Severe Inter-cell Interference Research Articles

Performance Analysis of Cellular Ultradense IoT Networks With Wireless Backhauls

The rising era of smart living requires unprecedented advancements in cellular networks to support the communications of the Internet of Things (IoT), referred to as Machine-Type Communication (MTC). Hence, we consider an Ultra-Dense Network (UDN) environment supported by wireless backhaul links and investigate the achievable performance gains for MTC. By doing so, we avoid the complexity, cost, and/or infeasibility of providing fiber backhaul links for the massive number of Small Cells (SCs) found in UDNs. We utilize the Millimeter Wave (mmWave) band to support the communications between the IoT Devices (IoTDs) and their serving SCs. By doing so, the excess available bandwidth can be used to support a massive number of IoTDs while the propagation characteristics of the mmWave signals can be exploited to mitigate the severe Inter-Cell Interference (ICI) found in UDNs. In this regard, we formulate a mathematical framework using tools from stochastic geometry to derive analytical expressions for the density of supported IoTDs and the average capacities per SC on both the Access Link (AL) and the Backhaul Link (BH). In addition, we obtain a tight lower bound of the average capacity per SC under the considered wireless limited-capacity backhaul links. The obtained results show the existence of an optimal active SC density that maximizes SC utilization.

Impacts of Antenna Downtilt and Backhaul Connectivity on the UAV-Enabled Heterogeneous Networks

The performance of unmanned aerial vehicle (UAV)-enabled networks is generally bottlenecked by severe inter-cell interference and limited backhaul connectivity. Against this backdrop, we analyze the performance of the UAV-enabled heterogeneous networks, where antenna downtilt at each UAV-mounted BS is employed to mitigate inter-cell interference, and the tethered UAV (TUAV)-mounted BS is deployed to provide backhaul connectivity for multiple spatially randomly distributed untethered UAV-mounted BSs, while they are cooperatively serving the terrestrial users. Through leveraging stochastic geometry, the user association probability and conditional distance distributions of serving links are derived. Then, the compact expressions of coverage probability and ergodic rate of the UAV-enabled heterogeneous networks are derived, and the impacts of antenna downtilt and backhaul connectivity are investigated. Numerical results validate our analysis and show the effectiveness of antenna downtilt and the TUAV-enabled backhaul connectivity. Moreover, we find that a larger antenna downtilt angle is required for a higher deployment altitude. We show that the optimal size of the network area that maximizes the average coverage probability reduces, when the backhaul connectivity is considered. We also demonstrate that the average network performance can be significantly improved by judiciously selecting the number of untethered UAVs, which is closely related to the the size of the network area. Finally, the advantage of the proposed UAV-enabled heterogeneous network on the average ergodic rate has been validated by comparing it with two benchmarks under the 3GPP channel model.

An Interference-Oriented 5G Radio Resource Allocation Framework for Ultradense Networks

To cope with the explosive growth in demands of wireless network, ultradense network (UDN) technology is widely adopted, which could increase the capacity of wireless network, but also bring severe intercell interference (ICI). However, existing solutions cannot work well in such complex scenarios, due to the limits of their mechanisms. To solve the problem, in this article, an interference-oriented radio resource allocation framework is proposed with multiple usages, including supplying precise, stable, and timely performance feedbacks, near perfect offline training, and high compatibility. As the use of the framework is derived from precise interference identification, a practical regression-based interference modeling algorithm is proposed to support the framework. With in-depth analysis of the mechanism of interference, the proposed algorithm could efficiently and accurately model interference between users using only data collected from operating wireless networks. Compared with the baseline algorithm, the proposed algorithm could reach the same accuracy with training time of two orders of magnitude shorter. To further show the advantages of the framework, a high-performance double-deep- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network-based resource allocation algorithm is also proposed. By integrating into the proposed framework, the proposed algorithm could coordinate ICI better, with 40% to 101% higher energy efficiency compared with baseline algorithms.

