Localization technique based on dual-frequency doppler ranging estimation

Positioning awareness plays an important role in modern applications such as internet of thing (IoT) and intelligent transportation system (ITS). One of the commonly used positioning techniques is trilateration as it estimates the location of a blind device or node by using the distance from several other devices or anchor nodes. The ranging technique used to obtain distance information is a crucial step to provide high accuracy in location estimation. Dual-frequency Doppler radar (DFDR) ranging technique has been widely used in radars and radio frequency identification (RFID) applications. In radar application, this technique requires a closed-loop communication link to estimate distance and has not been exploited in single radio frequency transmission. In this thesis, a ranging technique not requiring a closedloop communication link named one-way forward communication link dual-frequency Doppler (DFD) ranging technique is introduced. The performances of the DFD distance estimation were analysed using simulations and experimental measurements. In the DFD experiment, the anchor node transmitted two different frequencies with a certain frequency separation. The blind node captured the received signal, and the phase difference was extracted and unwrapped using offline processing system. The phase difference between the two received signals was used for DFD ranging estimation before being applied to locate the position of the blind node through trilateration method. Software defined radio (SDR) platform using GNU radio and universal software radio peripheral (USRP) was used to develop the localization system. The experimental results showed that DFD ranging technique can deliver up to 84% distance estimation improvement in comparison to conventional receive signal strength (RSS) ranging technique. In conclusion, the proposed DFD ranging technique is a promising positioning solution for future applications such as IoT and ITS

Evaluation of Natural Robustness of Best Constant Weights to Random Communication Breakdowns

One of the most crucial aspects of an algorithmdesign for the wireless sensors networks is the failure tolerance.A high natural robustness and an effectively bounded executiontime are factors that can significantly optimize the overall energyconsumption and therefore, a great emphasis is laid on theseaspects in many applications from the area of the wireless sensornetworks. This paper addresses the robustness of the optimizedBest Constant weights of Average Consensus with a stoppingcriterion (i.e. the algorithm is executed in a finite time) and theirfive variations with a lower mixing parameter (i.e. slowervariants) to random communication breakdowns modeled as a stochastic event of a Bernoulli distribution. We choose threemetrics, namely the deviation of the least precise final estimatesfrom the average, the convergence rate expressed as the numberof the iterations for the consensus, and the deceleration of eachinitial setup, in order to evaluate the robustness of various initialsetups of Best Constant weights under a varying failureprobability and over 30 random geometric graphs of either astrong or a weak connectivity. Our contribution is to find themost robust initial setup of Best Constant weights according tonumerical experiments executed in Matlab. Finally, theexperimentally obtained results are discussed, compared to theresults from the error-free executions, and our conclusions arecompared with the conclusions from related papers

EARPC – Energy Aware Routing Protocol for Cooperative MIMO Scheme in WSNs, Journal of Telecommunications and Information Technology, 2020, nr 3

Wireless sensor networks are typically operated on batteries. Therefore, in order to prolong network lifetime, an energy efficient routing algorithm is required. In this paper, an energy-aware routing protocol for the co-operative MIMO scheme in WSNs (EARPC) is presented. It is based on an improved cluster head selection method that considers the remaining energy level of a node and recent energy consumption of all nodes. This means that sensor nodes with lower energy levels are less likely to be chosen as cluster heads. Next, based on the cooperative node selection in each cluster, a virtual MIMO array is created, reducing uneven distribution of clusters. Simulation results show that the proposed routing protocol may reduce energy consumption and improve network lifetime compared with the LEACH protoco

SMARAD - Centre of Excellence in Smart Radios and Wireless Research - Activity Report 2011 - 2013

Centre of Excellence in Smart Radios and Wireless Research (SMARAD), originally established with the name Smart and Novel Radios Research Unit, is aiming at world-class research and education in Future radio and antenna systems, Cognitive radio, Millimetre wave and THz techniques, Sensors, and Materials and energy, using its expertise in RF, microwave and millimeter wave engineering, in integrated circuit design for multi-standard radios as well as in wireless communications. SMARAD has the Centre of Excellence in Research status from the Academy of Finland since 2002 (2002-2007 and 2008-2013). Currently SMARAD consists of five research groups from three departments, namely the Department of Radio Science and Engineering, Department of Micro and Nanosciences, and Department of Signal Processing and Acoustics, all within the Aalto University School of Electrical Engineering. The total number of employees within the research unit is about 100 including 8 professors, about 30 senior scientists and about 40 graduate students and several undergraduate students working on their Master thesis. The relevance of SMARAD to the Finnish society is very high considering the high national income from exports of telecommunications and electronics products. The unit conducts basic research but at the same time maintains close co-operation with industry. Novel ideas are applied in design of new communication circuits and platforms, transmission techniques and antenna structures. SMARAD has a well-established network of co-operating partners in industry, research institutes and academia worldwide. It coordinates a few EU projects. The funding sources of SMARAD are diverse including the Academy of Finland, EU, ESA, Tekes, and Finnish and foreign telecommunications and semiconductor industry. As a by-product of this research SMARAD provides highest-level education and supervision to graduate students in the areas of radio engineering, circuit design and communications through Aalto University and Finnish graduate schools. During years 2011 – 2013, 18 doctor degrees were awarded to the students of SMARAD. In the same period, the SMARAD researchers published 197 refereed journal articles and 360 conference papers

