High User Density Research Articles

Development model of a high-performance multiple input multiple output microstrip antenna based on a planar series array with 8×2 elements for 5G communication systems

MIMO (Multiple Input Multiple Output) makes a major contribution to 5G communication systems by increasing network capacity and spectrum efficiency. In 5G, MIMO enables the use of multiple antennas at base stations and user devices, allowing simultaneous sending and receiving of data over multiple paths. This significantly increases data throughput and connection reliability, especially in environments with high user density. In addition, MIMO technology supports the implementation of beamforming, which focuses signals on a specific direction, reduces interference, and improves signal coverage and quality, making it one of the keys to achieving faster and more responsive 5G performance. Therefore, antennas with wide bandwidth, high gain and MIMO performance are crucial for supporting 5G communication systems. This paper proposes a high-performance MIMO microstrip antenna based on a series planar array with 8×2 elements operating at a resonant frequency of 3.5 GHz for 5G communication systems. A spiral stub and a feed inset are proposed to control the reflection coefficient and bandwidth of the antenna while the series planar array is proposed to increase the gain. To support the MIMO communication system, the proposed antenna is separated into two different ports with a certain distance. From the measurement results, the proposed antenna has high performance indicated by a wide bandwidth of 680 MHz (3–3.68 GHz) and a high gain of 17.8 dB at a resonant frequency of 3.5 GHz. In addition, the proposed antenna has high mutual coupling and diversity indicated by ECC and DG of 0.001 and 9.99 dB, respectively. This work provides a solution to design a high-performance microstrip antenna and can be implemented as a receiving antenna for 5G communication systems

Integrating Multi-Access Edge Computing (MEC) into Open 5G Core

Multi-Access Edge Computing (MEC) represents the central concept that enables the creation of new applications and services that bring the benefits of edge computing to networks and users. By implementing applications and services at the edge, close to users and their devices, it becomes possible to take advantage of extremely low latency, substantial bandwidth, and optimized resource usage. However, enabling this approach requires careful integration between the MEC framework and the open 5G core. This work is dedicated to designing a new service that extends the functionality of the Multi-Access Traffic Steering (MTS) API, acting as a strategic bridge between the realms of MEC and the 5G core. To accomplish this objective, we utilize free5GC (open-source project for 5G core) as our 5G core, deployed on the Kubernetes cluster. The proposed service is validated using this framework, involving scenarios of high user density. To conclude whether the discussed solution is valid, KPIs of 5G MEC applications described in the scientific community were sought to use as a comparison parameter. The results indicate that the service effectively addresses the described issues while demonstrating its feasibility in various use cases such as e-Health, Paramedic Support, Smart Home, and Smart Farms.

Cross-Layer Wireless Resource Allocation Method Based on Environment-Awareness in High-Speed Mobile Networks

In high-speed mobile scenarios characteristic of the Fifth Generation Mobile Networks (5G) environment, the user video experience may be compromised due to concurrent access by numerous users and frequent base station transitions. Addressing this issue, this study introduces a cross-layer resource allocation model that integrates environmental awareness and is tailored for the exigencies of high-speed mobile networks. The paper delves into the challenges engendered by rapid mobility and extensive user access within the 5G environment and critiques the constraints of prevalent resource allocation methodologies. The model delineated herein is conceptualized as an optimization challenge and characterized as a nonlinear, NP-hard problem. In response to this challenge, this study advocates a novel streaming media transmission algorithm underpinned by edge computing, in tandem with an environment-aware wireless resource allocation algorithm. The article articulates the foundational principles and operational modalities of these algorithms, underscoring the significance of environmental cognizance in resource distribution and the efficacy of edge computing in increasing video transmission efficiency. Empirical validation, achieved through simulation experiments, corroborates the efficacy of the proposed approach. Comparative analysis reveals that, relative to conventional methodologies, the proposed framework significantly improves video transmission quality and user experience, particularly in contexts characterized by frequent network fluctuations and high user densities. This research contributes novel insights and pragmatic solutions to optimize video transmission in existing 5G and prospective network paradigms.

