Macrocell Users Research Articles

Cross-Tier Interference Mitigation for RIS-Assisted Heterogeneous Networks

With the development of the next generation of mobile networks, new research challenges have emerged, and new technologies have been proposed to address them. On the other hand, reconfigurable intelligent surface (RIS) technology is being investigated for partially controlling wireless channels. RIS is a promising technology for improving signal quality by controlling the scattering of electromagnetic waves in a nearly passive manner. Heterogeneous networks (HetNets) are another promising technology that is designed to meet the capacity requirements of the network. RIS technology can be used to improve system performance in the context of HetNets. This study investigates the applications of reconfigurable intelligent surfaces (RISs) in heterogeneous downlink networks (HetNets). Due to the network densification, the small cell base station (SBS) interferes with the macrocell users (MUEs). In this paper, we utilise RIS to mitigate cross-tier interference in a HetNet via directional beamforming by adjusting the phase shift of the RIS. We consider RIS-assisted heterogeneous networks consisting of multiple SBS nodes and MUEs that utilise both direct paths and reflected paths. Therefore, the aim of this study is to maximise the sum rate of all MUEs by jointly optimising the transmit beamforming of the macrocell base station (MBS) and the phase shift of the RIS. An efficient RIS reflecting coefficient-based optimisation (RCO) is proposed based on a successive convex approximation approach. Simulation results are provided to show the effectiveness of the proposed scheme in terms of its sum rate in comparison with the scheme HetNet without RIS and the scheme HetNet with RIS but with random phase shifts.

Optimal Learning Paradigm and Clustering for Effective Radio Resource Management in 5G HetNets

Ultra-dense heterogeneous networks (UDHN) based on small cells are a requisite part of the future cellular networks as they are proposed as one of the enabling technologies to handle coverage and capacity problems. But co-tier and cross-tier interferences in UDHN severely degrade the quality of service due to K-tiered architecture. Machine learning based radio resource management either through independent learning or cooperative learning is a proven efficient scheme for interference mitigation and quality of service provision in UDHN in a both distributive and cooperative manner. However, an optimal learning paradigm selection, i.e., either independent or cooperative learning and optimal cooperative cluster size in cooperative learning for efficient radio resource management in UDHN is still an open research problem. In this article, a Q-learning based radio resource management scheme is proposed and evaluated for both distributive and cooperative schemes using independent and cooperative learning. The proposed Q-learning solution follows the ϵ-greedy policy for optimal convergence. The simulation results for the UDHN in an urban setup show that in comparison to the independent learning paradigm, cooperative learning has no significant impact on macro cell user capacity. However, there is a significant improvement in small cell user capacity and the sum capacity of the cooperating small cells in the cluster. A significant increase of 48.57% and 37.9% is observed in the small cell user capacity, and sum capacity of the cooperating small cells, respectively, using cooperative learning as compared to independent learning which sets cooperative learning as an optimal learning strategy in UDHN. The improvement in small cell user capacity is at cost of increased computational time which is directly proportional to the number of cooperating small cells. To solve the issue of computational time in cooperative learning, an optimal clustering algorithm is proposed. The proposed optimal clustering reduced the computational time by four times in cooperative Q-learning.

Dynamic cell sleeping mechanism: An energy‐efficient approach for Mobile 5G HetCN

SummaryThe paper proposes a novel cell sleeping mechanism for enhancing network energy‐efficiency and to combat dynamic downlink interferences linked with mobile Small Cells (mSCs) in a 5G Heterogeneous Cellular Network (HetCN). The proposed Dynamic Mobile Cell Sleeping Mechanism (DMCSM) allows deployed vehicle‐mounted mSCs to dynamically go into sleep based on the calculated distances of users from its serving mSCs BS. With this mechanism, vehicular mSCs during mobility dynamically calculate their distances with the Macrocell (MC) users. The mSCs go into sleep or get deactivated for a while based on the calculated distance and the cell radius defined for mSCs. The mSCs will get active and starts to provide services to the users that are found within their coverage radius. The setup 5G HetCN is investigated with a MC superimpose with fixed SCs (fSCs) and mobile SCs (mSCs). The proficiency of DMCSM is investigated with the exploitation of existing sub 6 GHz groups at MCs and the millimeter wave (mmWave) spectrums at deployed fSCs and mSCs. The network downlink performance metrics: user throughput, sumrate, energy‐efficiency, and outage probability, have been explored. The work also depicts a comparison of the proposed DMCSM mechanism with the author's previously proposed ICI mitigation techniques.

