| 1. |
- Salehi, Fateme, et al.
(författare)
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Reliable Interference Prediction and Management with Time-Correlated Traffic for URLLC
- 2023
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Ingår i: GLOBECOM 2023 - 2023 IEEE Global Communications Conference. - : IEEE conference proceedings. - 9798350310900 ; , s. 6699-6704
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Konferensbidrag (refereegranskat)abstract
- In designing ultra-reliable low-latency communication (URLLC) services in 5G-and-beyond systems, link adaptation (LA) plays a vital role in adjusting transmission parameters under channel and interference dynamics. Without capturing such dynamics (e.g., relying on average estimates), the LA algorithms fail to simultaneously meet the strict reliability and latency bounds of mission-critical applications. To this end, this paper focuses on interference prediction-based adaptive resource allocation of one-shot URLLC transmission, wherein our solution deviates from the conventional average-based interference estimation schemes. We predict the next interference value based on the interference distribution estimation using a discrete-time Markov chain (DTMC). Further, to exploit the time correlation of each interference source, we model the correlated interference variations as a second-order DTMC to achieve higher prediction accuracy. While accounting for the risk sensitivity of interference estimates, the prediction outcome is then used for appropriate resource allocation of a URLLC transmission under link outage constraints. We evaluate the complete solution, given in the form of an algorithm, using Monte-Carlo simulations, and compare it with the first-order baseline counterpart. The analysis shows that the second-order interference estimate can fulfill the target outage as low as 10-7and improve the outage probability more than ten times in some scenarios compared to the baseline scheme while keeping the same amount of resource usage.
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| 2. |
- Akhtar, Muhammad Waseem, et al.
(författare)
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Partial NOMA for Semi-Integrated Sensing and Communication
- 2023
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Ingår i: 2023 IEEE Globecom Workshops (GC Wkshps). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350370218 ; , s. 1129-1134
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a novel partial non-orthogonal multiple access (P-NOMA)-based semi-integrated sensing and communication (ISaC) system design. As an example ISaC scenario, we consider a vehicle simultaneously receiving the communication signal from infrastructure-to-vehicle (I2V) and sensing signal from vehicle-to-vehicle (V2V). P-NOMA allows exploiting both the orthogonal multiple access (OMA) and NOMA schemes for interference reduction and spectral efficiency (SE) enhancement while providing the flexibility of controlling the overlap of the sensing and communication signals according to the channel conditions and priority of the sensing and communication tasks. In this respect, we derive the closed-form expressions for communication outage probability and sensing probability of detection in Nakagami-m fading by considering the interference from the composite sensing channel. Our extensive analysis allows capturing the performance trade-offs of the communication and the sensing tasks with respect to various system parameters such as overlapping partial NOMA parameter, target range, radar cross section (RCS), and parameter m of the Nakagami-m fading channel. Our results show that the proposed P-NOMA-based semi-ISaC system outperforms the benchmark OMA-and NOMA-based systems in terms of communication spectral efficiency and probability of detection for the sensing target.
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| 3. |
- Anjum, M., et al.
(författare)
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A Multi-Level ML-Based Optimization Framework for IIoT Networks with Distributed IRS Assisted UAVs
- 2023
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Ingår i: 2023 IEEE Globecom Workshops (GC Wkshps). - : IEEE conference proceedings. - 9798350370218 ; , s. 1338-1343
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Konferensbidrag (refereegranskat)abstract
- The development of the fifth generation (5G) of cellular systems enables the realization of densely connected, seamlessly integrated, and heterogeneous device networks. While 5G systems were developed to support the Internet of Everything (IoE) paradigm of communication, their mass-scale implementations have excessive capital deployment costs and severely detrimental environmental impacts. Hence, these systems are not feasibly scalable for the envisioned real-time, high-rate, high-reliability, and low-latency requirements of connected consumer, commercial, industrial, healthcare, and environmental processes of the IoE network. The IoE vision is expected to support 30 billion devices by 2030, hence, green communication architectures are critical for the development of next-generation wireless systems. In this context, intelligent reflecting surfaces (IRS) have emerged as a promising disruptive technological advancement that can adjust wireless environments in an energy-efficient manner. This work utilizes and analyzes a multi-node distributed IRS-assisted system in variable channel conditions and resource availability. We then employ machine learning and optimization algorithms for efficient resource allocation and system design of a distributed IRS-enabled industrial Internet of Things (IoT) network. The results show that the proposed data-driven solution is a promising optimization architecture for high-rate, next-generation IoE applications.
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| 4. |
- Basharat, Sarah, et al.