A novel approach of privacy protection of mobile users while using location-based services applications

The necessity of protecting mobile users' privacy when using location-based services (LBS) is highlighted in recent study, and location cloaking is one of the solutions offered. Location cloaking makes an effort to safeguard users' location privacy by introducing uncertainty while finding out the users’ location-context. The existing location cloaking algorithms are cautious about quality of the query service metrics (QoS), which mainly include success rate, anonymity level and spatial resolution. However, performance metric that is time to generate the cloaked regions should be improved. This was particularly important when user density is high. This work provides an efficient location cloaking scheme where a clique based cloaked region is constructed via partitioning of user-graph using graph partitioning algorithm, thereby generates reasonably-sized cloaked regions in less time. Further, to optimize cloaked region generation, multi-depth partitioning algorithm called BLH (Balanced Location Hiding) is proposed. Through concurrent and optimal generations of cloaked regions the proposed location cloaking preserves users' location privacy to a higher extent while retaining both the QoS and performance criteria. Experimental findings demonstrate that the success rate of the proposed algorithms is significantly increased. Even with a higher user density, it is approximately 98% with quicker response time.

Traffic based power consumption and node deployment in green LTE-A cellular networks

The energy utilization of green cellular networking or cellular network infrastructure has become a popular research domain due to the economic concern of network operators and global climate change. Also, due to the growth of wireless networks, the authors are attracted to finding novel relay station (RS) deployment techniques to support high user density and high data rate services by reducing energy consumption and operating costs of different network elements. The location of RSs greatly influences how far a cell can be covered. Therefore, the deployment RS plays a major role in widening the cell's coverage radius. To solve these challenges and to realize greener network designs by maximizing power consumption with QoS assurance, a novel traffic-based power consumption minimization and node deployment for the LTE green cellular networks is proposed. Also, key performance indicators (KPIs) are required to monitor and improve network performance. KPIs control the accomplished resource utilization and the quality of services. The research work comprises RS deployment, traffic estimation, and power allocation. Power losses, communication delays, increased implementation costs, and decreased throughput are the effects of an inappropriate RS deployment. Therefore, this research introduces the transmission area-based relay stations deployment scheme (TARSD) with multiobjective functions maximization of coverage area, minimization of the overlapping area and the minimization of power consumption cost. Traffic estimation helps to identify future capacity requirements and enables improved planning and decision-making. The deployment scheme of a cellular network relies on network traffic estimation. Nash traffic entropy learning algorithm (NTEL) is used to estimate traffic, and based on the estimated eNB switching ON/OFF is performed to minimize power consumption. Inter cell interference (ICI) can impact the performance of the LTE network. Several power allocation schemes can be used to improve system performance to achieve the optimal trade-off between interference and signal-to-noise ratio (SINR). Because of its good performance and efficacy in improving the mutual information between a channel's input and output, the enhanced low complexity water-filling (ELCWF) has been widely employed in power allocation with its increment and decrement properties. This research studies the statistical behaviour of a Received Signal Strength Indicator (RSSI), Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ) and Signal to Interference noise Ratio (SINR). Also, statistical analysis of cost versus coverage performance is examined. Under the 7th simulation run, the proposed method yields the best coverage of 88.81 and 98.74%, respectively. In the experimental scenario, for the proposed model, the deployment cost for BS and RS are 27 and 13 units, the average throughput per user is 9.74 Mbps, and the coverage ratio is 97% under the BS candidate location of 20, respectively. In the power allocation stage, if the number of users is 50, the proposed model yields a power consumption of 0.2 W, throughput of 23.4 Mbps, delay of 15 ms and SINR of 20.12 dB, respectively.

QoS-Aware User Scheduling in Crowded XL-MIMO Systems Under Non-Stationary Multi-State LoS/NLoS Channels

Ensuring that the quality-of-service (QoS) requirements are satisfied in wireless communications systems with high user density is challenging due to the limitations on the transmit power budget and the number of resource blocks. In this paper, we propose a QoS-aware joint user scheduling and power allocation technique to enhance the number of served users in the downlink of crowded extra-large scale massive multiple-input multiple-output (XL-MIMO) with minimum QoS requirements guarantee. The proposed technique is constituted by two sequential procedures: the clique search-based scheduling (CBS) algorithm for user scheduling followed by optimal power allocation with transmit power budget and minimum achievable rate per user constraints.We propose a generalized non-stationary multi-state channel model based on spherical wave propagation assuming that users under line-of-sight (LoS) and non-LoS (NLoS) transmission coexist in the same communication cell. This is done to accurately evaluate the proposed technique in realistic XL-MIMO scenarios. Numerical results reveal that the proposed CBS algorithm provides fair coverage over the whole cell area, achieving remarkable numbers of scheduled users with satisfied QoS requirements when users under the LoS and NLoS channel states coexist in the communication cell.