Cell Edge User Capacity-Coverage Reliability Tradeoff for 5G-R Systems With Overlapped Linear Coverage

Fifth-Generation mobile networks for Railway (5G-R) is a promising train-ground communication solution to deal with the design challenges on ubiquitous connections and high reliability transmission for smart railways. Considering the linear coverage scenario along the railway lines, deep overlapping coverage can enhance the coverage reliability, but the capacity of cell edge users is plagued by severe inter-cell interference. In this paper, the fundamental performance of 5G-R systems with linear redundant coverage is investigated. We quantitatively analyze the impact of inter-cell interference on the capacity of cell edge users, in which the exponential effective signal to interference plus noise (SINR) mapping method is utilized to analyze SINR distribution of 5G-R users. Then, we investigate the coverage reliability considering the base station (BS) failure. Finally, taking cell edge user capacity as the optimization objective, we analyze the cell edge user capacity-coverage reliability tradeoff for 5G-R systems, and then provide useful insights into BS deployment for 5G-R systems through performance simulations and numerical results.

Edge-Aware Remote Radio Heads Cooperation for Interference Mitigation in Heterogeneous C-RAN

Heterogeneous cloud radio access network (H-CRAN) has been deemed as a promising architecture for the fifth generation wireless networks. In H-CRAN, small remote radio heads (SRRHs) are deployed underlaying the macro remote radio head (MRRH) to boost the spectral efficiency. H-CRANs with dense RRHs suffer from severe inter-cell interference due to the random deployment of dense SRRHs, which limits its performance. In this paper, we propose an Edge-Aware RRH-Cooperation (EARC) scheme to mitigate the impact of both the cross-tier and co-tier interference. We partition the users into two groups, Cell Edge Device (CED) and Non-cell Edge Device (NED). These two groups, CED and NED, are mapped into two association modes, dual-association and single-association mode, respectively. In single-association mode, the NED associates with the RRH that provides the maximum received signal. On the other hand, in the dual-association mode, the CED associates with the two strongest RRHs where the two RRHs may belong to the same or different tier(s). Using tools from stochastic geometry, we quantify the performance gains of the proposed EARC scheme in terms of outage probability and ergodic rate. To further show the efficacy of the proposed scheme, we compare it to four schemes developed in the literature.

A State Based Resource Allocation Game for Distributed Optimization in 5G Small-Cell Networks

The upcoming 5G advocates efficient utilization of the existing spectrum through cell densification using small cells. However, dense packing of small-cells over a given area experiences severe inter-cell interference and fierce competition for shared resources. In this paper, we address the topic of resource allocation in interference-prone 5G small-cell networks (SCNs). We formulate a state based potential game for resource sharing problems to fulfill and maximize users’ quality of experience (QoE). The designed game uses knowledge exchange among players through a communication graph to solve the optimization problem. We employ the time-invariant and time-varying structure of the communication graph in the proposed game. In addition, we propose a decentralized scheme for the joint optimization of spectrum allocation, power selection, and user scheduling. Through extensive simulations, we show that our algorithm surpasses the existing schemes in terms of QoE, computational complexity, and convergence rate for stable resource allocations. Furthermore, it is robust to wireless link failure and state transition in the communication graph. Besides, under imperfect channel state information, it suffers relatively less performance degradation compared to the existing schemes.

Joint Optimization of Base Station Activation and User Association in Ultra Dense Networks Under Traffic Uncertainty

In ultra-dense networks (UDNs), the dense deployment of base stations (BSs) is facing challenges due to the pronounced unbalanced traffic loads, severe inter-cell interference, and uncertain traffic demands. In this paper, we tame traffic uncertainty for the joint optimization of BS activation and user association in UDNs to mitigate interference and balance traffic loads among BSs. Specifically, we address the traffic uncertainty by using chance constraint programming with the known first- and second-order statistics of the uncertain traffic. We formulate the joint BS activation and user association problem as a mixed integer non-linear programming problem, which is then decomposed into a set of user association sub-problems by modeling the BS states (active or idle) as a Markov chain. We solve the user association sub-problem at each BS state by transforming it into a convex problem over the positive orthant. In particular, at each BS state, the candidate serving BSs that lead to the optimal load balancing performance are identified for each user and parts of the user's traffic are offloaded to the identified BSs. Based on the obtained solutions, we propose a distributed near-optimal BS activation and user association scheme. Numerical results demonstrate that our proposed scheme is more robust to traffic uncertainty and provides better load-balancing performance than the existing schemes.

