Inter-cell Interference Research Articles

Joint Pilot and Data Power Control for Cell-Free Massive MIMO IoT Systems

Although cell-free massive multiple-input–multiple-output (mMIMO) Internet-of-Things (IoT) systems can improve spectral efficiency (SE) significantly compared with conventional cellular mMIMO IoT systems due to high coverage and no intercell interference, the pilot contamination and active sensors (ASs) interference can still seriously limit the further improvement of SE. In this article, we first develop a model of the cell-free mMIMO IoT system and then derive a mathematical expression of the SE to formulate a maximized sum SE optimization problem for jointly controlling pilot and data power. This problem is nonconvex; thus, a successive convex approximation (SCA) iteration algorithm is proposed, which, in fact, approximates the objective and constraint function in each iteration, and converts the initial optimization problem into an easy-to-solve geometric programming (GP) problem. Finally, simulation results show that our proposed algorithm outperforms the existing algorithms related to power control optimization in terms of the sum SE performance.

Recent Trends in Fractal Antenna Research for In-body Communications

Fractal structure is a unique geometry that can be seen in many objects in nature, such as clouds, coastlines, DNA, trees, and even pineapple. This structure has manifold geometries, self-similarities, and space-filling properties. Due to these properties, fractal geometries are preferred to miniaturize an antenna in wireless communications. There are many cases that require a small compact antenna, including in-body communications. In this article, we present a review of the recent trends and advancements in fractal antenna research, especially in the miniaturization of implantable antennas for in-body communications. The review is derived from articles that are gathered from online libraries such as IEEE, PubMed, Nature, MDPI, Elsevier, and Google Scholar. As a result, we have collected more than 60 articles related to fractal-implantable antenna and in-body communications. Indeed, many researchers have proposed an implantable compact antenna with fractal geometries in the last decades. Fractal geometry allows a longer electrical length to be routed in a smaller area of the antenna. However, several things remain challenging in designing a fractal antenna, including bandwidth, fabrication complexity, and intercell interference.

Writing on dirty flash memory: Combating inter-cell interference via coding with side information

High-density flash memories suffer from inter-cell interference (ICI) which threatens the reliability of stored data. In order to cope with the ICI problem, we propose a channel coding scheme with channel state information of flash memories (i.e., side information of ICI). This side information is obtained before writing data into flash memories and incorporated during the encoding stage. We show that flash memories under ICI problem can be transformed into the model of memory with defective cells due to the unique asymmetry property between write (page write) and erase (block erase) operations. Then, the channel coding for memory with defective cells is employed to combat ICI. Simulation results support that the proposed scheme with the ICI side information can effectively improve the decoding failure probability.

Knowledge Transfer-Based Multiagent Q-Learning for Medium Access in Dense Cellular Networks

Inter-cell interference in dense cellular networks causes severe network performance degradation because the base stations (BSs) and devices cannot fully utilize all the network status information in practice. Reinforcement learning enables the wireless network to find an effective transmission strategy with limited information and the network entities to operate in a distributed manner. We herein propose a medium access algorithm based on multi-agent reinforcement learning by exchanging knowledge of transmissions from other cells. Specifically, a state mapping function between the device and the BS and a transfer value iteration are proposed. The BS transfers reward processes derived from inter-cell interference so that the partially observed medium of devices is expanded to monitor entire networks. Extensive simulation results show that simple knowledge transfer can help performance improvement in terms of network stability and throughput.

Joint association and beamforming optimization in reconfigurable intelligent surface-enhanced user-centric networks

Fully coordinated Cell-Free (CF) networks can alleviate the Inter-Cell Interference (ICI) for the cell-edge users in cellular networks. Due to the complex topology of the association between the Access Points (APs) and the users in CF networks, it is challenging to deploy CF networks in practical scenarios. In order to make CF networks feasible, we introduce User-Centric (UC) networks enabling each user served by a limited number of APs. As a low-cost and energy-efficient technology, Reconfigurable Intelligent Surface (RIS) can be embedded in UC networks to further improve the system performance. First, we provide a brief survey on the prior works in UC networks for clear comprehension. Then, we formulate a Spectral Efficiency (SE) maximization problem for RIS-enhanced UC networks. For solving the non-convex problem, we divide it into three subproblems and propose a joint optimization framework for optimizing AP-user association, active beamforming of multiple antennas at the APs, and the passive beamforming of the RIS. Besides, a channel gain based association method coupled with the design of RIS is proposed to construct a dynamic and efficient association. The subproblems about optimizing active and passive beamforming are solved with the fractional programming. Simulation results show that the proposed joint optimization framework for RIS-enhanced UC networks can obtain good SE compared with other benchmark schemes.

Improvements for Uplink Long Term Evolution (UL-LTE) in Heterogeneous Network using Dynamic Spectrum Allocation Technique

Several interference mitigation strategies can be used in LTE networks to meet the consumers' increasing need for faster data speeds. Dynamic Spectrum Allocation (DSA), which maintains spectrum in a converged radio system and distributes it across all participating radio terminals, is one of the most promising solutions. However, the fundamental obstacle to achieving increased network capacities is increased inter-cell interference. The aim of this paper is to analyse the effectiveness of a newly proposed DSA technique by comparing its bit error rate (BER) and throughput (TP) with a typical uplink LTE configuration. To maximise throughput in a heterogeneous network, the suggested DSA technique utilises the Nash Bargaining Solution (NBS) scheme, a cooperative game theory that could correlate with the bit error rate. The software tool GNU Radio, which offers signal-processing blocks to develop softwaredefined radios and signal-processing systems, was used to create the setups. After the simulations were performed, the BER values were recorded and compared, while the TP values were established and computed appropriately. According to the results, the uplink (UL-LTE) architecture with DSA has an improved TP and a lower BER value. As a result, spectral efficiency can also be improved.