New Bridge to Cloud: An Ultra-Dense LEO Assisted Green Computation Offloading Approach

Mobile edge computing and cloud computing have emerged as effective technologies to alleviate the increasing computational workload of mobile devices. As a promising enabling 6G technology, the ultra-dense (UD) low earth orbit (LEO) satellite network with low communication latency and high throughput is considered a new bridge for cloud computation offloading. In this paper, we investigate energy-efficient cloud and edge computing in UD-LEO-assisted terrestrial-satellite networks. An optimization problem aiming at minimizing the energy consumption of the computation tasks is formulated. The optimization problem is a mixed-integer non-linear programming problem. To solve this problem, we decompose it into two subproblems, i.e., a joint user association and task scheduling subproblem, and an adaptive computation resource allocation subproblem. For the first subproblem, we model the input of a forward neural network (NN) as the large-scale information (i.e., channel gain and task arrival rates) and obtain the optimal solution by transforming the direct output of the NN. For the second subproblem, we introduce a successive convex approximation method to optimize it iteratively. The simulation results show that our proposed user association and task scheduling strategy outperforms two benchmark algorithms in terms of energy consumption under a strict delay bound and high user density.

Dynamics of a Discrete Lotka–Volterra Information Diffusion Model

To explore the process of online social network information interaction, in this paper, we analyze the dynamics of a discrete Lotka–Volterra information diffusion model. Using the center manifold theorem, the conditions for transcritical bifurcation and flip bifurcation are obtained. With the help of approximation by a flow and Picard iteration, we explore the qualitative structures and stability of degenerate fixed point of the model with eigenvalues [Formula: see text]. What’s interesting is that our results reveal a new and complex qualitative structure for fixed point, which are different from the previous reports and called degenerate saddle point. Additionally, the qualitative structures provide a new idea for investigation the stability of degenerate fixed point. Meanwhile, near the maximum user density, the dynamic results of degenerate fixed point indicate that if the intervention rate is greater than the inverse of the maximum user density, then the higher user density decreases, the lower user density increases when intrinsic growth rates are small (between 0 and 2). However, when the intrinsic growth rate is greater than 2, the high user density will continue to increase until it approaches the maximum user density indefinitely, while the small user density will approach 0, which provide us with new insights into information diffusion. Finally, we show the results of the model by numerical simulations, and the characteristics of information diffusion near the degenerate fixed point are predicted by theoretical analysis.

Identification of Bacteria and Fungi in Various Types of Multi-Use Facilities in Bucheon, South Korea

The recent sequential appearance of infectious pathogens has caused ongoing social and economic damage. Despite the very high potential for pathogen transmission within indoor multi-use facilities, there are insufficient measures for the systematic analysis, diagnosis, and reduction in such transmission. Although real-time environmental information is available for pollutants such as particulate matter, carbon dioxide, and nitrogen dioxide in South Korea, an automatic network for the real-time measurements of harmful microorganisms has not yet been established. Therefore, in this study, we analyzed the concentrations of bacteria and fungi in different types of multiple-use facilities in Bucheon, South Korea, using the analytic hierarchy process (AHP) method. All multi-use facilities in the region were classified into six types: facilities for pollution-sensitive groups (e.g., children or the elderly) and transportation-related, public transportation, temperature-controlled, food preparation, and other facilities. Next, the importance of each facility type in terms of bacterial and fungal abundance was evaluated using the AHP method, according to criteria selected using the AHP method. The highest importance was assigned to multi-use facilities for air-pollution-sensitive groups, which were associated with higher user density and more confirmed cases of COVID-19; the second-highest importance was assigned to public-transportation facilities. Bacteria detected at representative multi-use facilities were identified using 16S rRNA sequencing and included the human pathogens Bacillus anthracis, Bacillus cereus, Pseudomonas fluorescens, Erwinia billingiae, and Enterobacter cloacae. This study is the first to measure monthly and seasonal concentrations of bacteria and fungi at 30 multi-use facilities in Bucheon. The results of this study will be useful for designing systematic measures for the control of infectious bacteria and fungi in various types of multi-use facilities, according to their specific characteristics.

