Non-uniform User Distribution Research Articles

Coded Caching in Satellite Networks With Non-Uniform User Distribution

Coded Caching in Satellite Networks With Non-Uniform User Distribution

Coded Caching in Hierarchical Cache-Aided Networks With Nonuniform User Distribution

Coded Caching in Hierarchical Cache-Aided Networks With Nonuniform User Distribution

Improved Spread Spectrum Aloha Protocol and Beam-Hopping Approach for Return Channel in Satellite Internet of Things.

This paper examines potential performances of the Spread Spectrum-based random access technique and proposes an Improved Spread Spectrum Aloha (ISSA) protocol for the return channel in satellite Internet of Things (IoT) based on the beam-hopping technique. The key design driver and detailed solution of ISSA protocol are presented in this work and it is shown that the proposed protocol achieves high throughput and low collision probability. To match user/traffic distribution, delay requirement and channel condition with beam allocation better, a low-complexity heuristic beam scheduling algorithm and a more effective Maximum-Weighted Clique (MWC) algorithm have been proposed. The heuristic algorithm considers the user/traffic distribution, inter-beam interference, and fairness primarily. However, the MWC algorithm gives considerations not only on above factors, but also on delay requirement and channel condition (path loss and rain attenuation) to maximize system capacity. The beam angle and interference avoidance threshold are proposed to measure the inter-beam interference, and the link propagation loss and rain attenuation are considered meanwhile in the channel condition. In the MWC algorithm, we construct an auxiliary graph to find the maximum-weighted clique and derive the weighting approach to be applied in different application scenarios. The performance evaluation of our ISSA protocol compared with the SSA protocol is presented, which achieves a gain of 16.7%. The simulation of the ISSA protocol combined with round robin, heuristic, and MWC beam scheduling for the return link in beam-hopping satellite IoTs is also provided. The results indicate that the throughput in nonuniform user distribution is much lower than in the uniform case without the beam scheduling algorithm. Through the application of the scheduling algorithm, the throughput performance can approach the uniform distribution. Finally, the degree of user satisfaction with different scheduling approaches is presented, which validates the effectiveness of heuristic and MWC algorithms.

Deep Reinforcement Learning Evolution Algorithm for Dynamic Antenna Control in Multi-Cell Configuration HAPS System

In this paper, we propose a novel Deep Reinforcement Learning Evolution Algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random movement of the HAPS caused by the winds, the throughput of the users might decrease. Therefore, we propose a method that can dynamically adjust the antenna parameters based on the throughput of the users in the coverage area to reduce the number of low-throughput users by improving the users’ throughput. Different from other model-based reinforcement learning methods, such as the Deep Q Network (DQN), the proposed method combines the Evolution Algorithm (EA) with Reinforcement Learning (RL) to avoid the sub-optimal solutions in each state. Moreover, we consider non-uniform user distribution scenarios, which are common in the real world, rather than ideal uniform user distribution scenarios. To evaluate the proposed method, we do the simulations under four different real user distribution scenarios and compare the proposed method with the conventional EA and RL methods. The simulation results show that the proposed method effectively reduces the number of low throughput users after the HAPS moves.

Development of a Ka-Band Non-Regular Multibeam Coverage Antenna

This article describes the development of a novel Ka-band array fed reflector antenna based on a passive multifeed-per-beam (MFPB) beamforming network (BFN) to realize multiple shaped beams for broadband satellite communications applications. The feeding array is organized in non-regular overlapped sub-arrays (OSAs) in order to generate contiguous shaped beams in two polarizations with a single reflector. The main novelty is the generation of the non-regular multibeam coverage with differently shaped beams which equalizes the traffic demand per beam as generated by a non-uniform distribution of users over the satellite coverage. The entire feeding array is organized in two interleaved and partially overlapped groups of sub-arrays, one per polarization, to better satisfy the beam overlap requirements and isolation between co-polarized beams. A complex BFN has been developed as a monolithic component through a 3-D additive manufacturing (AM) technique. The test campaign demonstrated the suitability of the design approach and the accuracy of the selected manufacturing technique for the Ka-band frequencies, with a good correlation between measured and simulated results.

