| 1. |
- Alabbasi, A., et al.
(författare)
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On Cascaded Federated Learning for Multi-tier Predictive Models
- 2021
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Ingår i: 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- The performance prediction of user equipment (UE) metrics has many applications in the 5G era and beyond. For instance, throughput prediction can improve carrier selection, adaptive video streaming's quality of experience (QoE), and traffic latency. Many studies suggest distributed learning algorithms (e.g., federated learning (FL)) for this purpose. However, in a multi-tier design, features are measured in different tiers, e.g., UE tier, and gNodeB (gNB) tier. On one hand, neglecting the measurements in one tier results in inaccurate predictions. On the other hand, transmitting the data from one tier to another improves the prediction performance at the expense of increasing network overhead and privacy risks. In this paper, we propose cascaded FL to enhance UE throughput prediction with minimum network footprint and privacy ramifications (if any). The idea is to introduce feedback to conventional FL, in multi-tier architectures. Although we use cascaded FL for UE prediction tasks, the idea is rather general and can be used for many prediction problems in multi-tier architectures, such as cellular networks. We evaluate the performance of cascaded FL by detailed and 3GPP compliant simulations of London's city center. Our simulations show that the proposed cascaded FL can achieve up to 54% improvement over conventional FL in the normalized gain, at the cost of 1.8 MB (without quantization) and no cost with quantization.
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| 2. |
- Amare, Azmeraw, et al.
(författare)
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Association of Polygenic Score and the involvement of Cholinergic and Glutamatergic Pathways with Lithium Treatment Response in Patients with Bipolar Disorder.
- 2023
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Ingår i: Research square. - : Research Square Platform LLC.
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Tidskriftsartikel (refereegranskat)abstract
- Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithium's possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N=2,367) and replicated in the combined PsyCourse (N=89) and BipoLife (N=102) studies. The associations of Li+PGS and lithium treatment response - defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P<����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������.
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| 3. |
- Amare, Azmeraw T, et al.
(författare)
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Association of polygenic score and the involvement of cholinergic and glutamatergic pathways with lithium treatment response in patients with bipolar disorder.
- 2023
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Ingår i: Molecular psychiatry. - 1476-5578. ; 28, s. 5251-5261
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Tidskriftsartikel (refereegranskat)abstract
- Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental healthdisorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithium's possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N=2367) and replicated in the combined PsyCourse (N=89) and BipoLife (N=102) studies. The associations of Li+PGS and lithium treatment response - defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P<0.05. Li+PGS was positively associated with lithium treatment response in the ConLi+Gen cohort, in both the categorical (P=9.8×10-12, R2=1.9%) and continuous (P=6.4×10-9, R2=2.6%) outcomes. Compared to bipolar patients in the 1st decile of the risk distribution, individuals in the 10th decile had 3.47-fold (95%CI: 2.22-5.47) higher odds of responding favorably to lithium. The results were replicated in the independent cohorts for the categorical treatment outcome (P=3.9×10-4, R2=0.9%), but not for the continuous outcome (P=0.13). Gene-based analyses revealed 36 candidate genes that are enriched in biological pathways controlled by glutamate and acetylcholine. Li+PGS may be useful in the development of pharmacogenomic testing strategies by enabling a classification of bipolar patients according to their response to treatment.
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| 4. |
- Cai, Tao, et al.
(författare)
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A TD-LTE prototype system with modules for general-purpose cognitive resource management and radio-environmental mapping
- 2011
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Ingår i: International Journal of Wireless Information Networks. - : Springer Science and Business Media LLC. - 1068-9605 .- 1572-8129. ; 18:3, s. 131-145
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Tidskriftsartikel (refereegranskat)abstract
- In this article we describe a demonstrator that shows how the cognitive resource manager (CRM) and the radio-environmental map (REM) can be efficiently implemented in full commercial grade cellular system (i.e., LTE system). The demonstrator shows how the modular CRM together with its open interface, the universal link-layer API (ULLA), facilitates the implementation of efficient radio resource management techniques guaranteeing the quality of service in the LTE system. The CRM, through ULLA, is able to obtain PHY/MAC status information of the link between the tested eNode B and the user equipment, and reconfigure link parameters. This measure-and-control by CRM/ULLA is independent of the underlying radio access technology, which shows the neutrality of CRM/ULLA towards PHY/MAC characteristics. The article also shows how the REM can be easily implemented in such system and how the REM provides the CRM with environmental information that enhances system management performance
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| 5. |
- Cai, Tao, et al.
(författare)
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An implementation of cognitive resource management on LTE platform
- 2010
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Ingår i: 2010 IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2010). - Piscataway, NJ : IEEE Communications Society. - 9781424480166 ; , s. 2663-2668
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Konferensbidrag (refereegranskat)abstract
- In this paper we describe an LTE based demonstrator of the Universal Link Layer API (ULLA) and Cognitive Resource Manager (CRM) modules that are developed in ARAGORN project. The demonstrated LTE system comprises one LTE TDD eNode B and one User Equipment (UE). We first introduce ULLA and CRM framework and then demonstrate their suitability to be implemented with the existing LTE equipments. We show how, through ULLA, CRM is able to obtain PHY/MAC status information of the link between the eNode B and UE, and in turn change system parameters to achieve better resource utilization and transmission efficiency. The control logic can be implemented with simple adaptation or policy-based intelligent methods. The platform clearly shows the feasibility to use ULLA/CRM architecture for radio resource management in a LTE network. It also shows the neutrality of ULLA/CRM mechanisms towards PHY/MAC characteristics of LTE technology platform; hence the platform is viable to flexibly switch between technology platforms (e.g. between LTE access and WiFi access) under the control of ULLA/CRM.