Size distribution and concentration of indoor culturable bacterial and fungal bioaerosols

Exposure to bioaerosols in indoor environments may adversely affect human health. Herein, we present the concentrations and size distributions of bacterial and fungal bioaerosols measured in 5 locations (café, cafeteria, building lobby, classroom, and meeting room) within one building during winter. Sampling was conducted using a six-stage Andersen cascade impactor and the size-specific concentration of culturable bioaerosols was quantified via the colony counting method. The concentration and size distribution of bioaerosols differed among locations. The highest average concentrations of bacterial and fungal bioaerosols were found in the café (170 colony-forming unit [CFU]/m 3 ) and cafeteria (205 CFU/m 3 ). In the winter indoor environment, both fungal and bacterial bioaerosols were concentrated at respirable sizes <3.3 μm, and significant levels of bacterial bioaerosols <1.1 μm were observed in the café. This quantitative characterization of the physical properties of bioaerosols in indoor environments can be used to establish effective protection measures against the health impacts of bioaerosols. • Culturable bioaerosols were determined within a building in winter. • Significant bacterial bioaerosols were observed in the café with high user density. • Bacterial and fungal bioaerosols were concentrated in inhalable sizes <3.3 μm. • Bacterial bioaerosols <1.1 μm were observed in the café and building lobby.

Grant-Free Access for mMTC: A Performance Analysis Based on Number of Preambles, Repetitions, and Retransmissions

Grant-free (GF) access is a multiple access mechanism being considered for supporting high user density in massive machine-type communication (mMTC). Since the GF schemes are predominantly contention based, a major challenge would be to handle user packet collisions. Additionally, users experiencing collision will retransmit their packets, leading to an increased load on an already dense network. Thus, the probability of successful transmission of an individual user and that of all users become the key performance indicators (KPIs) to analyze such systems. In this article, a GF uplink system is modeled for cases when the number of users is—1) fixed and 2) random (following a Poisson distribution). Analytical closed-form expressions for all-user and per-user success probabilities are derived in both the cases. Conventionally, GF access schemes have been analyzed using a fixed number of preambles. In this work, it is proposed to choose the number of preambles in the GF system such that the probability of success of at least “all but two” users is greater than or equal to 99%. It is shown that such a choice limits the maximum number of colliding users to be within four, thereby reducing the number of retransmissions. The analysis is extended to the case of transmission using multiple repetitions and numerical simulations are performed to validate the theoretical analysis. Finally, a tradeoff between the number of preambles, repetitions, and retransmissions is presented in terms of the per-user success probability, transmission latency, and energy consumption.

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

The rapid growth of data traffic due to the demands of new services and applications poses new challenges to the wireless network. Unmanned aerial vehicles (UAVs) can be a solution to support wireless networks during congestion, especially in scenarios where the region has high traffic peaks due to the temporal and spatial flow of users. In this paper, an intelligent machine-learning-based system is proposed to deploy UAV base stations (UAV-BS) to temporarily support the mobile network in regions suffering from the congestion effect caused by the high density of users. The system includes two main steps, the load prediction algorithm (LPA) and the UAV-BSs clustering and positioning algorithm (UCPA). In LPA, the load history generated by the mobile network is used to predict which macrocells are congested. In UCPA, planning is performed to calculate the number of UAV BSs needed based on two strategies: naïve and optimized, in addition to calculating the optimal positioning for each device requested to support the overloaded macrocells. For prediction, we used two models, generalized regression neural networks (GRNN) and random forest, and the results showed that both models were able to make accurate predictions, and the random forest model was better with an accuracy of over 85%. The results showed that the intelligent system significantly reduced the overhead of the affected macrocells, improved the quality of service (QoS), and reduced the probability of blocking users, as well as defined the preventive scheduling for the UAV BSs, which benefited the scheduling and energy efficiency.

Application Layer-Forward Error Correction Raptor Q Codes in 5G Mobile Networks for Factory of the Future

The future communication requirements for industrial automation will differ significantly from existing technologies. Traffic in Industry 4.0 imposes real-time requirements, requiring ultra-reliable communication (URC) with high reliability and minimal latency. The demand for ultra-high reliability as high as 99.999999 percent and as low as 1 ms end-to-end latency is the major challenge of the NOMA communication system in the factory of the future. The high expectations on reliability and latency need modifications to the radio system’s baseband signal processing, medium access control (MAC) layer, and application layer to protect against packet losses. Thus, this paper investigates the utilization of Raptor Q codes, which is a type of Application Layer Forward Error Correction (AL-FEC), by developing an end-to-end system level simulator to evaluate and analyze the performance of the transmission signals based on cross-layer approach; from the physical layer (PHY), MAC layer, and application layer parameters in an indoor factory network setting. The factory is assumed to be operated with various factory robots of different speeds, from static to 10 km/h. The 5G technology relies heavily on flexible network operations. High user density, high user mobility, deployment, and coverage are all qualities that allow for this flexibility. Through extensive simulations, the results showed that the Raptor Q codes are not only able to give good results, i.e., packet reception rate PRR = 0.9 of 10 m or 1.8% to 10 m or 3.4% depending on different scenarios, but are also able to meet PRR = 0.9 in the mobility scenario at 10 km/h. Thus, the Raptor Q codes can be seen as a good candidate for obtaining results within a strict range of requirements set by URC communications for the factory of the future replacing RLNC.