(författare)
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Effective Capacity Analysis of Delay-Constrained STAR-RIS Assisted BAC-NOMA Systems
- 2023
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Ingår i: IEEE International Conference on Communications. - : IEEE conference proceedings. - 9781538674628 ; , s. 5793-5798
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Konferensbidrag (refereegranskat)abstract
- Targeting the delay-constrained Internet-of-Things (IoT) applications in sixth-generation (6G) networks, in this paper, we study the integration of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) and non-orthogonal multiple access-based backscatter communication (BAC-NOMA) under statistical delay quality-of-service (QoS) requirements. In particular, we derive the closed-form expressions for the effective capacity of the STAR-RIS assisted BAC-NOMA system under Nakagami-m fading channels and energy-splitting protocol of STAR-RIS. Our simulation results demonstrate the effectiveness of STAR-RIS over the conventional RIS (C-RIS) and show an excellent correlation with analytical results, validating our analysis. The results reveal that the stringent QoS constraint degrades the effective capacity; however, the system performance can be improved by increasing the STAR-RIS elements and adjusting the energy-splitting coefficients. Finally, we determine the optimal pair of power reflection coefficients subject to the per-BSN effective capacity requirements.
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| 5. |
- Basharat, S., et al.
(författare)
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Ergodic Rate Analysis of RIS-Assisted BAC-NOMA Systems Under Nakagami-m Fading
- 2023
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Ingår i: GLOBECOM 2023 - 2023 IEEE Global Communications Conference. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350310900 ; , s. 2378-2383
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Konferensbidrag (refereegranskat)abstract
- In this paper, we investigate the reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access-based backscatter communication (BAC-NOMA) system under Nakagami-m fading channels and element-splitting protocol. To evaluate the system performance, we first approximate the composite channel gain, i.e., the product of the forward and backscatter channel gains, as a Gamma random variable via the central limit theorem (CLT) and method of moments (MoM). Then, by leveraging the obtained results, we derive the closed-form expressions for the ergodic rates of the strong and weak backscatter nodes (BNs). To provide further insights, we conduct the asymptotic analysis in the high signal-to-noise ratio (SNR) regime. Our numerical results show an excellent correlation with the simulation results, validating our analysis, and demonstrate that the desired system performance can be achieved by adjusting the power reflection and element-splitting coefficients. Moreover, the results reveal the significant performance gain of the RIS-assisted BAC-NOMA system over the conventional BAC-NOMA system.
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| 6. |
- Chmieliauskas, Darius, et al.
(författare)
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Q-Learning Inspired Method for Antenna Azimuth Selection in Cellular Networks
- 2023
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Ingår i: 2023 Workshop on Microwave Theory and Technology in Wireless Communications (MTTW). - : IEEE conference proceedings. - 9798350393491
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Konferensbidrag (refereegranskat)abstract
- Cellular networks are becoming increasingly complex, requiring careful optimization of parameters such as antenna propagation pattern, tilt, direction, height, and transmitted reference signal power to ensure a high-quality user experience. In this paper, we propose a new method to optimize antenna direction in a cellular network using Q-learning. Our approach involves utilizing the open-source quasi-deterministic radio channel generator to generate radio frequency (RF) power maps for various antenna configurations. We then implement a Q-learning algorithm to learn the optimal antenna directions that maximize the signal-to-interference-plus-noise ratio (SINR) across the coverage area. The learning process takes place in the constructed open-source OpenAI Gym environment associated with the antenna configuration. Our tests demonstrate that the proposed Q-learning-based method outperforms random exhaustive search methods and can effectively improve the performance of cellular networks while enhancing the quality of experience (QoE) for end users.
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| 7. |
- Fedullo, T., et al.
(författare)
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Exploiting Hybrid Medium Access Control and Relaying Strategies to Overcome Duty-Cycle Limitations in LoRa-Based Sensor Networks
- 2023
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Ingår i: 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781665453837
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Konferensbidrag (refereegranskat)abstract
- The industrial Internet-of-things (IIoT) paradigm is reshaping the way industrial measurement systems are designed. Industrial systems require collecting accurate and timely measurements from the field using smart sensor networks distributed in wide production areas. In this context, wireless connectivity of sensors acquires undeniable importance, and in turn, opens sig-nificant research challenges. Therefore, the research community is actively analyzing the suitability of different wireless technologies, for instance, Wi-Fi, 5G-and-beyond, and low-power wide-area networks (LPWANs), toward their possible industrial applications and optimizing them to realize high-performance and accurate smart measurement systems. In this paper, we focus on long range (LoRa)-based LPWANs (i.e., LoRaWAN), especially to overcome the duty cycle (DC) limitations of the adopted ALOHA-based medium access control (MAC) strategy in the industrial, scientific, and medical (ISM) bands. The ISM bands are subjected to an hourly constraint on the number of packet transmissions or inter-message delay, where the devices using higher spreading factors (SFs) can quickly consume the available transmission time. In this paper, we propose and assess the hybrid MAC designs in a LoRa network by combining carrier sense multiple access (CSMA) with ALOHA in two different ways i) exploiting different channel plans for the access mechanisms, ii) relay-assisted access, with devices using small SFs assisting neighboring higher-SF devices with listen-before-talk (LBT) mechanism. Our simulation results reveal that the proposed access strategies lead to a higher packet delivery rate (PDR) as well as lower mean and standard deviation of the communication delay; thus, increasing the overall measurement accuracy.
