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Riku Jantti

Publications and source records attributed to Riku Jantti.

31 records · Page 2Linked to original sources

Zero-Energy-Device for 6G: First Real-Time Backscatter Communication thanks to the Detection of Pilots from an Ambient Commercial Cellular Network

Ambient backscatter communication technology (AmBC) and a novel device category called zero-energy devices (ZED) have recently emerged as potential components for the forthcoming 6th generation (6G) networks. A ZED communicates with a smartphone without emitting additional radio waves, by backscattering ambient waves from base stations. Thanks to its very low consumption, a ZED powers itself by harvesting ambient light energy. However, the time variations of data traffic in cellular networks prevents AmBC to work properly. Recent works have demonstrated experimentally that a backscatter device could be detected by listening only ambient pilot signals (which are steady) instead of the whole ambient signal (which is bursty) of 4G. However, these experiments were run with a 4G base station emulator and a bulky energy greedy backscatter device. In this paper, for the first time, we demonstrate real-time AmBC on the field, with Orange commercial 4G network as ambient source and Orange Zero-Energy Device.

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Ambient FSK Backscatter Communications using LTE Cell Specific Reference Signals

Long Term Evolution (LTE) signal is ubiquitously present in electromagnetic (EM) background environment, which make it an attractive signal source for the ambient backscatter communications (AmBC). In this paper, we propose a system, in which a backscatter device (BD) introduces artificial Doppler shift to the channel which is larger than the natural Doppler but still small enough such that it can be tracked by the channel estimator at the User Equipment (UE). Channel estimation is done using the downlink cell specific reference signals (CRS) that are present regardless the UE being attached to the network or not. FSK was selected due to its robust operation in a fading channel. We describe the whole AmBC system, use two receivers. Finally, numerical simulations and measurements are provided to validate the proposed FSK AmBC performance.

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Ambient backscatter communications using LTE cell specific reference signals

Long Term Evolution (LTE) systems provide ubiquitous coverage for mobile communications, which makes it a promising candidate to be used as a signal source in the ambient backscatter communications. In this paper, we propose a system in which a backscatter device modulates the ambient LTE signal by changing its reflection coefficient and the receiver uses the LTE Cell Specific Reference Signals (CRS) to estimate the channel and demodulates the backscattered signal from the obtained channel impulse response estimates. We first outline the overall system, discuss the receiver operation, and then provide experimental evidence on the practicality of the proposed system.

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Secrecy Limits of Energy Harvesting IoT Networks under Channel Imperfections

Simultaneous wireless information and power transfer (SWIPT) has recently gathered much research interest from both academia and industry as a key enabler of energy harvesting Internet-of-things (IoT) networks. Due to a number of growing use cases of such networks, it is important to study their performance limits from the perspective of physical layer security (PLS). With this intent, this work aims to provide a novel analysis of the ergodic secrecy capacity of a SWIPT system is provided for Rician and Nakagami-m faded communication links. For a realistic evaluation of the system, the imperfections of channel estimations for different receiver designs of the SWIPT-based IoT systems have been taken into account. Subsequently, the closedform expressions of the ergodic secrecy capacities for the considered scenario are provided and, then, validated through extensive simulations. The results indicate that an error ceiling appears due to imperfect channel estimation at high values of signal-to-noise ratio (SNR). More importantly, the secrecy capacity under different channel conditions stops increasing beyond a certain limit, despite an increase of the main link SNR. The in-depth analysis of secrecy-energy trade-off has also been performed and a comparison has been provided for imperfect and perfect channel estimation cases. As part of the continuous evolution of IoT networks, the results provided in this work can help in identifying the secrecy limits of IoT networks in the presence of multiple eavesdroppers.

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Efficient Power-Splitting and Resource Allocation for Cellular V2X Communications

The research efforts on cellular vehicle-to-everything (V2X) communications are gaining momentum with each passing year. It is considered as a paradigm-altering approach to connect a large number of vehicles with minimal cost of deployment and maintenance. This article aims to further push the state-of-the-art of cellular V2X communications by providing an optimization framework for wireless charging, power allocation, and resource block assignment. Specifically, we design a network model where roadside objects use wireless power from RF signals of electric vehicles for charging and information processing. Moreover, due to the resource-constraint nature of cellular V2X, the power allocation and resource block assignment are performed to efficiently use the resources. The proposed optimization framework shows an improvement in terms of the overall energy efficiency of the network when compared with the baseline technique. The performance gains of the proposed solution clearly demonstrate its feasibility and utility for cellular V2X communications.

