Switched Quasi-Logarithmic Quantizer with Golomb–Rice Coding

This paper proposes a model of switched quasilogarithmic quantizer for speech signal based on G.711 standard with usage of Golomb-Rice (GR) coding. In order to achieve better performances a method with switched quantizer is applied. Variance range is split into quantizers and for each of them a separate quantizer is designed, i.e. the support region is determined. Optimization of the support region and choice of the parameter μ is done in order to obtain a quantizer that obeys G.712 standard and gives minimal average bit rate. Every quantizer within the variance range has own model with a two-stage coder. Two stages are introduced with purpose to reduce the bit rate, whereby GR code plays its role as Variable Length Code (VLC). The first stage uses a GR coder for coding segments of the quantizer's support region, whereas the second stage applies the coding method with fixed code lengths for coding cells within a segment. GR has simpler and cheaper hardware realization than other VLC codes, Huffman's for instance, with very satisfying results regarding quality of quantized signal

5G transport network requirements for the next generation fronthaul interface

To meet the requirements of 5G mobile networks, several radio access technologies, such as millimeter wave communications and massive MIMO, are being proposed. In addition, cloud radio access network (C-RAN) architectures are considered instrumental to fully exploit the capabilities of future 5G RANs. However, RAN centralization imposes stringent requirements on the transport network, which today are addressed with purpose-specific and expensive fronthaul links. As the demands on future access networks rise, so will the challenges in the fronthaul and backhaul segments. It is hence of fundamental importance to consider the design of transport networks alongside the definition of future access technologies to avoid the transport becoming a bottleneck. Therefore, we analyze in this work the impact that future RAN technologies will have on the transport network and on the design of the next generation fronthaul interface. To understand the especially important impact of varying user traffic, we utilize measurements from a real-world 4G network and, taking target 5G performance figures into account, extrapolate its statistics to a 5G scenario. With this, we derive both per-cell and aggregated data rate requirements for 5G transport networks. In addition, we show that the effect of statistical multiplexing is an important factor to reduce transport network capacity requirements and costs. Based on our investigations, we provide guidelines for the development of the 5G transport network architecture.Peer ReviewedPostprint (published version

5G-XHaul:a converged optical and wireless solution for 5G transport networks

This is the pre-peer reviewed version of the following article: Gutiérrez-Terán, J., Maletic, N., Camps, D., Garcia-Villegas, E., Berberana, I., Anastasopoulos, M., Tzanakaki, A., Kalokidou, V., Flegkas, P., Syrivelis, D., Korakis, T., Legg, P., Markovic, D., Limperopoulos, G., Bartelt, J., Chaudhary, J.K., Grieger, M., Vucic, N., Zou, J., Grass, E. 5G-XHaul: a converged optical and wireless solution for 5G transport networks. "Transactions on emerging telecommunications technologies", 8 Juliol 2016, vol. 27, núm. 9, p. 1187-1195, which has been published in final form at http://onlinelibrary.wiley.com.recursos.biblioteca.upc.edu/doi/10.1002/ett.3063/epdf. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.The common European Information and Communications Technology sector vision for 5G is that it should leverage on the strengths of both optical and wireless technologies. In the 5G context, a wide spectra of radio access technologies—such as millimetre wave transmission, massive multiple-input multiple-output and new waveforms—demand for high capacity, highly flexible and convergent transport networks. As the requirements imposed on future 5G networks rise, so do the challenges in the transport network. Hence, 5G-XHaul proposes a converged optical and wireless transport network solution with a unified control plane based on software defined networking. This solution is able to support the flexible backhaul and fronthaul—X-Haul—options required to tackle the future challenges imposed by 5G radio access technologies. 5G-XHaul studies the trade-offs involving fully or partially converged backhaul and fronthaul functions, with the aim of maximising the associated sharing benefits, improving efficiency in resource utilisation and providing measurable benefits in terms of overall cost, scalability and sustainabilityPeer ReviewedPostprint (published version