Uplink Channel Allocation Scheme and QoS Management Mechanism for Cognitive Cellular-Femtocell Networks

Cognitive radio and femtocell are promising technologies which can satisfy the requirements of future mobile communications in terms of dynamic spectrum sharing and high user density areas. Providing quality-of-service (QoS) guaranteed realtime services is challenging issue of future cognitive cellular-femtocell mobile networks. In this paper, we introduce a user’s QoS management mechanism used to protect SINR of macro users from QoS violation caused by femtocell users. We design a novel uplink channel allocation scheme (denoted as “flexible scheme”) for real-time connections. The scheme uses the information of interference level and channel occupancy collected at cognitive femtocell access points and their covering macro base station (MBS) and apply relevant selection criteria to select an appropriate channel which causes the minimums interference to macro users of the covering MBS. Performance results prove that comparing with femtocell-access-point (FAP)-based and MBS-based uplink channel allocation schemes, the novel “flexible scheme” can provide lower unsuccessful probability of new connection requests.

A Road Truncation-Based Location Privacy-Preserving Method against Side-Weight Inference Attack

Taking advantage of precise positioning technology, location-based service (LBS) has brought a lot of convenience to people’s daily life and made the city smarter. However, the LBS applications also bring some challenges to personal location privacy protection. In order to obtain services from LBS providers, users have to upload their queries including sensitive information, such as identities and locations. This information may be leaked out by the LBS providers or even eavesdropped on by malicious adversaries, which may cause privacy leakage. To tackle this problem, many solutions have been investigated under the assumption that users are uniformly distributed. However, the users are not always uniformly distributed in real-world situations. For a side-weight inference attack, the adversary would infer that the target user is more likely to belong to the road section with more users, resulting in performance deterioration. In this paper, we investigate the issue of location privacy preservation against side-weight inference attack for non-uniform distributed road network. Meanwhile, we consider the cost function of LBS and formulate the object as a mixed integer programming problem. Then, we propose a road truncation-based scheme to protect location privacy. The road section with high user density is designed to be truncated. Finally, simulation results show that our scheme meets the demand for privacy protection at a low cost. As a result, our scheme is proven to protect users’ location privacy effectively and efficiently.

Survey on RoF technology and the mitigation schemes over the challenges in the RoF network

The fiber optic communication is the one where the wired and the wireless communication systems are integrated to have high bandwidth, low cost, and high data rate with high user density. The RoF technology brings about high-performance network and the guaranteed quality of service for the increased demand for multimedia services. The RoF faces problems like distortion and chromatic dispersion in the RoF link, reducing the quality of the signal at receiver. To enhance the quality of the signal at the receiver side, several techniques have been reported. Those techniques concentrated on the RoF link and the modulation schemes used in the technology. This article reviews the complete RoF technology, which includes the challenges that limit the RoF network along with its compensation scheme and the RoF in the PON services with security. Along with that, the comparative study on the problems faced by the RoF and its mitigation schemes, such as linearization techniques and compensation for chromatic dispersion, are discussed. The linearization schemes will analyze the different pre-distortion circuits used to linearize the transmitting signal such that the signal received by the mobile unit is free of distortion. When these problems are reduced, the RoF with PON has faced another major problem of insecure transmission. Hence the performance of the existing encryption schemes is compared.

TV White Space Network Power Allocation Using Hybrid Grey Wolf Optimizer with Firefly Algorithm and Particle Swarm Optimization

TV white spaces (TVWS) can be utilized by Secondary Users (SUs) equipped with cognitive radio functionality on the condition that they do not cause harmful interference to Primary Users (PUs). Optimization of power allocation is necessary when there is a high density of secondary users in a network in order to reduce the level of interference among SUs and to protect PUs against harmful interference. Grey Wolf Optimizer (GWO) is relatively recent population based metaheuristic algorithm that has shown superior performance compared to other population based metaheuristic algorithms. Recent trend has been to hybridize population based metaheuristic algorithms in order to avoid the problem of getting trapped in a local optimum. This paper presents the design and analysis of performance of a hybrid grey wolf optimizer and Firefly Algorithm (FA) with Particle Swarm Optimization operators for optimization of power allocation in TVWS network power allocation as a continuous optimization problem. Matlab was used for simulation. The hybrid of GWO, FA and PSO (HFAGWOPSO) reduces sum power by 81.42% compared to GWO and improves sum throughput by 16.41% when compared to GWO. Simulation results also show that the algorithm has better convergence rate.