Quality-driven video streaming for ultra-dense OFDMA heterogeneous networks

Quality-driven video streaming for ultra-dense OFDMA heterogeneous networks

Hybrid beamforming for millimeter-wave massive MIMO heterogeneous networks

Hybrid beamforming for millimeter-wave massive MIMO heterogeneous networks

Optimizing the operator’s economy in multi-tier cellular networks with underlaid D2D communications

Optimizing the operator’s economy in multi-tier cellular networks with underlaid D2D communications

MM-Wave HetNet in 5G and beyond Cellular Networks Reinforcement Learning Method to improve QoS and Exploiting Path Loss Model

This paper presents High density heterogeneous networks (HetNet) which are the most promising technology for the fifth generation (5G) cellular network. Since 5G will be available for a long time, previous generation networking systems will need customization and updates. We examine the merits and drawbacks of legacy and Q-Learning (QL)-based adaptive resource allocation systems. Furthermore, various comparisons between methods and schemes are made for the purpose of evaluating the solutions for future generation. Microwave macro cells are used to enable extra high capacity such as Long-Term Evolution (LTE), eNodeB (eNB), and Multimedia Communications Wireless technology (MC), in which they are most likely to be deployed. This paper also presents four scenarios for 5G mm-Wave implementation, including proposed system architectures. The WL algorithm allocates optimal power to the small cell base station (SBS) to satisfy the minimum necessary capacity of macro cell user equipment (MUEs) and small cell user equipment (SCUEs) in order to provide quality of service (QoS) (SUEs). The challenges with dense HetNet and the massive backhaul traffic they generate are discussed in this study. Finally, a core HetNet design based on clusters is aimed at reducing backhaul traffic. According to our findings, MM-wave HetNet and MEC can be useful in a wide range of applications, including ultra-high data rate and low latency communications in 5G and beyond. We also used the channel model simulator to examine the directional power delay profile with received signal power, path loss, and path loss exponent (PLE) for both LOS and NLOS using uniform linear array (ULA) 2X2 and 64x16 antenna configurations at 38 GHz and 73 GHz mmWave bands for both LOS and NLOS (NYUSIM). The simulation results show the performance of several path loss models in the mmWave and sub-6 GHz bands. The path loss in the close-in (CI) model at mmWave bands is higher than that of open space and two ray path loss models because it considers all shadowing and reflection effects between transmitter and receiver. We also compared the suggested method to existing models like Amiri, Su, Alsobhi, Iqbal, and greedy (non adaptive), and found that it not only enhanced MUE and SUE minimum capacities and reduced BT complexity, but it also established a new minimum QoS threshold. We also talked about 6G researches in the future. When compared to utilizing the dual slope route loss model alone in a hybrid heterogeneous network, our simulation findings show that decoupling is more visible when employing the dual slope path loss model, which enhances system performance in terms of coverage and data 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.

QoS-Aware Resource Allocation with Pilot-Aided Channel Estimation for Heterogeneous Wireless Networks

The deployment of heterogeneous networks (HetNets) is a way to increase the network capacity and release part of the traffic generated by users inside a cell to small-scale wireless networks for service. In this context, the main problem is managing the interference due to the coexistence of small cells and macro cells. In this paper, a QoS-aware Resource Allocation (RA) algorithm jointly working with admission control (AC) over a two-tier HetNet scenario is investigated in the presence of both the pilot-symbols for channel estimation and the channel estimation error. The RA algorithm allows two users, the macro cell user (CU) and small cell user (SU), to simultaneously share the same resource block. Moreover, system performance and fairness are improved by including adaptive power allocation to users over resource blocks. In the framework of RA with proportional rate constraints, a novel algorithm is designed by including the effects of pilot-aided channel estimation. The algorithm is able to distribute the same proportional rate to all CUs and SUs, even in the presence of channel estimation error. Relevant numerical results for the downlink of a two-tier HetNet with pilot-aided channel estimation show that the rate dispersion is driven to zero while the sum-rate is maximized, and the average user rate penalty with respect to a perfect-CSI scenario may rise to 20%.

Multiplexing Capacity of Hybrid PD-SCMA Heterogeneous Networks

Hybrid multiple access schemes are considered potential technologies towards achieving optimal spectrum sharing for future heterogeneous networks. Multiple users are multiplexed on a single resource unit in the code domain for sparse code multiple access (SCMA) or power domain for power domain non-orthogonal multiple access (PD-NOMA) and in both domains for the hybrid power domain sparse code non-orthogonal multiple access (PD-SCMA). This allows for effective spectrum usage but comes at a cost of increased detection complexity resulting in loss of performance in terms of outages. It is therefore imperative to determine the user multiplexing capacity for effective performance. This work investigates codebook capacity bounds in small cells, pairing and power capacity bounds for the number of small cell user equipment’s (SUEs) and macro cell user equipment’s (MUEs) that can be multiplexed on a codebook for the developed PD-SCMA technology. Closed-form solutions for codebook, pairing and power multiplexing capacity bounds are derived. The performance of the system results into low outage when the system’s point of operation is within the multiplexing bounds. To alleviate the resource allocation (RA) challenges of such a system at the transmitter, dual parameter ranking (DPR) and alternate search method (ASM) based RA schemes are proposed. The results show significant capacity gain with DPR-RA in comparison with conventional schemes.