Comparative Study of Distributed Estimation Precision by Average Consensus Weight Models

Distributed algorithms for an aggregate function estimation are an important complement of many real-life applications based on wireless sensor networks. Achieving a high precision of an estimation in a shorter time can optimize the overall energy consumption. Therefore, the choice of a proper distributed algorithm is an important part of an application design. In this study, we focus our attention on the average consensus algorithm and evaluate six weight models appropriate for the implementation into real-life applications. Our aim is to find the most suitable model in terms of the estimation precision in various phases of the algorithm. We examine the deviation of the least precise estimate over iterations for a Gaussian, a Uniform and a Bernoulli distribution of the initial states in strongly and weakly connected networks with a randomly generated topology. We examine which model is the most and the least precise in various phases. Based on these findings, we determine the most suitable model for real-life applications

High-resolution multipath channel parameter estimation using wavelet analysis

This thesis explores the novel use of wavelet analysis as a high-resolution digital signal processing algorithm for multipath channel parameter estimation. The results obtained from this research indicate that this wavelet-based digital signal processing algorithm overcomes the resolution limitation in conventional high-resolution algorithm. This may provide a more cost-effective means of implementing channel sounding equipments for very high-resolution measurements

Channel Estimation in Massive Multi-User MIMO Systems Based on Low-Rank Matrix Approximation

In recent years, massive Multi-User Multi-Input Multi-Output (MU-MIMO) system has attracted significant research interests in mobile communication systems. It has been considered as one of the promising technologies for 5G mobile wireless networks. In massive MU-MIMO system, the base station (BS) is equipped with a very large number of antenna elements and simultaneously serves a large number of single-antenna users. Compared to traditional MIMO system with fewer antennas, massive MU-MIMO system can offer many advantages such as significant improvements in both spectral and power efficiencies. However, the channel estimation in massive MU-MIMO system is particularly challenging due to large number of channel matrix entries to be estimated within a limited coherence time interval. This problem occurs in a single-cell case where both dimensions of the channel matrix grow large. Also, It happens in the multi-cell setting due to the pilot contamination effect. In this thesis, the problem of channel estimation in both single-cell and multi-cell time division duplex (TDD) massive MU-MIMO systems is studied. Thus, two-channel estimation namely “nuclear norm (NN)” and “iterative weighted nuclear norm (IWNN)” approximation techniques are proposed to solve the channel estimation problem in both systems. First, channel estimation in a single-cell TDD massive MU-MIMO system is formulated as a convex nuclear norm optimization problem with regularization parameter γ. In this study, the regularization parameter γ is selected based on the cross-validation (CV) curve method. The simulation results in terms of the normalized mean square error (NMSE) and uplink achievable sum-rate (ASR) are provided to show the effectiveness of the NN proposed scheme compared to the conventional least square (LS) estimator. Then, the IWNN approximation is proposed to improve the performance of the NN method. Thus, the channel estimation in a single-cell TDD massive MU-MIMO system is formulated as a weighted nuclear norm optimization problem. The simulation results show the effectiveness of the IWNN estimation approach compared to the standard NN and conventional LS estimation methods in terms of the NMSE and ASR. Second, both previous estimation techniques are extended to apply in a multi-cell TDD massive MU-MIMO system to mitigate pilot contamination effect. The simulation results in terms of the NMSE and uplink ASR show that the IWNN scheme outperforms the NN and LS estimations in the presence of high pilot contamination effect. Finally, a novel channel estimation scheme namely “Approximate minimum mean square error (AMMSE)” is proposed to reduce the computational complexity of the minimum mean square error (MMSE) estimator which was proposed for multi-cell TDD massive MU-MIMO system. Furthermore, a brief analysis of the computational complexity regarding the number of multiplications of the proposed AMMSE estimator is provided. It has been shown that the complexity of the proposed AMMSE estimator is reduced compared to the conventional MMSE estimator. The simulation results in terms of the NMSE and the uplink ASR performances show the proposed AMMSE estimation performance is almost the same as the conventional MMSE estimator under two different scenarios: noise-limited and pilot contamination

Low Power Memory/Memristor Devices and Systems

This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within