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Efficient Mining Cluster Selection for Blockchain-based Cellular V2X Communications

Cellular vehicle-to-everything (V2X) communication is expected to herald the age of autonomous vehicles in the coming years. With the integration of blockchain in such networks, information of all granularity levels, from complete blocks to individual transactions, would be accessible to vehicles at any time. Specifically, the blockchain technology is expected to improve the security, immutability, and decentralization of cellular V2X communication through smart contract and distributed ledgers. Although blockchain-based cellular V2X networks hold promise, many challenges need to be addressed to enable the future interoperability and accessibility of such large-scale platforms. One such challenge is the offloading of mining tasks in cellular V2X networks. While transportation authorities may try to balance the network mining load, the vehicles may select the nearest mining clusters to offload a task. This may cause congestion and disproportionate use of vehicular network resources. To address this issue, we propose a game-theoretic approach for balancing the load at mining clusters while maintaining fairness among offloading vehicles. Keeping in mind the low-latency requirements of vehicles, we consider a finite channel blocklength transmission which is more practical compared to the use of infinite blocklength codes. The simulation results obtained with our proposed offloading framework show improved performance over the conventional nearest mining cluster selection technique.

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Machine Learning assisted Handover and Resource Management for Cellular Connected Drones

Enabling cellular connectivity for drones introduces a wide set of challenges and opportunities. Communication of cellular-connected drones is influenced by 3-dimensional mobility and line-of-sight channel characteristics which results in higher number of handovers with increasing altitude. Our cell planning simulations in coexistence of aerial and terrestrial users indicate that the severe interference from drones to base stations is a major challenge for uplink communications of terrestrial users. Here, we first present the major challenges in co-existence of terrestrial and drone communications by considering real geographical network data for Stockholm. Then, we derive analytical models for the key performance indicators (KPIs), including communications delay and interference over cellular networks, and formulate the handover and radio resource management (H-RRM) optimization problem. Afterwards, we transform this problem into a machine learning problem, and propose a deep reinforcement learning solution to solve H-RRM problem. Finally, using simulation results, we present how the speed and altitude of drones, and the tolerable level of interference, shape the optimal H-RRM policy in the network. Especially, the heat-maps of handover decisions in different drone's altitudes/speeds have been presented, which promote a revision of the legacy handover schemes and redefining the boundaries of cells in the sky.

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Applications of Backscatter Communications for Healthcare Networks

Backscatter communication is expected to help in revitalizing the domain of healthcare through its myriad applications. From on-body sensors to in-body implants and miniature embeddable devices, there are many potential use cases that can leverage the miniature and low-powered nature of backscatter devices. However, the existing literature lacks a comprehensive study that provides a distilled review of the latest studies on backscatter communications from the healthcare perspective. Thus, with the objective to promote the utility of backscatter communication in healthcare, this paper aims to identify specific applications of backscatter systems. A detailed taxonomy of recent studies and gap analysis for future research directions are provided in this work. Finally, we conduct measurements at 590 MHz in different propagation environments with the in-house designed backscatter device. The link budget results show the promise of backscatter devices to communicate over large distances for indoor environments which demonstrates its potential in the healthcare system.

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5G Control Channel Design for Ultra-Reliable Low-Latency Communications

The fifth generation (5G) of wireless systems holds the promise of supporting a wide range of services with different communication requirements. Ultra-reliable low-latency communications (URLLC) is a generic service that enables mission-critical applications, such as industrial automation, augmented reality, and vehicular communications. URLLC has stringent requirements for reliability and latency of delivering both data and control information. In order to meet these requirements, the Third Generation Partnership Project (3GPP) has been introducing new features to the upcoming releases of the cellular system standards, namely releases 15 and beyond. This article reviews some of these features and introduces new enhancements for designing the control channels to efficiently support the URLLC. In particular, a flexible slot structure is presented as a solution to detect a failure in delivering the control information at an early stage, thereby allowing timely retransmission of the control information. Finally, some remaining challenges and envisioned research directions are discussed for shaping the 5G new radio (NR) as a unified wireless access technology for supporting different services.

