Hard Decision Cooperative Spectrum Sensing Based on Estimating the Noise Uncertainty Factor

Spectrum Sensing (SS) is one of the most challenging issues in Cognitive Radio (CR) systems. Cooperative Spectrum Sensing (CSS) is proposed to enhance the detection reliability of a Primary User (PU) in fading environments. In this paper, we propose a hard decision based CSS algorithm using energy detection with taking into account the noise uncertainty effect. In the proposed algorithm, two dynamic thresholds are toggled based on predicting the current PU activity, which can be successfully expected using a simple successive averaging process with time. Also, their values are evaluated using an estimated value of the noise uncertainty factor. These dynamic thresholds are used to compensate the noise uncertainty effect and increase (decrease) the probability of detection (false alarm), respectively. Theoretical analysis is performed on the proposed algorithm to deduce its enhanced false alarm and detection probabilities compared to the conventional hard decision CSS. Moreover, simulation analysis is used to confirm the theoretical claims and prove the high performance of the proposed scheme compared to the conventional CSS using different fusion rules.Comment: 5 pages, 4 figures, IEEE International Conference on Computer Engineering and Systems (ICCES 2015). arXiv admin note: text overlap with arXiv:1505.0558

Cloud Cooperated Heterogeneous Cellular Networks for Delayed Offloading using Millimeter Wave Gates

Increasing the capacity of wireless cellular network is one of the major challenges for the coming years. A lot of research works have been done to exploit the ultra-wide band of millimeter wave (mmWave) and integrate it into future cellular networks. In this paper, to efficiently utilize the mmWave band while reducing the total deployment cost, we propose to deploy the mmWave access in the form of ultra-high capacity mmWave gates distributed in the coverage area of the macro basestation (Macro BS). Delayed offloading is also proposed to proficiently exploit the gates and relax the demand of deploying a large number of them. Furthermore, a mobility-aware weighted proportional fair (WPF) user scheduling is proposed to maximize the intra-gate offloading efficiency while maintaining the long-term offloading fairness among the users inside the gate. To efficiently link the mmWave gates with the Macro BS in a unified cellular network structure, a cloud cooperated heterogeneous cellular network (CC-HetNet) is proposed. In which, the gates and the Macro BS are linked to the centralized radio access network (C-RAN) via high-speed backhaul links. Using the concept of control/user (C/U) plane splitting, signaling information is sent to the UEs through the wide coverage Macro BS, and most of users’ delayed traffic is offloaded through the ultra-high capacity mmWave gates. An enhanced access network discovery and selection function (eANDSF) based on a network wide proportional fair criterion is proposed to discover and select an optimal mmWave gate to associate a user with delayed traffic. It is interesting to find out that a mmWave gate consisting of only 4 mmWave access points (APs) can offload up to 70 GB of delayed traffic within 25 sec, which reduces the energy consumption of a user equipment (UE) by 99.6 % compared to the case of only using Macro BS without gate offloading. Also, more than a double increase in total gates offloaded bytes is obtained using the proposed eANDSF over using the conventional ANDSF proposed by 3GPP due to the optimality in selecting the associating gate. 

Millimeter Wave Beamforming Training: A Reinforcement Learning Approach

Beamforming training (BT) is considered as an essential process to accomplish the communications in the millimeter wave (mmWave) band, i.e., 30 ~ 300 GHz. This process aims to find out the best transmit/receive antenna beams to compensate the impairments of the mmWave channel and successfully establish the mmWave link. Typically, the mmWave BT process is highly-time consuming affecting the overall throughput and energy consumption of the mmWave link establishment. In this paper, a machine learning (ML) approach, specifically reinforcement learning (RL), is utilized for enabling the mmWave BT process by modeling it as a multi-armed bandit (MAB) problem with the aim of maximizing the long-term throughput of the constructed mmWave link. Based on this formulation, MAB algorithms such as upper confidence bound (UCB), Thompson sampling (TS), epsilon-greedy (e-greedy), are utilized to address the problem and accomplish the mmWave BT process. Numerical simulations confirm the superior performance of the proposed MAB approach over the existing mmWave BT techniques.   