Wireless-optical network convergence: enabling the 5G architecture to support operational and end-user services

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This article presents a converged 5G network infrastructure and an overarching architecture to jointly support operational network and end-user services, proposed by the EU 5G PPP project 5G-XHaul. The 5G-XHaul infrastructure adopts a common fronthaul/backhaul network solution, deploying a wealth of wireless technologies and a hybrid active/passive optical transport, supporting flexible fronthaul split options. This infrastructure is evaluated through a novel modeling. Numerical results indicate significant energy savings at the expense of increased end-user service delay.Peer ReviewedPostprint (author's final draft

Robuste Optimierung von Mehrantennensystemen mit imperfekter Kanalkenntnis

Die Anpassung an den Mobilfunk Kanal ist ein kritischer Faktor um die räumliche Diversität als zusätzlichen Freiheitsgrad in Mehrantennensystemen effektiv auszunutzen. Auf Grund fehleranfälliger Kanalschätzung, Quantisierung, schnell wechselnder Umgebung kombiniert mit strikten Forderungen an die Verzögerung und Hardware-Einschränkungen ist die Annahme perfekter Kanalkenntnis in der Praxis nicht realistisch. In Abhängigkeit der Hauptfehlerquellen existieren verschiedene mathematische Modelle um die Unsicherheit zu beschreiben. Hierbei sind die Quantisierungsfehler üblicherweise beschränkt. Im Gegensatz dazu werden die Fehler bei der Kanalschätzung als unbeschränkte, gaußverteilte Zufallsvariablen angenommen. In modernen Mobifunksystemen werden bei der Entwicklung der Send- und Empfangseinheiten bestimmte Qualitätsanforderungen berücksichtigt. Wird die Fehlerbehaftung der Kanalkoeffizienten bei der Systementwicklung nicht mitberücksichtigt kann dies zu einer häufigen Verletzung der Anforderungen führen. Deswegen ist Robustheit bezüglich imperfekter Kanalkenntnis von besonderer, praktischer Bedeutung. Qualitätsanforderungen können strikt sein und haben damit eine besonders hohe Priorität. Für die anderen Anforderungen könnte (oder müßte) der Dienstanbieter eine bestimmte Prozentzahl der Ausfälle erlauben. Dies führt zu zwei unterschiedlichen Philosophien, der worst-case (schlimmster Fall) Optimierung und der probabilistischen Optimierung. Bei worst-case Methoden wird das strenge Einhalten der Performanzziele für alle Kanäle, die in einer bestimmten Unsicherheitsregion enthalten sind, gefordert. Demgegenüber zielt die probabilistische Optimierung darauf ab die Qualitätsanforderungen mit bestimmten Wahrscheinlichkeiten zu erfüllen. In dieser Arbeit werden Transceiver Optimierungsprobleme robust gelöst. Hauptsächlich wird die Abwärtsstrecke (downlink) im Mobilfunk untersucht. Hierbei liegt der Fokus auf imperfekter Kanalkentnis an der Senderseite. Für strikte Qualitätsanforderungen werden die Transceiver Optimierungsprobleme als semidefinite Programme mit effizienten iterativen Lösungen umgeschrieben. Bei probabilistischen Anforderungen werden die speziellen Eigenschaften der vorhandenen Zufallsvariablen (wie zum Beispiel die Unimodalität) genutzt, um Algorithmen abzuleiten, die auf der Interferenzfunktionentheorie oder auf konvexer Optimierung basieren. Für Sonderfälle werden Lösungen in geschlossener Form entwickelt. Im Vergleich zu Methoden aus der Literatur erzielen die in dieser Arbeit entwickelten Algorithmen erhebliche Verbesserungen bezüglich der Performanz oder der Komplexität.Exploitation of spatial diversity, as an additional degree of freedom in multiple antenna wireless systems, depends crucially on ability to adapt to channel conditions. The assumption of having perfect knowledge of the channel is, however, often unrealistic in practice. Noise-prone channel estimation, quantization effects, fast varying environment combined with delay requirements, and hardware limitations are some of the most important factors that cause errors. Depending on the primary source of errors in channel state information (CSI), various mathematical models for the uncertainty can be adopted. For example, quantization errors are usually bounded, while estimation errors are often modeled as Gaussian random variables. Modern wireless systems are supposed to include quality-of-service (QoS) based transceiver designs. However, a transceiver design that does not account for the errors in CSI can result in frequent violation of the promised QoS targets. Providing robustness to imperfect CSI is, therefore, a task of significant practical interest. Some QoS targets might be of particular importance and defined as strict. In other cases, the operator will be either willing or forced to allow a certain percentage of outages in the system. These observations naturally lead to two robust philosophies: the worst-case optimization and the probabilistically constrained optimization. In the worst-case approaches, some performance targets must be satisfied for all channels contained in the uncertainty regions. On the other hand, the probabilistically constrained optimization has a goal of satisfying the QoS constraints with certain probabilities. The principal task of this thesis is solving transceiver optimization problems in a robust manner. The accent is put on downlink systems and the critical problem of imperfect CSI at the transmitter. As main tools, extensions of mathematical programming for supporting uncertain problem parameters are employed. The used iterative algorithms benefit from a major recent progress in optimization theory, which was particularly noticeable in the area of convex programming. Robust transceiver optimization problems with strict QoS targets are rewritten as semidefinite programming problems, which have efficient numerical solutions. In the case of probabilistic constraints, the exploitation of specific properties of random variables at hand, such as unimodality, yielded problems solvable by theory of interference functions, or conic quadratic programming. In some special setups, closed-form solutions are derived. The obtained algorithms for the worst-case and probabilistically constrained robust transceiver designs outperform relevant results from the literature in terms of performance or computational complexity