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Energy Efficiency Maximization of Full-Duplex Two-Way Relay With Non-Ideal Power Amplifiers and Non-Negligible Circuit Power

In this paper, we maximize the energy efficiency (EE) of full-duplex (FD) two-way relay (TWR) systems under non-ideal power amplifiers (PAs) and non-negligible transmission-dependent circuit power. We start with the case where only the relay operates full duplex and two timeslots are required for TWR. Then, we extend to the advanced case, where the relay and the two nodes all operate full duplex, and accomplish TWR in a single timeslot. In both cases, we establish the intrinsic connections between the optimal transmit powers and durations, based on which the original non-convex EE maximization can be convexified and optimally solved. Simulations show the superiority of FD-TWR in terms of EE, especially when traffic demand is high. The simulations also reveal that the maximum EE of FD-TWR is more sensitive to the PA efficiency, than it is to self-cancellation. The full FD design of FD-TWR is susceptible to traffic imbalance, while the design with only the relay operating in the FD mode exhibits strong tolerance.

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Design and Analysis of Downlink Channel Estimation Based on Parametric Model for Massive MIMO in FDD Systems

This paper investigates downlink channel estimation in frequency-division duplex (FDD)-based massive multiple-input multiple-output (MIMO) systems. To reduce the overhead of downlink channel estimation and uplink feedback in FDD systems, cascaded precoding has been used in massive MIMO such that only a low-dimensional effective channel needs to be estimated and fed back. On the other hand, traditional channel estimations can hardly achieve the minimum mean-square-error (MMSE) performance due to lack of the a priori knowledge of the channels. In this paper, we design and analyze a strategy for downlink channel estimation based on the parametric model in massive MIMO with cascaded precoding. For a parametric model, channel frequency responses are expressed using the path delays and the associated complex amplitudes. The path delays of uplink channels are first estimated and quantized at the base station, then fed forward to the user equipment (UE) through a dedicated feedforward link. In this manner, the UE can obtain the a priori knowledge of the downlink channel in advance since it has been demonstrated that the downlink and the uplink channels can have identical path delays. Our analysis and simulation results show that the proposed approach can achieve near-MMSE performance.

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On the Aggregate Interference in Random CSMA/CA Networks: A Stochastic Geometry Approach

In this paper, we investigate the cumulative distribution function (CDF) of the aggregate interference in carrier sensing multiple access/collision avoidance (CSMA/CA) networks measured at an arbitrary time and position. We assume that nodes are deployed in an infinite two-dimensional plane by Poisson point process (PPP) and the channel model follows the singular path loss function and Rayleigh fading. To find the effective active node density we analyze the distributed coordinate function (DCF) dynamics in a common sensing area and obtain the steady-state power distribution within a spatial disk of radius $R/2$, where $R$ is the effective carrier sensing distance. The results of massive simulation using Network Simulator-2 (NS-2) show a high correlation with the derived CDF.

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Input-Output Clustering Criterion (IOCC) for Optimizing Distributed Antenna Locations

In this paper, we propose an input-output space clustering criterion (IOCC) to optimize the locations of the remote antenna units (RAUs) of generalized Distributed Antenna Systems (DASs) under sum power constraint. In IOCC, the input space refers to RAU location space and output space refers to location specific ergodic capacity space for noise-limited environments. Given a location-specific arbitrary desired ergodic capacity function over a geographical area, we define the error as the difference between actual and desired ergodic capacity. Our investigations show that i) the IOCC provides an upper bound to the cell averaged ergodic capacity error; and ii) the derived upper bound is equal to a weighted quantization error function in location-capacity space (input-output space) and iii) the upper bound can be made arbitrarily small by a clustering process increasing the number of RAUs for a feasible DAS. IOCC converts the RAU location problem into a codebook design problem in vector quantization in input-output space, and thus includes the Squared Distance Criterion (SDC) for DAS in [15] (and other related papers) as a special case, which takes only the input space into account. Computer simulations confirm the theoretical findings and show that the IOCC outperforms the SDC for DAS in terms of the defined cell averaged "effective" ergodic capacity.

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