the receiver field-of-view. Extensive mathematical analysis is conducted to evaluate the beam tracking performance under the random effects of hovering fluctuations. The accuracy of the derived analytical expressions is corroborated by performing Monte-Carlo simulations. It is shown that the performance of such links depends heavily on the random fluctuations of hovering UAV, and, for each level of instability there is an optimal size for the array that minimizes the tracking error probability
Future wireless access networks will support simultaneously a large number of devices with heterogeneous service requirements. These include data rates, error rates, and latencies. While there exist achievable rate and capacity results for Gaussian b roadcast channels in the asymptotic regime, the characterization of second-order achievable rate regions for different blocklength constraints are not available. Therefore, we investigate a two-user Gaussian broadcast channel (GBC) with heterogeneous blocklength constraints under a maximal input power constraint and an average error probability constraint. Unlike the traditional GBC where two users have the same blocklength constraints, here the user with higher output SNR has a shorter blocklength constraint. We show that with sufficiently large output SNR, the stronger user can invoke the technique named early decoding (ED) to decode the interference. Then the successive interference cancellation (SIC) can proceed. This leads to an improved achievable rate region compared to the state of the art. To achieve it, we derive an explicit lower bound on the necessary number of received symbols for a successful ED, using an independent and identically distributed Gaussian input. A second-order rate of the weaker user who suffers from an SNR change due to the heterogeneous blocklength constraint, is also derived. We then formulate the rate region of the considered setting with individual and also sum power constraints and compare to that of the hybrid non-orthogonal multiple access (HNOMA) scheme. Numerical results show that ED has a larger rate region than HNOMA partly when the gain of the better channel is sufficiently larger than the weaker one. Under the considered setting, about 7-dB SNR gain can be achieved. This makes ED with SIC a promising technique for future wireless network.
Reconfigurable intelligent surfaces (RISs) have promising coverage and data rate gains for wireless communication systems in 5G and beyond. Prior work has mainly focused on analyzing the performance of these surfaces using computer simulations or lab -level prototypes. To draw accurate insights about the actual performance of these systems, this paper develops an RIS proof-of-concept prototype and extensively evaluates its potential gains in the field and under realistic wireless communication settings. In particular, a 160-element reconfigurable surface, operating at a 5.8GHz band, is first designed, fabricated, and accurately measured in the anechoic chamber. This surface is then integrated into a wireless communication system and the beamforming gains, path-loss, and coverage improvements are evaluated in realistic outdoor communication scenarios. When both the transmitter and receiver employ directional antennas and with 5m and 10m distances between the transmitter-RIS and RIS-receiver, the developed RIS achieves $15$-$20$dB gain in the signal-to-noise ratio (SNR) in a range of $pm60^circ$ beamforming angles. In terms of coverage, and considering a far-field experiment with a blockage between a base station and a grid of mobile users and with an average distance of $35m$ between base station (BS) and the user (through the RIS), the RIS provides an average SNR improvement of $6$dB (max $8$dB) within an area $> 75$m$^2$. Thanks to the scalable RIS design, these SNR gains can be directly increased with larger RIS areas. For example, a 1,600-element RIS with the same design is expected to provide around $26$dB SNR gain for a similar deployment. These results, among others, draw useful insights into the design and performance of RIS systems and provide an important proof for their potential gains in real-world far-field wireless communication environments.
Reconfigurable Intelligent Surfaces (RISs), comprising large numbers of low-cost and passive metamaterials with tunable reflection properties, have been recently proposed as an enabler for programmable radio propagation environments. However, the rol e of the channel conditions near the RISs on their optimizability has not been analyzed adequately. In this paper, we present an asymptotic closed-form expression for the mutual information of a multi-antenna transmitter-receiver pair in the presence of multiple RISs, in the large-antenna limit, using the random matrix and replica theories. Under mild assumptions, asymptotic expressions for the eigenvalues and the eigenvectors of the channel covariance matrices are derived. We find that, when the channel close to an RIS is correlated, for instance due to small angle spread, the communication link benefits significantly from the RIS optimization, resulting in gains that are surprisingly higher than the nearly uncorrelated case. Furthermore, when the desired reflection from the RIS departs significantly from geometrical optics, the surface can be optimized to provide robust communication links. Building on the properties of the eigenvectors of the covariance matrices, we are able to find the optimal response of the RISs in closed form, bypassing the need for brute-force optimization.
We present here a provenance management system adapted to astronomical projects needs. We collected use cases from various astronomy projects and defined a data model in the ecosystem developed by the IVOA (International Virtual Observatory Alliance) . From those use cases, we observed that some projects already have data collections generated and archived, from which the provenance has to be extracted (provenance on top), and some projects are building complex pipelines that automatically capture provenance information during the data processing (capture inside). Different tools and prototypes have been developed and tested to capture, store, access and visualize the provenance information, which participate to the shaping of a full provenance management system able to handle detailed provenance information.
Physical layer security is a useful tool to prevent confidential information from wiretapping. In this paper, we consider a generalized model of conventional physical layer security, referred to as hierarchical information accessibility (HIA). A main feature of the HIA model is that a network has a hierarchy in information accessibility, wherein decoding feasibility is determined by a priority of users. Under this HIA model, we formulate a sum secrecy rate maximization problem with regard to precoding vectors. This problem is challenging since multiple non-smooth functions are involved into the secrecy rate to fulfill the HIA conditions and also the problem is non-convex. To address the challenges, we approximate the minimum function by using the LogSumExp technique, thereafter obtain the first-order optimality condition. One key observation is that the derived condition is cast as a functional eigenvalue problem, where the eigenvalue is equivalent to the approximated objective function of the formulated problem. Accordingly, we show that finding a principal eigenvector is equivalent to finding a local optimal solution. To this end, we develop a novel method called generalized power iteration for HIA (GPI-HIA). Simulations demonstrate that the GPI-HIA significantly outperforms other baseline methods in terms of the secrecy rate.
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