Location-Based Resource Allocation in Ultra-Dense Network with Clustering.

With the rapid deployment of present-day mobile communication systems, user traffic requirements have increased tremendously. An ultra-dense network is a configuration in which the density of small base stations is greater than or equal to that of the user equipment. Ultra-dense networks are considered as the key technology for 5th generation networks as they can improve the link quality and increase the system capacity. However, in an ultra-dense network, small base stations are densely positioned, so one user equipment may receive signals from two or more small base stations. This may cause a severe inter-cell interference problem. In this study, we considered a coordinated multi-point scenario, a cooperative technology between base stations to alleviate the interference. In addition, to suppress the occurrence of severe interference at the cell edges, link formation was carried out by considering the degree of cell load for each cluster. After the formation of links between all the base stations and user equipment, a subcarrier allocation procedure was performed. The subcarrier allocation method used in this study was based on the location of base stations with clustering to improve the data rate and reduce the interference between the clusters. Power allocation was based on the channel gain between the base station and user equipment. Simulation results showed that the proposed scheme delivered a higher sum rate than the other resource allocation methods reported previously for various types of user equipment.

A QoS-based joint user association and resource allocation scheme in ultra-dense networks

Ultra-dense networks (UDNs) have become an important architecture for the fifth generation (5G) networks. A large number of small base stations (SBSs) are deployed to provide high-speed and seamless connections for users in the network. However, the advantage of increasing the system capacity brought by the dense distribution of SBSs comes at the cost of severe inter-cell interference. Although the user-centric virtual cell method has been proposed to solve the interference problem, some challenges have been encountered in practical applications. For example, inter-cell interference still exists to a certain extent, and the cell load may be imbalance. Hence, under the virtual cell architecture, we propose a quality of service (QoS)-based joint user association and resource allocation scheme in UDNs. In order to mitigate the interference, balance cell load and improve the system throughput, a non-convex NP-hard problem is formulated. To effectively solve this problem, we decouple the formulated problem into three sub-problems: user association, physical resource block (PRB) allocation and power allocation. First, we consider the QoS requirements of user equipment (UE) and perform user association based on the PRB estimation method. Then, based on the overlapped virtual cells constructed, we propose a graph-based PRB allocation scheme for reducing virtual inter-cell interference. Moreover, we solve power allocation sub-problem by using the difference of concave (DC) programing method. The simulation results show that our proposed scheme is superior to other schemes in terms of user rates, cell load and system throughput.

A Fast Terminal Matching Method for Interference Coordination Based on Wavelet Transform and Graph Theory in Ultra-Dense Multi-Cell Scenarios

The intensive deployment of cells in a wireless communication system may significantly increase the network capacity, but it may cause more complex and severe inter-cell interference. In order to effectively coordinate the interference, terminals in different cells are matched with the purpose of avoiding severe interference, called terminal matching, and a fast method is proposed in this article for multi-cell scenarios. By constructing a fully weighted graph of all the base stations with interferences as the weights, the minimum spanning tree method in graph theory is used to identify the coordination relationship among all the cells, and multiple coordination pairs can be obtained. For each coordination pair, terminal matching is modeled as a two-dimensional assignment problem between the coefficients after wavelet decomposition and Hungarian algorithm is applied to obtain their optimal match. Wavelet transform is used to extract the scale transformation coefficients describing the terminal features in each single cell, so that the optimization process can be accelerated because the amount of these coefficients is dramatically reduced after several layers of wavelet decomposition. The minimum Hamilton path method in graph theory is used to rank the terminals in each cell based on their key features, so that the coefficients lose as minimum useful information as possible during wavelet decomposition. Simulation results show that the performance of the proposed method reaches approximately the optimal matching obtained by traversing all the coordination possibilities among all the cells. Compared with the simulated annealing algorithm, the performance loss in terms of system spectral efficiency is only about 2%, but the proposed method requires only less than one thousandth of its computational time.

