Deep Reinforcement Learning based Handover Management for Millimeter Wave Communication

Mollel, Michael S.; Kaijage, Shubi; Kisangiri, Michael

doi:10.14569/ijacsa.2021.0120298

Cited by 12 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The per-block rate 𝑅 𝑢,𝑏 depends on Signal to Interference and Noise Ratio (SINR) and the bandwidth of one PRB (the bandwidth of micro BSs is larger than of macro because of mmWaves). We calculate 𝑅 𝑢,𝑏 using the Shannon's formula, as in [12]. Moreover, we guarantee a minimum required data rate 𝑟 𝑢 for every user 𝑢.…”

Section: Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Proportionally Fair Resource Allocation Considering Geometric Blockage Modeling for Improved Mobility Management in 5G

Prado

Gölitz

Mehmeti

et al. 2022

Proceedings of the 18th ACM International Symposium on QoS and Security for Wireless and Mobile Networks on 18th ACM Internatio

View full text Add to dashboard Cite

Line of Sight (LoS) blockages are a common occurrence in densely deployed cellular networks, as is the case with 5G. This leads to a significant deterioration in the signal quality on the user side. Modeling LoS blockages is crucial for simulations to obtain reliable results, but also challenging since LoS might appear and disappear occasionally because how often an LoS happens depends on the environment and the user speed. To capture LoS blockages in a realistic manner for a particular scenario in a given environment, we propose to model blockages geometrically by considering all static and mobile objects in the environment such as buildings, cars, busses and humans, including self-blockages from the user. This enables a better evaluation of the metrics of interest, such as handover rate. In dense network deployments, users make frequent handovers, which deteriorates their experience and reduces the network capacity. Also, operators should strive to provide fairness in resource allocation to all users as well as to guarantee a minimum Quality of Service (QoS). Thus, handover decisions should be considered jointly with resource allocation. To that end, in this paper, we formulate an optimization problem that provides proportional fair resource allocation, while simultaneously reducing the handover rate, and providing a minimum data rate for all users at all times. It is an integer non-linear program, which is NP-hard. We relax it to a linear problem, which allows us to find a near-optimal user-to-BS assignment and resource proportion for every user quickly. We compare the result from our optimal and relaxed approaches with other two benchmarks showing that it outperforms them considerably in terms of fairness, handover rate reduction and users' rate satisfaction. Moreover, our relaxed approach performs within above 90% of the optimum and reduces the handover rate up to 40%.

show abstract

Section: Problem Formulationmentioning

confidence: 99%

“…( 11)) ensures that a minimum required rate 𝑟 𝑢 for user 𝑢 is provided. Constraint (12) limits the number of handovers per slot in the network 1 . We allow only 𝑁 𝐻𝑂 users to make a handover by forcing 𝑁 𝑢 − 𝑁 𝐻𝑂 users per slot to stay connected to the same BSs as in the previous slot.…”

Section: Problem Formulationmentioning

confidence: 99%

Proportionally Fair Resource Allocation Considering Geometric Blockage Modeling for Improved Mobility Management in 5G

Prado

Gölitz

Mehmeti

et al. 2022

Proceedings of the 18th ACM International Symposium on QoS and Security for Wireless and Mobile Networks on 18th ACM Internatio

View full text Add to dashboard Cite

show abstract

“…In [116], the authors propose an offline RL algorithm to optimize Handover decisions. The model is able to decrease excess Handover up to 70% by studying the prolonged user's connectivity.…”

Section: Handovermentioning

confidence: 99%

Machine Learning in Beyond 5G/6G Networks—State-of-the-Art and Future Trends

et al. 2021

View full text Add to dashboard Cite

Artificial Intelligence (AI) and especially Machine Learning (ML) can play a very important role in realizing and optimizing 6G network applications. In this paper, we present a brief summary of ML methods, as well as an up-to-date review of ML approaches in 6G wireless communication systems. These methods include supervised, unsupervised and reinforcement techniques. Additionally, we discuss open issues in the field of ML for 6G networks and wireless communications in general, as well as some potential future trends to motivate further research into this area.

show abstract

“…Cell re-selection is the process of choosing a suitable cell after the UE camps on a cell and stays in the idle or inactive state. 4 Learn more about 5G mmWave Networks, Published on 20 Oct. 2020. Available online at https://www.5gmmwave.com/tag/mmwave/.…”

Section: Definition 4 (Cell Selection)mentioning

confidence: 99%

Improved handover decision scheme for 5g mm-wave communication: optimum base station selection using machine learning approach.

Mollel¹

View full text Add to dashboard Cite

The rapid growth in mobile and wireless devices has led to an exponential demand for data traf fic and exacerbated the burden on conventional wireless networks. Fifth generation (5G) and beyond networks are expected to not only accommodate this growth in data demand but also provide additional services beyond the capability of existing wireless networks, while main taining a high quality-of-experience (QoE) for users. The need for several orders of magnitude increase in system capacity has necessitated the use of millimetre wave (mm-wave) frequencies as well as the proliferation of low-power small cells overlaying the existing macro-cell layer. These approaches offer a potential increase in throughput in magnitudes of several gigabits per second and a reduction in transmission latency, but they also present new challenges. For exam ple, mm-wave frequencies have higher propagation losses and a limited coverage area, thereby escalating mobility challenges such as more frequent handovers (HOs). In addition, the ad vent of low-power small cells with smaller footprints also causes signal fluctuations across the network, resulting in repeated HOs (ping-pong) from one small cell (SC) to another. Therefore, efficient HO management is very critical in future cellular networks since frequent HOs pose multiple threats to the quality-of-service (QoS), such as a reduction in the system throughput as well as service interruptions, which results in a poor QoE for the user. How ever, HO management is a significant challenge in 5G networks due to the use of mm-wave frequencies which have much smaller footprints. To address these challenges, this work in vestigates the HO performance of 5G mm-wave networks and proposes a novel method for achieving seamless user mobility in dense networks. The proposed model is based on a double deep reinforcement learning (DDRL) algorithm. To test the performance of the model, a com parative study was made between the proposed approach and benchmark solutions, including a benchmark developed as part of this thesis. The evaluation metrics considered include system throughput, execution time, ping-pong, and the scalability of the solutions. The results reveal that the developed DDRL-based solution vastly outperforms not only conventional methods but also other machine-learning-based benchmark techniques. The main contribution of this thesis is to provide an intelligent framework for mobility man agement in the connected state (i.e HO management) in 5G. Though primarily developed for mm-wave links between UEs and BSs in ultra-dense heterogeneous networks (UDHNs), the proposed framework can also be applied to sub-6 GHz frequencies.

show abstract

Deep Reinforcement Learning based Handover Management for Millimeter Wave Communication

Cited by 12 publications

References 20 publications

Proportionally Fair Resource Allocation Considering Geometric Blockage Modeling for Improved Mobility Management in 5G

Proportionally Fair Resource Allocation Considering Geometric Blockage Modeling for Improved Mobility Management in 5G

Machine Learning in Beyond 5G/6G Networks—State-of-the-Art and Future Trends

Improved handover decision scheme for 5g mm-wave communication: optimum base station selection using machine learning approach.

Contact Info

Product

Resources

About