Performance analysis of 5G heterogeneous networks under the impact of aggregate interference over Nakagami‐m fading channels

Abstract Heterogeneous networks (HetNets), which consist of ultra‐dense femtocell networks underlaid by traditional homogenous macrocell networks, are a promising option for the exceedingly high data rate demands of upcoming 5G communications. The densely deployed femtocells in cochannel mode cause severe interference because of the short distances between cochannel cells, which is the main challenge for femtocell deployment in HetNets. The HetNets are modeled based on stochastic geometry, which is assumed to distribute the interfering nodes as a point process. Specifically, in this article, a homogeneous Poisson point process is used to model the random distribution of macrocell users and femtocell base stations. Based on the complementary cumulative distribution function of the received signal to interference ratio at the macrocell base stations and femtocells, closed‐form expressions are derived for the outage probability. Further, closed‐form expressions are derived for the network throughput and energy efficiency of femtocells. Finally, theoretical and simulation results are discussed for the derived expressions under different system parameters.

Relay-Based Clustering Method for Interference Management in Heterogeneous Wireless Cellular Network

Femtocell is one of solutions to improve quality of services and network capacity for users in indoor areas. Radio resources used by femtocells are shared from macrocell network, thus it saves the use of frequency spectrum. However, one of problems in deploying femtocells within coverage area of macrocells is interference due to radio resources sharing between femtocells and macrocells. It creates interferences called as cross-tier (macrocell-femtocell/femtocell-macrocell) and co-tier (macrocell-macrocell/femtocell-femtocell) interferences. This paper proposes a relay-based clustering method to mitigate interference in femtocells located in the whole edge area of macrocell and the cell edge area of sectorized macrocells. Relay nodes are deployed statically (fixed location) in the neighboring macrocell area. Relay node will recruit their members based on the shortest distance. Certain relay node’s members do not need to transmit large amounts of power to enhanced Node B (eNB), such that interference from Macrocell User Equipment (MUE) to Home enhanced Node B (HeNB) can be minimized. Simulation experiments has been carried out and optimistic results for the sectorized macrocells scenario show that Signal-to-Interference-plus-Noise-Ratio (SINR) of femtocells for the conventional system that does not reach the targeted SINR of 20 dB is 87%. Meanwhile, after applying the relay-based clustering method, SINR value of femtocells below or equal to 20 dB reaches 72%. Optimistic results for throughput and Bit Error Rate (BER) show improvement of 15% and 14%, respectively. It has been shown that the relay-based clustering method can provide better performance compared to the conventional system even for femtocells densely deployed.

Hybrid spectrum management using integrated fuzzy and femtocells in cognitive domain

In Today’s pandemic situation, ‘Spectrum accessing and smart usage’ is the sacred Mantra uttered by every individual citizen in the world. Work from home for techies, online classes for students, games for kids, webinar for teaching fraternity etc., are going almost on indoor coverage without any limit in pace because of the smart spectrum coverage by the network service providers. This paper provides an add-on facility to the existing wireless infrastructure to provide a better user experience in this highly regrettable routine. In this paper, a cognitive domain unused spectrum holes are efficiently handled by (i) adaptive spectrum management technique; (ii) Fuzzy Inference System based spectrum administration and (iii) Hybrid Cognitive Femtocell approaches based on the user demand and their applications. The proposed integrated cognitive femtocell and Fuzzy-based approach reduces the indoor coverage problems and enhances the throughput of the macrocell users by allowing adaptive spectrum management based on the demand, thereby eliminating spectrum underlay and overlay problems during critical conditions. In cognitive femtocell networks, the access points are prepared and installed with Cognitive Radio which can determine spectrum dynamically by macrocells and nearby Femto Access Points. It adjusts its radiating parameters to evade the macrocells’ interferences and the neighbouring femtocells, thereby maximising the spectrum band’s overall utility.