Soft Decision Cooperative Spectrum Sensing Based Upon Noise Uncertainty Estimation

Spectrum Sensing (SS) constitutes the most critical task i n Cognitive Radio (CR) systems for Primary User (PU) detection. Cooperative Spectrum Sensing (CSS) is introduced to enhance the detection reliability of the PU in fading environments. In this paper, we propose a soft decision based CSS algorithm using energy detection by taking into account the noise uncertainty effect. In the proposed algorithm, two threshold levels are utilized based on predicting the current PU activity, which can be successfully expected using a simple successive averaging process with time. The two threshold levels are evaluated based on estimating the noise uncertainty factor. In addition, they are toggled in a dynamic manner to compensate the noise uncertainty effect and to increase the probability of detection and decrease the probability of false alarm. Theoretical analysis is performed on the proposed algorithm to evaluate its enhanced false alarm and detection probabilities over the conventional soft decision CSS using different combining schemes. In addition, simulation results show the high efficiency of the proposed scheme compared to the conventional soft decision CSS, with high computational complexity enhancements.Comment: 6 Pages, 5 Figures, ICC workshops 201

Combinatorial optimisation for arterial image segmentation.

Cardiovascular disease is one of the leading causes of the mortality in the western world. Many imaging modalities have been used to diagnose cardiovascular diseases. However, each has different forms of noise and artifacts that make the medical image analysis field important and challenging. This thesis is concerned with developing fully automatic segmentation methods for cross-sectional coronary arterial imaging in particular, intra-vascular ultrasound and optical coherence tomography, by incorporating prior and tracking information without any user intervention, to effectively overcome various image artifacts and occlusions. Combinatorial optimisation methods are proposed to solve the segmentation problem in polynomial time. A node-weighted directed graph is constructed so that the vessel border delineation is considered as computing a minimum closed set. A set of complementary edge and texture features is extracted. Single and double interface segmentation methods are introduced. Novel optimisation of the boundary energy function is proposed based on a supervised classification method. Shape prior model is incorporated into the segmentation framework based on global and local information through the energy function design and graph construction. A combination of cross-sectional segmentation and longitudinal tracking is proposed using the Kalman filter and the hidden Markov model. The border is parameterised using the radial basis functions. The Kalman filter is used to adapt the inter-frame constraints between every two consecutive frames to obtain coherent temporal segmentation. An HMM-based border tracking method is also proposed in which the emission probability is derived from both the classification-based cost function and the shape prior model. The optimal sequence of the hidden states is computed using the Viterbi algorithm. Both qualitative and quantitative results on thousands of images show superior performance of the proposed methods compared to a number of state-of-the-art segmentation methods

WiFi Assisted Multi-WiGig AP Coordination for Future Multi-Gbps WLANs

Wireless Gigabit (WiGig) access points (APs) using 60 GHz unlicensed frequency band are considered as key enablers for future Gbps wireless local area networks (WLANs). Exhaustive search analog beamforming (BF) is mainly used with WiGig transmissions to overcome channel propagation loss and accomplish high rate data transmissions. Due to its short range transmission with high susceptibility to path blocking, a multiple number of WiGig APs should be installed to fully cover a typical target environment. Therefore, coordination among the installed APs is highly needed for enabling WiGig concurrent transmissions while overcoming packet collisions and reducing interference, which highly increases the total throughput of WiGig WLANs. In this paper, we propose a comprehensive architecture for coordinated WiGig WLANs. The proposed WiGig WLAN is based on a tight coordination between the 5 GHz (WiFi) and the 60 GHz (WiGig) unlicensed frequency bands. By which, the wide coverage WiFi band is used to do the signaling required for organizing WiGig concurrent data transmissions using control/user (C/U) plane splitting. To reduce interference to existing WiGig data links while doing BF, a novel location based BF mechanism is also proposed based on WiFi fingerprinting. The proposed coordinated WiGig WLAN highly outperforms conventional un-coordinated one in terms of total throughput, average packet delay and packet dropping rate.Comment: 6 pages, 8 Figures, IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC) 201