Voronoi-Based UAV Flight Method for Non-Uniform User Distribution in Delay-Tolerant Aerial Networks

This paper considers an emergency communication system controlling multiple unmanned aerial vehicles (UAVs) in the sky over a large-scale disaster-affected area. This system is based on delay-tolerant networking, and information from ground users is relayed by the UAVs through wireless transmission and the movement of UAVs in a store-and-forward manner. Each UAV moves autonomously according to a predetermined flight method, which uses the positions of other UAVs through communication. In this paper, we propose a new method for UAV flight considering the non-uniformity of user distributions. The method is based on the Voronoi cell using the predicted locations of other UAVs. We evaluate the performance of the proposed method through computer simulations with a non-uniform user distribution generated by a general cluster point process. The simulation results demonstrate the effectiveness of the proposed method.

A Novel Load Balancing Scheme for Satellite IoT Networks Based on Spatial-Temporal Distribution of Users and Advanced Genetic Algorithms.

Satellite IoT networks (S-IoT-N), which have been a hot issue regarding the next generation of communication, are quite important for the coming era of digital twins and the metaverse because of their performance in sensing and monitoring anywhere, anytime, and anyway, in more dimensions. However, this will cause communication links to face greater traffic loads. Satellite internet networks (SIN) are considered the most possible evolution road, possessing characteristics of many satellites, such as low earth orbit (LEO), the Ku/Ka frequency, and a high data rate. Existing research on load balancing schemes for satellite networks cannot solve the problems of low efficiency under conditions of extremely non-uniform distribution of users (DoU) and dynamic density variances. Therefore, this paper proposes a novel load balancing scheme of adjacent beams for S-IoT-N based on the modeling of spatial–temporal DoU and advanced GA. In our scheme, the PDF of the DoU in the direction of movement of the SSP’s trajectory was modeled first, which provided a multi-directional constraint for the non-uniform distribution of users in S-IoT-N. Fully considering the prior periodicity of satellite movement and the similarity of DoU in different areas, we proposed an adaptive inheritance iteration to optimize the crossover factor and mutation factor for GA for the first time. Based on the proposed improved GA, we obtained the optimal scheme of load balancing under the conditions of the adaptation from the local balancing scheme to global balancing, and a selection of Ser-Beams to access. Finally, the simulations show that the proposed method can improve the average throughput by 3% under specific conditions and improve processing efficiency by 30% on average.

NFV/SDN as an Enabler for Dynamic Placement Method of mmWave Embedded UAV Access Base Stations

In the event of a major disaster, base stations in the disaster area will cease to function, making it impossible to obtain life-saving information. Therefore, it is necessary to provide a wireless communication infrastructure as soon as possible. To cope with this situation, we focus on NFV/SDN (Network Function Virtualization/Software-Defined Networking)-enabled UAVs equipped with a wireless communication infrastructure to provide services. The access link between the UAV and the user is assumed to be equipped with a millimeter-wave interface to achieve high throughput. However, the use of millimeter-waves increases the effect of attenuation, making the deployment of UAVs problematic. In addition, if multiple UAVs are deployed in a limited frequency band, co-channel interference will occur between the UAVs, resulting in a decrease in the data rate. Therefore, in this paper, we propose a method that combines UAV placement and frequency division for a non-uniform user distribution in an environment with multiple UAVs. As a result, it is found that the offered data rate is improved by using our specific placement method, in terms of not only the average but also the outage user rate.

An Improved Modelling of User Clustering for Small Cell Deployment in Heterogeneous Cellular Network

Users in practical cellular geographical areas are found to be non-uniformly distributed. Small cell (SC) deployments in heterogeneous user distribution in a cellular geographical area help to meet high data rate user demands for multimedia data communications in hot spots. SCs help to offload traffic burden from the macro cell (MC) base station, and also cater the data traffic need for the edge users where signal strength from the MC base station (BS) is very weak. For deployments of SCs along with the central MC BS (hence called HetNet) in such spatial heterogeneous user distribution, effective user grouping or clustering algorithm is required for appropriate and satisfactory service coverage. We call it service grouping or clustering of users to be put under a SC for data transmission and reception. It does not disturb the spatial positions of users in clustered non-uniform distribution. Efficient grouping or clustering of users and then deploying a SC at optimal location enhances the performance of the HetNet. It is found that the K-means algorithm used for such grouping of users to position SCs is not efficient. A novel and improved user grouping algorithm is proposed in this paper which performs much better compared to the k-means algorithm. The proposed algorithm of modelling of user clustering results in increase in the number of users under SCs, increase in more offloading of data traffic from MC BS thereby increasing data throughput of MC users. The algorithm also increases in the energy efficiencies of the SCs which is considered as one important performance metric. A doubly stochastic poison process (DSPP), also called Cox process, is assumed here for simulation of non-uniform user distributions. We consider Rayleigh distributed small scale fading model, large scale fading factor representing shadow fading, and users’ geographical distances from BSs to evaluate users’ data rates.