Location-Based Robust Beamforming Design for Cellular-Enabled UAV Communications

Cellular communications have been regarded as promising approaches to deliver high-broadband communication links for unmanned aerial vehicles (UAVs), which have been widely deployed to conduct various missions, e.g., precision agriculture, forest monitoring, and border patrol. However, the unique features of aerial UAVs, including high-altitude manipulation, 3-D mobility, and rapid velocity changes, pose challenging issues to realize reliable cellular-enabled UAV communications, especially with the severe intercell interference generated by UAVs. To deal with this issue, we propose a novel position-based robust beamforming algorithm through complementarily integrating the navigation information and wireless channel information to improve the performance of cellular-enabled UAV communications. Specifically, in order to achieve the optimal beam weight vector, the navigation information of the UAV system is innovatively exploited to predict the changes of the direction-of-Arrival (DoA) angle. To fight against the high mobility of UAV operations, an optimization problem is formed by considering the tapered surface of DoA angle and solved to correct the inherent position error. Comprehensive simulation experiments are conducted and the results show that the proposed robust beamforming algorithm could achieve over 90% DoA estimation error reduction and up to 14-dB SINR gain compared with five benchmark beamforming algorithms, including linearly constrained minimum variance (LCMV), position-based beamforming, diagonal loading (DL), robust capon beamforming (RCB), and robust LCMV algorithm.

UE throughput guaranteed small cell on/off algorithm with machine learning

Ultra-dense network (UDN) has been considered a promising solution to improve the signal quality of cell edge UEs and enhance the serving coverage. However, a large number of small cells causes severe inter-cell interference and high energy consumption, and efficient small cell management is an important issue. In this paper, a UE throughput guaranteed small cell on/off algorithm in UDN environment is proposed to solve the problem of UE throughput reduction due to small cell off process. The proposed small cell on/off algorithm with machine learning is executed by the following processes: First, the network attributes that affect UE throughput are analyzed. Second, the correlation between UE throughput and network attributes is evaluated through multiple linear regression analysis. Third, through understanding the correlation between UE throughput and network attributes, we determine the proper criteria for small cell on/off process. Simulation results show that the proposed small cell on/off algorithm can improve the total network energy efficiency as well as efficiently ensure sufficient UE throughput. Compare to the conventional algorithm, the proposed algorithm shows more than 75% improvements of average network energy efficiency. whole network.

Decentralized Learning Based Indoor Interference Mitigation for 5G-and-Beyond Systems

Due to the explosive growth of data traffic, and poor indoor coverage, ultra-dense network (UDN) has been introduced as a fundamental architectural technology for 5G-and-beyond systems. As the telecom operator is shifting to a plug-and-play paradigm in mobile networks, network planning, and optimization become difficult, and costly, especially in residential small-cell base stations (SBSs) deployment. Under this circumstance, severe inter-cell interference (ICI) becomes inevitable. Therefore, interference mitigation is of vital importance for indoor coverage in mobile communication systems. In this paper, we propose a fully distributed self-learning interference mitigation (SLIM) scheme for autonomous networks under a model-free multi-agent reinforcement learning (MARL) framework. In SLIM, individual SBSs autonomously perceive surrounding interferences, and determine downlink transmit power without necessity of signaling interactions between SBSs for mitigating interferences. To tackle the dimensional disaster of joint action in the MARL model, we employ the Mean Field Theory to approximate the action value function to greatly decrease the computational complexity. Simulation results based on 3GPP dual-stripe urban model demonstrate that SLIM outperforms several existing known interference coordination schemes in mitigating interference, and reducing power consumption while guaranteeing UEs’ quality of service for autonomous UDNs.