Acute vascular rejection after kidney transplantation outcome and effect of different therapeutic modalities

Background: Steroid resistant acute vascular rejection (AVR) is a great obstacle in successful renal transplantation (KTx). The aim of this work was to evaluate the outcome of histologically confirmed acute vascular rejection - which occurred in severe aggressive form in 39 patients following kidney transplantation as well as to study the outcome of therapy. These cases were chosen from 1000 renal allograft recipients who underwent kidney transplantation in the period between March, 1976 and April 1997 in Urology-Nephrology Center, Mansoura, Egypt.Methods: Statistical analysis of risk factors leading to AVR was carried out. The outcome of different rescue therapies used for AVR as well as graft survival functions were also analyzed.Results: Survival analysis for grafts with AVR revealed 60%, 53%, 30 %, 0% graft survival at 1, 2, 5, 10 yrs respectively after Tx. A statistically significant difference was found in comparison to patients who only experienced acute cellular rejection (90%, 84%, 71%, 46% graft survival at 1, 2, 5, 10 years post- KTx respectively) or patients who passed without rejection in their post-transplantation follow up (95%, 91.3%, 83.3%, 65.5% graft survival at 1, 2, 5, 10 yrs respectively). No statistically significant difference on the overall graft survival between the different modalities of therapy was noted. Steroid pulses + plasma exchange were given for 14 patients with AVR, whereas ATG, MAB ± plasma exchange were added to steroid resistant cases (25 patients). Logistic regression analysis of these data showed that prior blood transfusion, donor-recipient consanguinity, retransplantation are the most significant variables related to occurrence of AVR after kidney transplantation. At last follow up, 14 patients 35.9%) were living with functioning grafts, 16 patients (41%) were living on dialysis, 5 patients died with functioning grafts (12.8%) and 4 patients (10.25%) died with failed grafts.In conclusion: AVR remains a major obstacle for renal transplantation as it markedly impaired graft survival and responded poorly to therapy. Prior blood transfusion decreased the incidence of AVR whereas retransplantation and unrelated donation account significantly to the occurrence of AVR after renal Tx

Does therapeutic plasma exchange have a role in resistant cytokine storm state of COVID-19 infection?

Introduction: Among the main causes of mortality in COVID-19 patients is cytokine storm (CS) state. Few treatment options with variable efficacy results are available for its management. We aimed to illustrate the efficacy of Therapeutic Plasma Exchange (TPE) treatment in COVID-19 patients with resistant CS.Material and methods: This research is a prospective pilot study which included ten COVID-19 positive patients with CS state with no response after two doses of tocilizumab. Each patient received three to five TPE sessions according to his/her response. Respiratory status {oxygen (O2) requirements and data of mechanical ventilation} and laboratory markers (IL-6, CRP, ferritin, D dimer, LDH) were assessed before and after TPE. We reported mortality at 28 day of illness.Results: Six males and four females were enrolled in the study with a mean age of (52.9 years). Seven patients (70%) were on mechanical ventilation (MV). After TPE, oxygenation parameters and most laboratory markers improved significantly in all patients (p < 0.05). Four patients survived and were discharged (40%). One was on MV and three were not. The four patients had better hypoxic index (PaO2/FiO2 ratio) (˃100 vs <100), started TPE sooner after tocilizumab failure (2–3 vs 5–6 days), needed fewer TPE sessions (3 vs 4–5, p = 0.03), and less duration in ICU (6.5 vs 12.5 days) compared to those who did not benefit.Conclusions: In patients with CS state who did not respond well to tocilizumab and steroids, TPE could be a good option. Larger randomized clinical trials are needed to support its use.Clinical trials registration: ClinicalTrials.gov Identifier:NCT0445734