62,288 research outputs found
RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network
Outdoor radio map estimation is an important tool for network planning and
resource management in modern Internet of Things (IoT) and cellular systems.
Radio map describes spatial signal strength distribution and provides network
coverage information. A practical goal is to estimate fine-resolution radio
maps from sparse radio strength measurements. However, non-uniformly positioned
measurements and access obstacles can make it difficult for accurate radio map
estimation (RME) and spectrum planning in many outdoor environments. In this
work, we develop a two-phase learning framework for radio map estimation by
integrating radio propagation model and designing a conditional generative
adversarial network (cGAN). We first explore global information to extract the
radio propagation patterns. We then focus on the local features to estimate the
effect of shadowing on radio maps in order to train and optimize the cGAN. Our
experimental results demonstrate the efficacy of the proposed framework for
radio map estimation based on generative models from sparse observations in
outdoor scenarios
NOMA based resource allocation and mobility enhancement framework for IoT in next generation cellular networks
With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the internet, forming what is called internet of things (IoT). IoT devices with heterogeneous characteristics and quality of experience (QoE) requirements may engage in dynamic spectrum market due to scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of the paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions, enhance the radio resource utilization efficiency. The results show substantial reduction in the number of sub-carriers required when compared to conventional orthogonal multiple access (OMA) and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has less user or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve required quality of service (QoS) with guaranteed bit rate (GBR) and non-guaranteed bit rate (Non-GBR)
On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems
Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output
(MIMO) systems is a favorable candidate for the fifth generation (5G) cellular
systems. However, a key challenge is the high power consumption imposed by its
numerous radio frequency (RF) chains, which may be mitigated by opting for
low-resolution analog-to-digital converters (ADCs), whilst tolerating a
moderate performance loss. In this article, we discuss several important issues
based on the most recent research on mmWave massive MIMO systems relying on
low-resolution ADCs. We discuss the key transceiver design challenges including
channel estimation, signal detector, channel information feedback and transmit
precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative
technique of improving the overall system performance. Finally, the associated
challenges and potential implementations of the practical 5G mmWave massive
MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin
Techniques of detection, estimation and coding for fading channels
The thesis describes techniques of detection, coding and estimation, for use in
high speed serial modems operating over fading channels such as HF radio and land mobile
radio links. The performance of the various systems that employ the above techniques are
obtained via computer simulation tests.
A review of the characteristics of HF radio channels is first presented, leading
to the development of an appropriate channel model which imposes Rayleigh fading on the
transmitted signal. Detection processes for a 4.8 kbit/s HF radio modem are then
discussed, the emphasis, here, being on variants of the maximum likelihood detector that is
implemented by the Viterbi algorithm. The performance of these detectors are compared
with that of a nonlinear equalizer operating under the same conditions, and the detector
which offers the best compromise between performance and complexity is chosen for
further tests.
Forward error correction, in the form of trellis coded modulation, is next
introduced. An appropriate 8-PSK coded modulation scheme is discussed, and its
operation over the above mentioned HF radio modem is evaluated. Performance
comparisons are made of the coded and uncoded systems.
Channel estimation techniques for fast fading channels akin to cellular land
mobile radio links, are next discussed. A suitable model for a fast fading channel is
developed, and some novel estimators are tested over this channel. Computer simulation
tests are also used to study the feasibility of the simultaneous transmission of two 4-level
QAM signals occupying the same frequency band, when each of these signals are
transmitted at 24 kbit/s over two independently fading channels, to a single receiver. A
novel combined detector/estimator is developed for this purpose.
Finally, the performance of the complete 4.8 kbit/s HF radio modem is
obtained, when all the functions of detection, estimation and prefiltering are present, where
the prefilter and associated processor use a recently developed technique for the adjustment
of its tap gains and for the estimation of the minimum phase sampled impulse response
Joint Pose and Radio Channel Estimation
This thesis investigates the combination of pose and radio channel estimation. Pose is the knowledge of the position and orientation of a device whereas the radio channel describes the transmission medium between radio transmitters and receivers. The two subjects are both active research topics with a long history of applications but there has to the author's knowledge been very little work published about combining the two areas using a sensor fusion framework. A well established approach for pose estimation is using an inertial measurement unit (IMU). Using an inexpensive IMU standalone for dead reckoning pose estimation is tempting but it is not a working solution due to noise and other imperfections in the IMU. There is also a fundamental limitation of inertial sensors, they can not, because of Galileo's principle, obtain any information about absolute velocity of the device. To obtain reliable pose estimates for a longer time, the measurements from the IMU must be fused with some other sensor information. This thesis shows how the pervasive electric magnetic fields from existing radio communication systems such as the cellular mobile systems GSM, 3G, or 4G can be used. Angle of arrival estimation using antenna arrays is a well studied problem with many different algorithms resolving the individual rays impinging on the array. However, less attention has been given to so called virtual array antennas where only one receiver element is used. By tracking the movement of the element, an array with properties similar to a stationary array with multiple elements is formed. By combining the IMU and the radio channel information, a map of the local radio environment can be obtained. At the same time, the map is used for adjusting for the errors in the IMU that lead to inaccurate pose estimates by using tightly coupled nonlinear state estimation algorithms from the sensor fusion framework. The goals for this thesis is to develop a dynamic model for kinematics and a ray-trace based radio channel model that can be used together with the particle filter for sensor fusion. It also contains an initial investigation of limitations and achievable performance for the joint pose and radio channel estimation problem, including radio imperfection such as thermal noise, and phase/frequency error. The proposed model is evaluated using both simulations and datasets from experiments. The analysis of the evaluation shows that the proposed model, together with sensor fusion algorithms, provides a breakthrough in pose estimation using a low cost IMU
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