Number Of Physical Resource Blocks Research Articles

Reinforcement Learning Approach for Adaptive C-V2X Resource Management

The modulation coding scheme (MCS) index is the essential configuration parameter in cellular vehicle-to-everything (C-V2X) communication. As referenced by the 3rd Generation Partnership Project (3GPP), the MCS index will dictate the transport block size (TBS) index, which will affect the size of transport blocks and the number of physical resource blocks. These numbers are crucial in the C-V2X resource management since it is also bound to the transmission power used in the system. To the authors’ knowledge, this particular area of research has not been previously investigated. Ultimately, this research establishes the fundamental principles for future studies seeking to use the MCS adaptability in many contexts. In this work, we proposed the application of the reinforcement learning (RL) algorithm, as we used the Q-learning approach to adaptively change the MCS index according to the current environmental states. The simulation results showed that our proposed RL approach outperformed the static MCS index and was able to attain stability in a short number of events.

Load balancing in 5G heterogeneous networks based on automatic weight function

Load balancing is a major challenge in heterogeneous networks (HetNets) consisting of 5G and 6G ultra-dense small cells with long-term evaluation advanced (LTE-A) networks. A key factor in achieving efficient load balancing during user mobility is creating appropriate optimisation for handover control parameters (HCP). This paper proposes a coordinated load balancing algorithm for LTE-A/fifth generation (5G) HetNets. The algorithm automatically optimises HCP settings for a given user based on three bounded functions (the signal-to-interference-plus-noise ratio (SINR) of the user equipment (UE), the number of physical resource blocks (PRBs) per UE and the UE’s speed) as well as their automatic weight levels. A two-step target cell determination strategy is implemented according to the cell load level and RSRP criteria, ensuring that users are handed over to low-loaded target cells. A new HO procedure that considers the pilot signal power is also proposed, which includes the number of PRBs per UE and the RSRP. Cells with freer PRBs are prioritised in user association to provide load balance and enhanced throughput. The proposed load balancing algorithm is compared with five other load balancing algorithms selected from the literature. The simulation results reveal that under various mobile speed scenarios, the proposed load balancing scheme enhances network performance in terms of load level, throughput, spectral efficiency and call dropping ratio (CDR).

SmartCon: Deep Probabilistic Learning-Based Intelligent Link-Configuration in Narrowband-IoT Toward 5G and B5G

To enhance the coverage and transmission reliability, repetitions adopted by Narrowband Internet of Things (NB-IoT) allow repeating transmissions several times. However, this results in a waste of radio resources when the signal strength is high. In addition, in low signal quality, the selection of a higher modulation and coding scheme (MCS) level leads to a huge packet loss in the network. Moreover, the number of physical resource blocks (PRBs) per-user needs to be chosen dynamically, such that the utilization of radio resources can be improved on per-user basis. Therefore, in NB-IoT systems, dynamic adaptation of repetitions, MCS, and radio resources, known as auto link-configuration, is crucial. Accordingly, in this paper, we propose SmartCon which is a Generative Adversarial Network (GAN)-based deep learning approach for auto link-configuration during uplink or downlink scheduling, such that the packet loss rate is significantly reduced in NB-IoT networks. For the training purpose of the GAN, we use a Multi-Armed Bandit (MAB)-based reinforcement learning mechanism that intelligently tunes its output depending on the present network condition. The performance of SmartCon is thoroughly evaluated through simulations where it is shown to significantly improve the performance of NB-IoT systems compared to baseline schemes.

Machine Learning for Hidden Nodes Detection in Unlicensed LTE Networks

This paper analyzes and evaluates the use of Machine Learning (ML) techniques as an alternative to heuristic methods to identify UEs in Licensed-Assisted Access (LAA) networks that are being affected by collisions from hidden nodes sources. The paper analyzes different models based on supervised ML methods to classify UEs into two possible classes: UEs affected by collisions and UEs free of collisions. To implement them, we employ a set of typical parameters of the LTE standard, e.g., Channel Quality Indicator (CQI), Reference Signal Received Quality (RSRQ) and number of Physical Resource Blocks (PRB). These parameters are fed into different supervised ML models which, once optimized, obtain the best models to predict when an UE is facing interference from hidden nodes in a dynamic channel environment. Results show that classification methods based on ML techniques are capable of predicting with a high level of accuracy when a UE is affected by collisions and, consequently, if this node is located in a hidden area. Nevertheless, and despite the growing interest in applying ML techniques in an increasing number of fields, we can conclude that, in this case, the performance of the ML approach is not necessarily better than heuristic methods. For instance, it does not outperform the Dynamic Collision Detection (DCD) previously presented by the authors. In addition, the ML algorithms exhibit a higher prediction time and computational complexity, with respect to those obtained with DCD algorithm. This should be considered a drawback in some LTE-Advanced and 5G wireless network scenarios that prioritize lower latencies.