Coalition Formation Games for Coordinated Service in Realistic Small Cell Propagation Topologies

The densification of modern wireless networks into a dense ecosystem of small cells imposes challenges for the reliable service and high quality of experience of its users as it can result into severe intercell interference, especially for users scattered on the cell edges. Joint Transmission Coordinated Multipoint (JT-CoMP) is a technique that can be deployed to form cooperating clusters of transmission points, enabling them to jointly transmit data to significantly mitigate this type of interference for these users. However, JT-CoMP stresses the backhaul links and radio resources are limited, meaning that, with incautious clustering, the data rates for cell edge users may not improve, while the data rates for the non-cell edge users may severely decrease. To tackle these drawbacks, a dynamic coalition formation algorithm is proposed to form the appropriate transmission point clusters to implement JT-CoMP. Furthermore, to ensure reliable service for all the network’s users, the case where JT-CoMP is enhanced with the capability to serve users based on their selected application is examined. The proposed scheme’s adaptability and capability to increase cell edge user throughput is then tested and compared to the non JT-CoMP case, a JT-CoMP scheme with static clustering and a JT-CoMP scheme with greedy clustering for a user mobility scenario. To obtain more reliable and accurate results of the JT-CoMP deployment, physical layer parameters retrieved from a fully deterministic physical layer radio planning tool (TruNET wireless) are imported for our simulations.

On the Performance Gain of NOMA Over OMA in Uplink Communication Systems

In this paper, we investigate and reveal the ergodic sum-rate gain (ESG) of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in uplink cellular communication systems. A base station equipped with a single-antenna, with multiple antennas, and with massive antenna arrays is considered both in single-cell and multi-cell deployments. In particular, in single-antenna systems, we identify two types of gains brought about by NOMA: 1) a large-scale near-far gain arising from the distance discrepancy between the base station and users; 2) a small-scale fading gain originating from the multipath channel fading. Furthermore, we reveal that the large-scale near-far gain increases with the normalized cell size, while the small-scale fading gain is a constant, given by $\gamma$ = 0.57721 nat/s/Hz, in Rayleigh fading channels. When extending single-antenna NOMA to $M$-antenna NOMA, we prove that both the large-scale near-far gain and small-scale fading gain achieved by single-antenna NOMA can be increased by a factor of $M$ for a large number of users. Moreover, given a massive antenna array at the base station and considering a fixed ratio between the number of antennas, $M$, and the number of users, $K$, the ESG of NOMA over OMA increases linearly with both $M$ and $K$. We then further extend the analysis to a multi-cell scenario. Compared to the single-cell case, the ESG in multi-cell systems degrades as NOMA faces more severe inter-cell interference due to the non-orthogonal transmissions. Besides, we unveil that a large cell size is always beneficial to the ergodic sum-rate performance of NOMA in both single-cell and multi-cell systems. Numerical results verify the accuracy of the analytical results derived and confirm the insights revealed about the ESG of NOMA over OMA in different scenarios.

Least Pair-Wise Collision Beam Schedule for mmWave Inter-Cell Interference Suppression

The narrow beamwidth characteristic in millimeter wave (mmWave) makes the level of inter-cell interference depend on the beams used by adjacent cells, which indicates that a well-designed schedule of the beams in neighboring cells can reduce the inter-cell interference. In this paper, we investigate a time-domain mmWave beam scheduling problem for interference suppression in a two-cell scenario, where each cell may transmit one or multiple beams simultaneously. Specifically, the beams are one-to-one paired between two adjacent cells, and the simultaneous transmission of two beams in one pair can cause severe inter-cell interference, which is referred to as a pair-wise collision. Instead of optimizing the sum rate of network, we minimize the number of pair-wise collision time slots under the constraint of the service demand of each beam. In this way, the channel state information is not required, and the global optimal solution can be found by our proposed least pair-wise collision (LPC) algorithm, which is a recursive algorithm with linear computational complexity. After that, we extend LPC algorithm to more complicated models, including an asymmetric model and a full-dimension model. Finally, simulations are conducted to verify the efficiency of our methodology.