Bargaining solutions in heterogeneous networks: A reinforcement learning‐based approach

To enhance the performance and the coverage area of the next-generation heterogeneous wireless networks (HetNets), smaller cells such as femtocells are deployed. A reasonable resource allocation strategy is crucial for the operation of such systems. In this work, the Nash bargaining solution (NBS), Kalai–Smorodinsky bargaining solution (KSBS), and a proposed bargaining solution, which is the combination of NBS and KSBS, are studied to ensure the efficiency and fairness in the transmission rate allocation among femtocells. The mentioned bargaining solutions are optimised subject to the rate constraints due to the Han–Kobayashi (HK) rate splitting scheme, and a maximum tolerable interference caused by femtocell base stations (FBSs) at the macrocell user equipment. Two methods are used to address the bargaining solutions' optimisations. First, the conventional optimisation-based method is used for the situations where the channel gains are known, then a reinforcement learning-based algorithm for a real environment is proposed without being aware of the channel gains. To accelerate the speed of the learning process and to initialise the values of quality functions, the hotbooting technique is used. The bargaining solutions are compared by fairness and efficiency criteria in both methods. Numerical simulations show that the proposed reinforcement learning-based algorithm achieves the same performance derived from the conventional optimisation method. First, this paper studies the two well-known Nash and Kalai–Smorodinsky bargaining solutions as the good tools to establish fairness in rate allocation problem among femtocells in a HetNet. Then a new bargaining solution is proposed as the combination of the two bargaining solutions. The fairness and the efficiency of the allocated rates to femtocells as the output of optimisation problems of bargaining solutions are compared via Jain index criterion (as the fairness metric) and inverse price of anarchy criterion (as the efficiency metric). Moreover, the femtocell base stations are allowed to use Han–Kobayashi signalling scheme to send their signals in private and common messages which results in the best-known achievable rate region. Two methods are used to solve the optimisation problems: the conventional optimisation method and the reinforcement learning-based method (to formulate a real-world model with the unknown channel gains). It is shown that the proposed bargaining solution (in both methods) results in fairer rates than Nash and Kalai–Smorodinsky bargaining solutions while all of them are efficient.

Robust Resource Allocation for Two-Tier HetNets: An Interference-Efficiency Perspective

The heterogeneous network (HetNet) is a promising network for beyond 5G to improve network capacity and spectral efficiency by developing low-power small cells within macro networks. Therefore, appropriate resource allocation schemes should be designed to balance between data rates and the cross-tier interference among different cells. To improve system robustness and also reduce the harmful interference to macrocell users (MUs), in this paper, we maximize the total interference efficiency (IE) (i.e., the ratio of the sum data rates of femtocell users (FUs) to the total cross-tier interference from femtocell base stations (FBSs) to MUs) with imperfect channel state information in a two-tier orthogonal frequency division multiple access based HetNet by jointly optimizing the transmit power of the FBS and the subcarrier allocation factor. Meanwhile, the outage rate constraint of each FU, the outage interference constraint of each MU, the maximum transmit power constraint of the FBS, and the subcarrier assignment constraint are considered simultaneously. The considered problem belongs to a mixed-integer non-linear programming problem and thus is NP-hard. The original problem with uncertain parameters is transformed into a deterministic and convex one solved by using the quadratic transformation approach, the variable relaxation approach, and Lagrange dual theory. The feasible region analysis and robust sensitivity are provided. Simulation results demonstrate that the proposed scheme has a lower interference to the MU and higher IE by comparing it with the traditional schemes.

Robust resource allocation for NOMA-assisted heterogeneous networks

Robust resource allocation for NOMA-assisted heterogeneous networks

Energy Efficient Downlink Resource Allocation in Cellular IoT Supported H-CRANs

The cloud computing supported heterogeneous cloud radio access network (H-CRAN) is one of the promising solutions to support cellular IoT devices with the legacy cellular systems. However, the dense deployment of small cells with fractional frequency reuse in orthogonal frequency division multiple access (OFDMA) based H-CRANs increases intra- and inter-cell interference, turning the resource allocation into a more challenging problem. In general, the macro cell users are considered as the legacy users, whereas the cellular IoT devices and small cell users share the macro cell users' resource blocks in an underlaid approach. In this paper, we investigate an underlaid approach of resource allocation for small and macro cell users to improve the energy efficiency (EE) in H-CRANs. The solution approaches are derived with the Dinkelbach, Lagrange multiplier and Alternating Direction Method of Multipliers (ADMM) methods by considering the maximum power, resource block allocation, fronthaul capacity and quality of service (QoS) constraints of macro cell users. A two-step energy efficient underlaid cellular IoT (UC-IoT) supported H-CRAN method is proposed and evaluated with the overlaid cellular IoT (OC-IoT) supported H-CRAN and underlaid H-CRAN without cellular IoT devices. The proposed method is evaluated in terms of energy efficiency and the Jain's fairness index, considering the effect of number of cellular IoT density in each small cell of the H-CRAN. The simulation results demonstrate the effectiveness of the proposed approach compared to earlier approaches.

