| 1. |
- Ansari, Rafay Iqbal, et al.
(author)
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Control-Data Separation Architecture for Dual-Band mmWave Networks : A New Dimension to Spectrum Management
- 2019
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In: IEEE Access. - 2169-3536. ; 7, s. 34925-34937
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Journal article (peer-reviewed)abstract
- The exponential growth in global mobile data traffic, especially with regards to the massive deployment of devices envisioned for the fifth generation (5G) mobile networks, has given impetus to exploring new spectrum opportunities to support the new traffic demands. The millimeter wave (mmWave) frequency band is considered as a potential candidate for alleviating the spectrum scarcity. Moreover, the concept of multi-tier networks has gained popularity, especially for dense network environments. In this article, we deviate from the conventional multi-tier networks and employ the concept of control-data separation architecture (CDSA), which comprises of a control base station (CBS) overlaying the data base station (DBS). We assume that the CBS operates on the sub-6 GHz single band, while the DBS possesses a dual-band mmWave capability, i.e., 26 GHz unlicensed band and 60 GHz licensed band. We formulate a multi-objective optimization (MOO) problem, which jointly optimizes conflicting objectives: the spectral efficiency (SE) and the energy efficiency (EE). The unique aspect of this work includes the analysis of a joint radio resource allocation algorithm based on Lagrangian Dual Decomposition (LDD) and we compare the proposed algorithm with the maximal-rate (maxRx), dynamic sub-carrier allocation (DSA) and joint power and rate adaptation (JPRA) algorithms to show the performance gains achieved by the proposed algorithm.
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| 2. |
- Shah, Syed Aziz, et al.
(author)
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Sensor Fusion for Identification of Freezing of Gait Episodes Using Wi-Fi and Radar Imaging
- 2020
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In: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 20:23, s. 14410-14422
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Journal article (peer-reviewed)abstract
- Parkinsons disease (PD) is a progressive and neurodegenerative condition causing motor impairments. One of the major motor related impairments that present biggest challenge is freezing of gait (FOG) in Parkinsons patients. In FOG episode, the patient is unable to initiate, control or sustain a gait that consequently affects the Activities of Daily Livings (ADLs) and increases the occurrence of critical events such as falls. This paper presents continuous monitoring ADLs and classification freezing of gait episodes using Wi-Fi and radar imaging. The idea is to exploit the multi-resolution scalograms generated by channel state information (CSI) imprint and micro-Doppler signatures produced by reflected radar signal. A total of 120 volunteers took part in experimental campaign and were asked to perform different activities including walking fast, walking slow, voluntary stop, sitting down & stand up and freezing of gait. Two neural networks namely Autoencoder and a proposed enhanced Autoencoder were used classify ADLs and FOG episodes using data fusion process by combining the images acquired from both sensing techniques. The Autoencoder provided overall classification accuracy of similar to 87% for combined datasets. The proposed algorithm provided significantly better results by presenting an overall accuracy of similar to 98% using data fusion.
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