Forward Adaptive Laplacian Source Coding Based on Restricted Quantization

A novel solution for Laplacian source coding based on three-level quantization is proposed in this paper. The restricted three-level quantizer is designed by assuming the restricted Laplacian distribution of the input signal. Quantizer and Huffman encoder are jointly designed. Forward adaptive scheme was employed, where the adaptation to the signal variance (power) was performed on frame-by frame basis. We employ switched model that consists of two restricted quantizers having unequal support regions. The simulation results (measured as SQNR) of the proposed scheme with a switched restricted three-level quantizer are compared to the cases when it involves three-level unrestricted quantizer and the Lloyd-Max quantizers having N=2 and N=4 levels. It is shown that the proposed solution offers performance comparable to the one of N=4 levels Lloyd-Max’s baseline with large savings in bit rate, while outperforming two other baselines

Dual-mode quasi-logarithmic quantizer with embedded G.711 codec

The G.711 codec has been accepted as a standard for high quality coding in many applications. A dual-mode quantizer, which combines the nonlinear logarithmic quantizer for restricted input signals and G.711 quantizer for unrestricted input signals is proposed in this paper. The parameters of the proposed quantizer are optimized, where the minimal distortion is used as the criterion. It is shown that the optimized version of the proposed quantizer provides 5.4 dB higher SQNR (Signal to Quantization Noise Ratio) compared to G.711 quantizer, or equivalently it performs savings in the bit rate of approximately 0.9 bit/sample for the same signal quality. Although the complexity is slightly increased, we believe that due to the superior performance it can be successfully implemented for high-quality quantization

Initial 3D Cadastre Registration in the Republic of Croatia by Cadastral Resurvey

By using the formal methods, this paper will present the model of the cadastral reform from the register of land into the contemporary register which is the backbone of a modern Multipurpose Land Administration System. Model will include its static components but also related processes. The Land Administration Domain Model (LADM), which in 2012 become ISO 19152 standard (Lemmen et al. 2013), will be used as the basis. This paper gives proposal for cadastral resurvey improvements, better registration of special legal regimes and registration of special parts of properties. It will identify and explore critical points and make recommendations to bring Real Property Cadastre closer to become real 3D register