Cooperative Multi-Agent Deep Reinforcement Learning for Resource Management in Full Flexible VHTS Systems

Very high throughput satellite (VHTS) systems are expected to have a huge increase in traffic demand in the near future. Nevertheless, this increase will not be uniform over the entire service area due to the non-uniform distribution of users and changes in traffic demand during the day. This problem is addressed by using flexible payload architectures, which allow the allocation of payload resources flexibly to meet the traffic demand of each beam, leading to dynamic resource management (DRM) approaches. However, DRM adds significant complexity to VHTS systems, so in this paper we discuss the use of one reinforcement learning (RL) algorithm and two deep reinforcement learning (DRL) algorithms to manage the resources available in flexible payload architectures for DRM. These algorithms are Q-Learning (QL), Deep Q-Learning (DQL) and Double Deep Q-Learning (DDQL) which are compared based on their performance, complexity and added latency. On the other hand, this work demonstrates the superiority a cooperative multiagent (CMA) decentralized distribution has over a single agent (SA).

Two-Step Dynamic Cell Optimization Algorithm for HAPS Mobile Communications

High altitude platform stations are attracting much attention as novel mobile communication platforms for ultra-wide coverage areas and disaster-resilient networks. Multi-cell configurations can increase communication capacity in a wide area. Conventional work optimizes the cell configuration for a multi-cell configuration based on a genetic algorithm (GA). This method identifies an optimal cell configuration in terms of spectral efficiency depending on the number of cells under a uniform user distribution. However, user distributions differ depending on location; thus, cell configuration optimization is also required for non-uniform user distributions. The problem is an increased number of parameters because each cell requires different antenna parameters for non-user distributions compared to a uniform user distribution. This increases the difficulty of optimization, even when using a GA in some cases. Therefore, We propose a GA-based two-step cell optimization algorithm that comprises both search space reduction and antenna parameter optimization steps to address this problem. The proposed method employs the concept of co-evolution, i.e., a divide-and-conquer method. The proposed method divides multiple cells into several groups to reduce the number of optimized parameters and optimizes each group in order. In addition, the search space is reduced based on a marginal histogram obtained via optimization with a large step size. Simulation results demonstrate that this technique can reduce the number of combinations compared to the case without sub-area division. In addition, there are cases where the search space reduction further reduces the number of combinations without degrading the sum of the square root of throughput.

Energy Efficient Optimal Resource Allocation in Multi-RAT Heterogeneous Network

ABSTRACT In this paper, we address the frequent problem associated with user association and resource allocation along with optimal deployment of base station (BS) in multiple radio access technology (Multi-RAT)-assisted heterogeneous network (Het-Net). Considering real time user scenarios, optimal resource allocation in Het-Net while ensuring each user’s minimum required data rate is a challenging task to be performed. Here, we propose a novel algorithm with a well-known and efficient meta-heuristic optimization technique to resolve the aforementioned problem. We use hybrid memory-based dragonfly algorithm with differential evolution (DADE) for its excellent convergence characteristics. Extensive simulations are performed to determine the optimal network utility under the consideration of nonuniform user distribution and fine-tuning their respective service class and contract of association parameters. Simulation results depict that the proposed algorithm improves the overall network utility in terms of radio resource utilization and energy consumption while satisfying the user demands. Comparative analysis of the proposed technique with the other state-of-the-art algorithm depicts the superiority of the proposed algorithm in terms of accuracy and consistency. We also perform optimal multi-RAT cell planning under the above constraints including a network blackout scenario. The algorithm ensures each user coverage by optimally allocating the available resources.

Open-Loop Communications for Up-Link URLLC Under Clustered User Distribution

To achieve ultra-low latency for ultra-reliable and low latency communications (URLLC) in fifth-generation (5G) systems, open-loop communications (OLC), which removes the time-consuming feedback procedure, has been proposed as a promising technique, especially for uplink transmission (UL). The performance of the OLC has been commonly analysed under a uniform user distribution model. Since the users are commonly clustered around highly populated areas, a clustered or non-uniform user distribution model is more realistic. However, the performance analysis under a clustered user distribution model is not trivial. In this paper, we therefore aim to analyse the OLC network performance under a clustered user distribution. We also investigate how to achieve URLLC by jointly employing the OLC mode, short packet transmission, multi-cell association and diversity schemes such as selection combining (SC) and maximum ratio combining (MRC). We provide a tractable approach to analyse the UL reliability and its approximated closed form. The simulation results show that the derived closed form can provide a reasonable approximation for the UL reliability when the SC algorithm is employed. Moreover, for the case of MRC, the analytical results can provide a tight approximation on the uplink reliability when the number of the associated BSs is lower than 2, while serving as a corresponding upper bound when the number of the associated BSs is larger than 2.