Design and analysis 5G mobile network model to enhancement high-density subscribers

To obtain a high data rate that is commensurate with the growing demand for internet services, the fifth generation (5G) cellular networks will use the bandwidth beyond 6 GHz, called millimeters waves (mm-waves), to obtain a higher. The first phase (phase I) of the 5G network design for high user density, where the optimized microcells are deployed at carrier frequency 700 MHz with 20 MHz bandwidth. The second phase (phase II) of the design consists of the deployment of microcells which are operating at 3.6 GHz with 100 MHz bandwidth; this phase is planned to cover 200000 users within the province. The third phase (phase III) of the design is represented by the deployment of picocells, which are planned to operate at 26 GHz frequency and bandwidth 500 MHz; this phase is planned to cover 3,500,000 users within the province. Two types of modulation are adopted for the network (orthogonal frequency division multiplexing (OFDM) and 256 quadrature amplitude modulation (QAM)); the overall performance of the network is studied with regards to the percentage of coverage, power overlapping ratio, frequency interference, and quality of service (QoS).

A Poisson Process-Based Random Access Channel for 5G and Beyond Networks

The 5th generation (5G) wireless networks propose to address a variety of usage scenarios, such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). Due to the exponential increase in the user equipment (UE) devices of wireless communication technologies, 5G and beyond networks (B5G) expect to support far higher user density and far lower latency than currently deployed cellular technologies, like long-term evolution-Advanced (LTE-A). However, one of the critical challenges for B5G is finding a clever way for various channel access mechanisms to maintain dense UE deployments. Random access channel (RACH) is a mandatory procedure for the UEs to connect with the evolved node B (eNB). The performance of the RACH directly affects the performance of the entire network. Currently, RACH uses a uniform distribution-based (UD) random access to prevent a possible network collision among multiple UEs attempting to access channel resources. However, in a UD-based channel access, every UE has an equal chance to choose a similar contention preamble close to the expected value, which causes an increase in the collision among the UEs. Therefore, in this paper, we propose a Poisson process-based RACH (2PRACH) alternative to a UD-based RACH. A Poisson process-based distribution, such as exponential distribution, disperses the random preambles between two bounds in a Poisson point method, where random variables occur continuously and independently with a constant parametric rate. In this way, our proposed 2PRACH approach distributes the UEs in a probability distribution of a parametric collection. Simulation results show that the shift of RACH from UD-based channel access to a Poisson process-based distribution enhances the reliability and lowers the network’s latency.

Joint Optimization of BS Clustering and Power Control for NOMA-Enabled CoMP Transmission in Dense Cellular Networks

Non-orthogonal multiple access (NOMA)-enabled coordinated multipoint (CoMP) transmission has great potential in balancing spectrum utilization and interference mitigation in dense cellular networks. However, NOMA reforms the spectrum sharing policy of CoMP transmission due to introducing additional interference among coordinated base stations (BSs), which deteriorates the CoMP transmission rate. In this paper, we investigate the joint optimization of BS clustering and power control for NOMA-enabled CoMP transmission in dense cellular networks to maximize system sum-rate. We first characterize the impact of interference among coordinated BSs on CoMP transmission rate and find that the BS clustering is dependent on NOMA user grouping and restricted by the NOMA decoding condition. We then derive a tight lower bound on the system sum-rate and exploit it to design a joint BS clustering and power control scheme. Specifically, a power control algorithm is designed by a penalty convex-concave procedure to satisfy the NOMA decoding condition and users’ rate requirements. A BS clustering algorithm based on successive convex approximation is designed to iteratively update the BS clustering and NOMA user grouping to increase system sum-rate. Finally, two algorithms are alternately performed until all users successfully access and the system sum-rate converges. Simulation results show that the proposed scheme can efficiently alleviate interference among coordinated BSs to improve system sum-rate and spectrum efficiency of NOMA-enabled CoMP transmission even under high user density.