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| 8. |
- Khodakhah, Farnaz, et al.
(författare)
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NOMA or Puncturing for Uplink eMBB-URLLC Coexistence from an AoI Perspective?
- 2023
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Ingår i: GLOBECOM 2023 - 2023 IEEE Global Communications Conference. - : IEEE. - 9798350310900 ; , s. 4301-4306
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Konferensbidrag (refereegranskat)abstract
- Through the lens of the age-of-information (AoI) metric, this paper takes a fresh look into the performance of coexisting enhanced mobile broadband (eMBB) and ultra-reliable low-latency (URLLC) services in the uplink scenario. To reduce AoI, a URLLC user with stochastic packet arrivals has two options: orthogonal multiple access (OMA) with the preemption of the eMBB user (labeled as puncturing) or non-orthogonal multiple access (NOMA) with the ongoing eMBB transmission. Puncturing leads to lower average AoI at the expense of the decrease in the eMBB user's rate, as well as in signaling complexity. On the other hand, NOMA can provide a higher eMBB rate at the expense of URLLC packet loss due to interference and, thus, the degradation in AoI performance. We study under which conditions NOMA could provide an average AoI performance that is close to the one of the puncturing, while maintaining the gain in the data rate. To this end, we derive a closed-form expression for the average AoI and investigate conditions on the eMBB and URLLC distances from the base station at which the difference between the average AoI in NOMA and in puncturing is within some small gap β. Our results show that with β as small as 0.1 minislot, the eMBB rate in NOMA can be roughly 5 times higher than that of puncturing. Thus, by choosing an appropriate access scheme, both the favorable average AoI for URLLC users and the high data rate for eMBB users can be achieved.
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| 9. |
- Mahmood, Aamir, 1980-, et al.
(författare)
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Remote-Timber : An Outlook for Teleoperated Forestry With First 5G Measurements
- 2023
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Ingår i: IEEE Industrial Electronics Magazine. - : IEEE. - 1932-4529 .- 1941-0115. ; 17:3, s. 42-53
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Tidskriftsartikel (refereegranskat)abstract
- Across all industries, digitalization and automation are on the rise under the Industry 4.0 vision, and the forest industry is no exception. The forest industry depends on distributed flows of raw materials to the industry through various phases, wherein the typical workflow of timber loading and offloading is finding traction in using automation and 5G wireless networking technologies to enhance efficiency and reduce cost. This article presents one such ongoing effort in Sweden, Remote-Timber—demonstrating a 5G-connected teleoperation use-case within a workflow of timber terminal—and disseminates its business attractiveness as well as first measurement results on network performance. Also, it outlines the future needs of the 5G network design/optimization from teleoperation perspective. Overall, the motivation of this article is to disseminate our early-stage findings and reflections to the industrial and academic communities for furthering the research and development activities in enhancing 5G networks for verticals.
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| 10. |
- Minhaj, Syed Usama, et al.
(författare)
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Intelligent Resource Allocation in LoRaWAN Using Machine Learning Techniques
- 2023
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Ingår i: IEEE Access. - 2169-3536. ; 11, s. 10092-10106
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Tidskriftsartikel (refereegranskat)abstract
- With the ubiquitous growth of Internet-of-things (IoT) devices, current low-power wide-area network (LPWAN) technologies will inevitably face performance degradation due to congestion and interference. The rule-based approaches to assign and adapt the device parameters are insufficient in dynamic massive IoT scenarios. For example, the adaptive data rate (ADR) algorithm in LoRaWAN has been proven inefficient and outdated for large-scale IoT networks. Meanwhile, new solutions involving machine learning (ML) and reinforcement learning (RL) techniques are shown to be very effective in solving resource allocation in dense IoT networks. In this article, we propose a new concept of using two independent learning approaches for allocating spreading factor (SF) and transmission power to the devices using a combination of a decentralized and centralized approach. SF is allocated to the devices using RL for contextual bandit problem, while transmission power is assigned centrally by treating it as a supervised ML problem. We compare our approach with existing state-of-the-art algorithms, showing a significant improvement in both network level goodput and energy consumption, especially for large and highly congested networks.
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