LEO Satellite Channel Allocation Scheme Based on Reinforcement Learning

Delay, cost, and loss are low in Low Earth Orbit (LEO) satellite networks, which play a pivotal role in channel allocation in global mobile communication system. Due to nonuniform distribution of users, the existing channel allocation schemes cannot adapt to load differences between beams. On the basis of the satellite resource pool, this paper proposes a network architecture of LEO satellite that utilizes a centralized resource pool and designs a combination allocation of fixed channel preallocation and dynamic channel scheduling. The dynamic channel scheduling can allocate or recycle free channels according to service requirements. The Q-Learning algorithm in reinforcement learning meets channel requirements between beams. Furthermore, the exponential gradient descent and information intensity updating accelerate the convergence speed of the Q-Learning algorithm. The simulation results show that the proposed scheme improves the system supply-demand ratio by 14%, compared with the fixed channel allocation (FCA) scheme and by 18%, compared with the Lagrange algorithm channel allocation (LACA) scheme. The results also demonstrate that our allocation scheme can exploit channel resources effectively.

Convolutional Neural Networks for Flexible Payload Management in VHTS Systems

Very high throughput satellite (VHTS) systems are expected to have a large increase in traffic demand in the near future. However, this increase will not be uniform throughout the service area due to the nonuniform user distribution, and the changing traffic demand during the day. This problem is addressed using flexible payload architectures, enabling the allocation of the payload resources in a flexible manner to meet traffic demand of each beam, leading to dynamic resource management (DRM) approaches. However, DRM adds significant complexity to the VHTS systems, which is why in this article, we are analyzing the use of convolutional neural networks (CNNs) to manage the resources available in flexible payload architectures for DRM. The VHTS system model is first outlined, for introducing the DRM problem statement and the CNN-based solution. A comparison between different payload architectures is performed in terms of DRM response, and the CNN algorithm performance is compared by three other algorithms, previously suggested in the literature to demonstrate the effectiveness of the suggested approach and to examine all the challenges involved.

Optimal strategies for floating anchored information with partial infrastructure support

Floating Content (FC) is a communication paradigm to locally share ephemeral content without direct support from infrastructure. It is based on constraining the opportunistic replication of content in a way that strikes a balance between minimizing resource usage and maximizing content availability among the intended recipients. However, existing approaches to management of FC schemes are unfit for realistic scenarios with non-uniform user distributions, resulting in heavy overdimensioning of resources allocated to FC. In this work, we propose a new version of FC, called Cellular Floating Content (CFC), which optimizes the use of bandwidth and memory by adapting the content replication and storage strategies to the spatial distribution of users, and to their mobility patterns. The main idea underlying our approach is to partition users into small “local communities”, and to optimally weight their contributions to the FC paradigm according to their specific mobility features, and to the resources required to achieve a target performance level. We characterize numerically the properties of the optimal strategies in a variety of mobility patterns and traffic conditions, showing the accuracy of our approach, and the significant savings it enables in the amount of resources necessary to run FC, which in a realistic setup can be as high as 27% with respect to traditional FC dimensioning strategies.

Intelligent User-Centric Networks: Learning-Based Downlink CoMP Region Breathing

In the presence of irregular transmission/reception point (TRP) topologies and non-uniform user distribution, the user-to-node association optimization is a rather challenging process in real user-centric networks, especially for the joint transmission aided coordinated multipoint (CoMP) technique. The grade of challenge further escalates, when taking the dynamic user scheduling process into account in order to enhance the system capacity attained. To tackle the above-mentioned problem, we holistically optimize the system by conceiving joint user scheduling and user-to-node association. Then, for the sake of striking a significantly better balance between the network capacity and coverage quality, we propose a generalized reinforcement learning assisted framework intrinsically amalgamated both with neural-fitted Q-iteration as well as with ensemble learning and transfer learning techniques. Consequently, a powerful policy can be found for dynamically adjusting the set of TRPs participating in the joint transmission, thus allowing the CoMP-region to breathe, depending on both the temporal and geographical distribution of the tele-traffic load across the network. To facilitate the prompt learning of the global policy supporting flexible scalability, the overall network optimization process is decoupled into multiple local optimization phases associated with a number of TRP clusters relying on iterative information exchange among them. Our simulation results show that the proposed scheme is capable of producing a policy achieving a network-edge throughput gain of up to 140%$ and a network capacity gain of up to 190% under the challenging scenario of having a non-uniform geographical UE distribution and bursty traffic.