Energy-Efficient Resource Allocation for Ultra-Dense Licensed and Unlicensed Dual-Access Small Cell Networks

In this study, an energy-efficient self-organized framework for sub-channel allocation and power allocation is presented for ultra-dense small cell networks, which can operate in both licensed and unlicensed bands. In order to protect legacy WiFi devices (operating in unlicensed bands), we consider the Long-Term Evolution (LTE) operation in unlicensed bands based on Carrier Sense Adaptive Transmission (CSAT), in which 'ON' and 'OFF' duty cycle approach is utilized. On the other hand, there are severe interference management problems among small cells (operating in licensed and unlicensed bands) and between macro cells and small cells (operating in licensed bands) due to co-channel and ultra-dense deployment of small cells. This article proposes a self-organized optimization framework for the allocation of sub-channels and power levels by exploiting a non-cooperative game with the objective to maximize the energy efficiency of dual-access small cells without creating harmful impact on coexisting network entities including macro cell users, small cell users, and legacy WiFi devices. Simulation results show that the proposed scheme outperforms (6 and 11 percent) and (8 and 18 percent) the round-robin and the spectrum-efficient schemes, respectively, for two different small cell scenarios. In addition, it is shown that for less channel state information (CSI) estimation errors ς = 0.02, the maximum performance degradation of the proposed scheme is reasonably small (5.5 percent) as compared to the perfect CSI.

Walsh–Hadamard Transform Based Non-Orthogonal Multiple Access (NOMA) and Interference Rejection Combining in Next-Generation HetNets

In heterogeneous networks (HetNets), non-orthogonal multiple access (NOMA) has recently been proposed for hybrid-access small-cells, promising a manifold network capacity compared to OMA. One of the major issues with the installation of a hybrid-access mechanism in small-cells is the cross-tier interference (intercell interference (ICI)) caused by the macrocell users (MUs) that are unable to establish a connection to the small-cell base station (SBS). In this paper, a joint strategy is proposed for hybrid-access small-cells using the Walsh–Hadamard transform (WHT) with NOMA and interference rejection combining (IRC) to achieve high performance gains and mitigate intercell interference (ICI), respectively. WHT is applied mathematically as an orthogonal variable spreading factor (OVSF) to achieve diversity in communication systems. When applied jointly with NOMA, it ensures better performance gains than the conventional NOMA. It reduces the bit error rate (BER) and enhances subsequent throughput performance of the system. IRC is used at the receiver side for managing the cross-tier interference caused by MUs that are unable to connect to the small-cell base station (SBS) for hybrid-access. The work considers both ideal and nonideal successive interference cancellation (SIC) conditions for NOMA. Mathematical modeling is provided for the proposed joint strategy for HetNets and the results validate it in terms of BER and subsequent user throughput performance, compared to the conventional NOMA approach.

Efficient Resource Allocation Using Distributed Edge Computing in D2D Based 5G-HCN With Network Slicing

Fifth Generation (5G) cellular networks aim to overcome the pressing demands posed by dynamic Quality of Service (QoS) constraints, which have primarily remained unaddressed using conventional network infrastructure. Cellular networks of the future necessitate the formulation of efficient resource allocation schemes that readily meet throughput requirements. The idea of combining Device-to-Device (D2D), Mobile Edge Computing (MEC), and Network slicing (NS) can improve spectrum utilization with better performance and scalability. This work presents a spectrum efficiency optimization problem in D2D based 5G-Heterogeneous Cellular Network (5G-HCN) with NS. Owing to the shortage of resources, we propose an underlay model where macro-cell users (MUs), small-cell users (SUs), and D2D users (DUs) reuse the resources while considering the effects of interference. The goal is to maximize the average network spectrum efficiency (SE) and throughput without degrading the system performance. The problem at hand is naturally a non-convex mixed-integer non-linear programming (MINLP) problem that is intractable. Therefore, we have suggested a distributed resource allocation strategy with an edge computing (DRA-EC) approach to find the sub-optimal solution. In distributed augmented Lagrange method, each edge router located at BS will solve its problem locally, and the consensus algorithm will find the global solution using these local estimates. The central slice controller will cut the customized network slices according to the bandwidth requirements of each user type with optimized spectrum information. The simulation outcomes prove that our proposed method is near the central optimization scheme with low computational complexity. It is much better because it reduces the computational time and system overhead.