Randomized Two-Timescale Hybrid Precoding for Downlink Multicell Massive MIMO Systems

Although massive multiple-input multiple-output (MIMO) promises high spectral efficiency, there are several issues that significantly limit the potential gain of massive MIMO, such as severe inter-cell interference, huge channel state information (CSI) overhead/delay, high cost and power consumption of RF chains, and user fairness. Several precoding schemes have been proposed for massive MIMO but there still lacks a systematic solution to address all of the aforementioned issues especially for the multicell case. In this paper, we propose a randomized two-timescale hybrid precoding (RTHP) scheme for the downlink transmission of multicell massive MIMO systems. RTHP allows time sharing among multiple sets of analog precoders to achieve better tradeoff between sum throughput and fairness. Moreover, the time-sharing factors and analog precoders are adapted to the long-term channel statistics to mitigate inter-cell interference with reduced CSI overhead and RF chains. The digital precoder is used to support multiuser MIMO at each base station based on local low-dimensional effective CSI only. As such, RTHP resolves all of the aforementioned issues. We formulate the optimization of RTHP as a general network utility maximization problem (GNUMP), which is a two-timescale stochastic non-convex problem, and consequently challenging to solve. By exploiting some approximation techniques, we convert the problem into a more tractable form and subsequently develop an online algorithm, which is shown to converge to stationary solutions of the approximate GNUMP. Finally, simulation results verify the advantages of the proposed scheme over baseline schemes.

An Efficient Pilot Assignment Scheme for Addressing Pilot Contamination in Multicell Massive MIMO Systems

The reuse of the same pilot group across cells to address bandwidth limitations in a network has resulted in pilot contamination. This causes severe inter-cell interference at the targeted cell. Pilot contamination is associated with multicell massive multiple-input multiple-output (MIMO) systems which degrades the system performance even when extra arrays of antennas are added to the network. In this paper, we propose an efficient pilot assignment (EPA) scheme to address this issue by maximizing the minimum uplink rate of the target cell’s users. To achieve this, we exploit the large-scale characteristics of the fading channel to minimize the amount of outgoing inter-cell interference at the target cell. Results from the simulation show that the EPA scheme outperforms both the conventional and the smart pilot assignment (SPA) schemes by reducing the effect of inter-cell interference. These results, show that the EPA scheme has significantly improved the system performance in terms of achievable uplink rate and cumulative distribution function (CDF) for both signal-to-interference-plus-noise ratio (SINR), and uplink rate.

Multiple Access Design for Ultra-Dense VLC Networks: Orthogonal vs Non-Orthogonal

Small-cell aided ultra-dense networks (UDNs) constitute an efficient solution to the ever-increasing thirst for more data. Thanks to the vast untapped high-frequency spectrum of visible light, visible light communications (VLCs) are a natural candidate for UDN. In this paper, layered asymmetrically clipped optical OFDM (LACO-OFDM) aided ultra-dense VLC (UD-VLC) is investigated in terms of its user association, multiple access (MA), and resource allocation. To handle the severe inter-cell interference (ICI) amongst the densely deployed access points, we propose a novel overlapped clustering technique relying on a hybrid non-orthogonal MA and orthogonal MA scheme for enhancing the performance, with the aid of our dynamic resource allocation conceived. Our simulations show that the proposed LACO-OFDM aided UD-VLC using our hybrid MA scheme is more robust against the ICI, at a price of modestly decreasing the sum throughput.

Mobility-Aware Transmit Beamforming for Ultra-Dense Networks With Sparse Feedback

The network densification brings favored channel gain. However, its performance is limited by the increased interference and feedback overhead. In this paper, we focus on the channel state information (CSI) measure error induced by feedback delay and study the effect of the uplink feedback on the downlink transmission performance in ultra-dense networks (UDNs). Using a mobility-aware sparse feedback scheme (MSFS), we analyze the feedback transmission delay and feedback waiting delay separately, considering the impact of CSI feedback probability and measurement range. To capture the effect of proposed MSFS, the coverage probability for a typical mobile user is derived, where the imperfect-CSI-based zero-forcing precoding is employed to mitigate the severe inter-cell interference in UDNs. Numerical results reveal that MSFS succeeds in providing the effective CSI for downlink precoding while keeping a lower feedback load, and the optimal feedback probability and measurement range are provided for diverse mobility cases and different network densities.