NOMA performance enhancement-based imperfect SIC minimization using a novel user pairing scenario involving three users in each pair

Due to the non-uniform distribution of users in a cellular area, there are users with similar channel conditions. In non-orthogonal multiple access (NOMA) systems, a small difference in channel gains of paired users can cause interference in end users, which so-called imperfect successive interference cancellation (SIC) and correspondingly, it causes serious degradation in the system capacity. In NOMA, most of the user pairing algorithms focus on pairing near and far users with each other, and about mid-users there are two options. First, pairing these users together over a common frequency band, which makes some degradation in capacity, due to imperfect SIC. Second, unpairing these users together, which makes mid-users cannot enjoy the benefits provided by the NOMA strategy. In this article to address these issues, a novel user pairing approach is proposed where all far, near, and mid-cell users can pair together based on the interference mitigation, and enjoy the advantages of NOMA. To evaluate the performance of this scenario, the ergodic sum capacity is calculated in both perfect and imperfect SIC modes, and also compared with the benchmark scheme. In addition, we provide a performance analysis of the proposed scheme in terms of user fairness and outage probability, and also investigate the effect of the resources (power/bandwidth) distribution among users on the system performance. Analytical computations and corresponding simulation results, confirm the performance enhancement of the proposed scheme in terms of the system capacity, outage probability, and user fairness.

Reinforcement Learning Based Load Balancing for Hybrid LiFi WiFi Networks

Light fidelity (LiFi) is an emerging communication technology, which utilizes the light-emitting diodes (LEDs) for high-speed wireless communications. Due to its huge unlicensed bandwidth, LiFi is capable of supporting high data rates. The quality of the LiFi channel fluctuates across the room due to interference, reflection from walls or blockage. On the other hand, WiFi is another wireless communication technology that is capable of providing moderate data rates with ubiquitous coverage. As the electromagnetic spectrum of LiFi does not overlap with WiFi, both of them can coexist to form a hybrid LiFi and WiFi network for seamless and high-throughput connectivity. The performance of a hybrid system significantly depends upon the access point (AP) assignment and resource allocation strategies. In this paper, a downlink hybrid system with one WiFi AP and four LiFi APs is considered, and a reinforcement learning (RL) algorithm is implemented in order to determine an optimal AP assignment strategy, which maximizes the long-term system throughput while ensuring the required users fairness and satisfaction. Furthermore, two different scenarios based on the random waypoint model with uniform and non-uniform distribution of users have been studied. The performance of the proposed system is compared against state-of-the-art benchmark approaches e.g., signal strength strategy (SSS), exhaustive search, and an iterative optimization method. The results are reported in terms of the average system throughput, user satisfaction, fairness, and capacity outage probability. It is shown that the proposed RL method performs closer to the exhaustive search scheme at fairly low complexity. The RL method also outperforms the SSS scheme and the iterative algorithm in most scenarios.

Uplink Performance Improvement for Downlink-Uplink Decoupled HetNets with Non-uniform User Distribution

In the heterogeneous networks (HetNets), the downlink-uplink decoupled (DUDE) user association is a very promising technology that can employ different association strategies for uplink and downlink. The DUDE mode has great potential for improving system capacity and frequency efficiency. However, interference between the DUDE user equipment (DUDE UE), macro user equipment (MUE) and small user equipment (SUE) still needs to be considered and properly tackled. Therefore, this paper presents an optimization scheme of frequency allocation and power control to improve the communication quality of users. Firstly, the multiple region frequency allocation (MRFA) scheme is proposed for non-uniform user distribution to alleviate interference among users and allocate spectrum resources equally to dense users. This scheme is modelled as a sub-region partition problem with maximum sum rate, which can disperse densely distributed users to different sub-bands for transmission. Secondly, the convergent power control (CPC) algorithm is considered to improve the transmission performance of each user. It can converge the transmit power to the optimal transmit power for reducing interference among co-channel users when there is no disturbance in the network. The CPC algorithm can also make the signal-to-interference-plus-noise-ratio (SINR) of each active user always greater than the target threshold until the end of communication by dynamically adjusting the transmit power of users. Simulation results show that the joint scheme of MRFA and CPC achieves a higher system throughput and better user performance than existing frequency allocation schemes or power control schemes.