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| 6. |
- Ganjalizadeh, Milad, et al.
(författare)
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An RL-based Joint Diversity and Power Control Optimization for Reliable Factory Automation
- 2021
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Ingår i: 2021 IEEE Global Communications Conference (Globecom). - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- Communication systems supporting cyber-physical production applications should satisfy stringent delay and reliability requirements. Violation of these requirements may result in faulty behavior of the system and cause significant economic losses. Although wireless communications enable mobility and easy maintenance to industrial networks, it introduces many challenges to high-performance control systems due to interference and harsh environments (e.g., vibrations and many metallic objects). Diversity techniques and power control are powerful approaches to reduce latency and enhance reliability at the expense of excessive resource usage due to redundant transmissions. In this paper, we adopt fundamental metrics from reliability literature to wireless communications and provide critical indicators to measure reliability key performance indicators (KPIs) of cyber-physical systems. Then, we design a deep reinforcement learning orchestrator for power control and hybrid automatic repeat request retransmissions to optimize our reliability KPIs. Our orchestrator enables near real-time control and can be implemented on the edge cloud. We implement our framework on 3GPP compliant simulator on a factory automation scenario. Our comprehensive experiments show that, compared to the state-of-the-art, our solution can substantially improve the performance, especially for 5th percentile availability.
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| 7. |
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| 8. |
- Ganjalizadeh, Milad, et al.
(författare)
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Impact of correlated failures in 5G dual connectivity architectures for URLLC applications
- 2019
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Ingår i: Proceedings 2019 IEEE Globecom Workshops. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728109602
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Konferensbidrag (refereegranskat)abstract
- Achieving end-to-end ultra-reliability and resiliency in mission critical communications is a major challenge for future wireless networks. Dual connectivity has been proposed by 3GPP as one of the viable solutions to fulfill the reliability requirements. However, the potential correlation in failures occurring over different wireless links is commonly neglected in current network design approaches. In this paper, we investigate the impact of realistic correlation among different wireless links on end-to-end reliability for two selected architectures from 3GPP. In ultra-reliable use-cases, we show that even small values of correlation can increase the end-to-end error rate by orders of magnitude. This may suggest alternative feasible architecture designs and paves the way towards serving ultra-reliable communications in 5G networks.
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| 9. |
- Ganjalizadeh, Milad, et al.
(författare)
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Interplay between Distributed AI Workflow and URLLC
- 2022
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Ingår i: 2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 4208-4213
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Konferensbidrag (refereegranskat)abstract
- Distributed artificial intelligence (AI) has recently accomplished tremendous breakthroughs in various communication services, ranging from fault-tolerant factory automation to smart cities. When distributed learning is run over a set of wireless connected devices, random channel fluctuations, and the incumbent services simultaneously running on the same network affect the performance of distributed learning. In this paper, we investigate the interplay between distributed AI workflow and ultra-reliable low latency communication (URLLC) services running concurrently over a network. Using 3GPP compliant simulations in a factory automation use case, we show the impact of various distributed AI settings (e.g., model size and the number of participating devices) on the convergence time of distributed AI and the application layer performance of URLLC. Unless we leverage the existing 5G-NR quality of service handling mechanisms to separate the traffic from the two services, our simulation results show that the impact of distributed AI on the availability of the URLLC devices is significant. Moreover, with proper setting of distributed AI (e.g., proper user selection), we can substantially reduce network resource utilization, leading to lower latency for distributed AI and higher availability for the URLLC users. Our results provide important insights for future 6G and AI standardization.
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| 10. |
- Ganjalizadeh, Milad, et al.
(författare)
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Saving Energy and Spectrum in Enabling URLLC Services : A Scalable RL Solution
- 2023
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Ingår i: IEEE Transactions on Industrial Informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1551-3203 .- 1941-0050. ; , s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- Communication systems supporting cyber-physical production applications should satisfy stringent delay and reliability requirements. Diversity techniques and power control are the main approaches to reduce latency and enhance the reliability of wireless communications at the expense of redundant transmissions and excessive resource usage. Focusing on the application layer reliability key performance indicators (KPIs), we design a deep reinforcement learning orchestrator for power control and hybrid automatic repeat request retransmissions to optimize these KPIs. Furthermore, to address the scalability issue that emerges in the per-device orchestration problem, we develop a new branching soft actor-critic framework in which a separate branch represents the action space of each industrial device. Our orchestrator enables near real-time control and can be implemented in the edge cloud. We test our solution with a 3GPP-compliant and realistic simulator for factory automation scenarios. Compared to the state-of-the-art, our solution offers significant scalability gains in terms of computational time and memory requirements. Our extensive experiments show significant improvements in our target KPIs, over the state-of-the-art, especially for 5th percentile user availability. To achieve these targets, our framework requires substantially less total energy or spectrum, thanks to our scalable RL solution.
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