The Impact of Efficient Transport Blocks Management on the Downlink Power in MIMO Spatial Multiplexing of LTE-A

Radio spectrum for mobile operators is an expensive resource that should be wisely provisioned to meet users’ demands. In long term evolution-advanced, the number of physical resource blocks (PRBs) are used to gauge the users’ needs. Accordingly, there is a time in the network when it is beneficial to gain the advantages of consuming the unused PRBs in the transport block (TB) context to operate at a significantly lower power cost per PRB. The novelty of the letter is that the $3\text{rd}$ generation partnership project (3GPP) standard tables are used with multiple-input-multiple-output (MIMO) spatial multiplexing mode, to control the transport block size (TBS) index. This can be done by setting more PRBs and hence changing the modulation order that leads to reducing the transmitted power. In addition to that, MIMO layers, the prerequisite throughput, the intercell interference as well as the channel conditions of each user are considered as constraints to improve the downlink power efficiency. To describe the abovestated problem, an optimization problem is formulated as binary integer nonlinear program, which is very difficult to solve. Thus, a reformulation linearization technique based on a branch-and-bound framework is used to simplify the aforementioned BINLP.

Power headroom report-based uplink power control in 3GPP LTE-A HetNet

In a 3rd Generation Partnership Project Long Term Evolution-Advanced (3GPP LTE-A) uplink, user equipment (UE) has a maximum transmission power limit defined by the UE power class. Generally, the cell edge UE has a higher probability to be constrained by the maximum transmission power level owing to the compensation of the large pathloss. When the UE transmission power is constrained by the maximum level, allocating a higher number of physical resource blocks (PRBs) than the UE power capability can afford will reduce the transmission power to be allocated per PRB, resulting in inefficient use of power resources. To avoid this power inefficiency, the uplink transmission power can be controlled according to the number of PRBs allocated using the power headroom report-based power efficient resource allocation (PHR-PERA) scheme proposed in this paper. Furthermore, adaptive open-loop power control (OL-PC) based on the signal-to-interference-plus-noise ratio (SINR) and the uplink interference is used to improve the cell capacity. By the uplink power control employing the proposed PHR-PERA scheme, the macro and femto UE throughputs were increased by 49.9 and 5 %, respectively, compared to the case of conventional fractional power control (FPC). Additional gains of 21.9 and 4.8 % for macro and femto UE throughputs, respectively, were achieved by adaptive OL-PC. The performance of fast closed-loop power control (CL-PC) based on the received SINR is also evaluated in this paper. The simulation results demonstrate that CL-PC supports OL-PC by compensating the fading effect for the UE uplink SINR to meet the target SINR.

CoPoMo: a context‐aware power consumption model for LTE user equipment

ABSTRACTIncreasing the battery lifetime of power‐hungry mobile devices has become a major research target for mobile operators. Based on extensive measurement campaigns with the most recent Long Term Evolution (LTE) devices, we introduce a new Markovian power consumption model, which takes into account the chosen system parameters (such as the number of physical resource blocks) as well as the context of a user in terms of radio channel conditions and service characteristics (non‐real‐time vs. real time). One key advancement of this generic model is its stochastic nature, which allows for determining the average power consumption of a device based on usage profiles including location information and service statistics. We have conducted comprehensive system simulations using realistic channel characteristics derived from ray‐tracing analyses and validated the new model. Finally, we show that the proposed context‐aware power consumption model enables quantitative analyses of the trade‐off between network resource allocation and enhanced battery lifetime. © 2013 The Authors. Transactions on Emerging Telecommunications Technologies published by John Wiley & Sons, Ltd.