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.

Design of Sparse Uniform Linear Array Beamformer using Modified FRM Structure for Varied Applications

This paper presents a method to generate antenna patterns for a Uniform Linear Array (ULA) having narrow beamwidth and low sidelobe levels (SLL) using the recently proposed Modified FRM (ModFRM) architecture. This allows it to direct the beams to specific ground cells for communications while mitigating inter-cell interference. The sharpness of the beam pattern defines the spatial discriminating performance of a ULA beamformer, while the SLL dictates the interference and noise suppression capabilities. Typically, a conventional ULA beamforming will demand high computational complexity and a large number of sensors to satisfy these requirements. Hence to reduce the system cost, using the ModFRM technique a sparse array is developed. With this strategy, the total number of sensors is drastically reduced compared to conventional ULA beamformers. The designed beamformers can be used in applications with stringent requirements where cost and size are concerned.

Ambient BackCom in beyond 5G NOMA networks: A multi-cell resource allocation framework

The research of Non-Orthogonal Multiple Access (NOMA) is extensively used to improve the capacity of networks beyond the fifth-generation. The recent merger of NOMA with ambient Backscatter Communication (BackCom), though opening new possibilities for massive connectivity, poses several challenges in dense wireless networks. One such challenge is the performance degradation of ambient BackCom in multi-cell NOMA networks under the effect of inter-cell interference. Driven by providing an efficient solution to the issue, this article proposes a new resource allocation framework that uses a duality theory approach. Specifically, the sum rate of the multi-cell network with backscatter tags and NOMA user equipment is maximized by formulating a joint optimization problem. To find the efficient base station transmit power and backscatter reflection coefficient in each cell, the original problem is first divided into two subproblems, and then the closed form solution is derived. A comparison with the Orthogonal Multiple Access (OMA) ambient BackCom and pure NOMA transmission has been provided. Simulation results of the proposed NOMA ambient BackCom indicate a significant improvement over the OMA ambient BackCom and pure NOMA in terms of sum-rate gains.

Traffic-Aware Mean-Field Power Allocation for Ultradense NB-IoT Networks

The narrowband Internet of Things (NB-IoT) is a cellular technology introduced by the third-generation partnership project (3GPP) to provide connectivity to a large number of low-cost Internet of Things (IoT) devices with strict energy consumption limitations. However, in an ultradense small cell network employing NB-IoT technology, intercell interference can be a problem, raising serious concerns regarding the performance of NB-IoT, particularly in uplink transmission. Thus, a power allocation method must be established to analyze uplink performance, control and predict intercell interference, and avoid excessive energy waste during transmission. Unfortunately, standard power allocation techniques become inappropriate as their computational complexity grows in an ultradense environment. Furthermore, the performance of NB-IoT is strongly dependent on the traffic generated by IoT devices. In order to tackle these challenges, we provide a consistent and distributed uplink power allocation solution under spatiotemporal fluctuation incorporating NB-IoT features, such as the number of repetitions and the data rate, as well as the IoT device’s energy budget, packet size, and traffic intensity, by leveraging stochastic geometry analysis and mean-field game (MFG) theory. The effectiveness of our approach is illustrated via extensive numerical analysis, and many insightful discussions are presented.

Self-Adaptive Game Theoretic Approaches for Resource Allocation in SC-FDMA Based Heterogeneous Networks

We investigate the resource blocks (RBs) allocation problem in the Single-Carrier Frequency Division Multiple Access (SC-FDMA) based heterogeneous uplink networks from a game-theoretical viewpoint. First, a general network model that considers the co-tier, cross-tier, and inter-cell interference caused by the co-channel deployment is presented. Then, we design an effective throughput-optimizing utility function and prove it an exact potential game. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\gamma$</tex-math></inline-formula> -logit is a learning algorithm which commonly used to achieve the optimal solution in potential game and is very sensitive to the changes of the network structures, making it hard in practice. To obtain the optimal solution under the different network structures stably, we propose a Self-adaptive logit algorithm, which can achieve the optimal solution automatically and is a variant of the well-established <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\gamma$</tex-math></inline-formula> -logit learning algorithm. Additionally, to speed up the convergence, we propose the corresponding suboptimal algorithm based on the better response principle. Simulation results show that the proposed Self-adaptive logit algorithm is throughput optimal and network structure adaptive, outperforming the existing Binary-logit, Max-logit, and Round Robin algorithm. Moreover, the proposed suboptimal algorithm is near-optimal throughput achieved and converges efficiently. In addition, we also study the impact of the initial point selection on our proposed two alogithms.

Online Probabilistic Activation Control of Base Stations Utilizing Temporal System Throughput and Activation States of Neighbor Cells for Heterogeneous Networks

In this paper, we propose an online probabilistic activation/deactivation control method for base stations (BSs) in heterogeneous networks based on the temporal system throughput and activation states of neighbor BSs (cells). The conventional method iteratively updates the activation/deactivation states in a probabilistic manner at each BS based on the change in the observed system throughput and activation/deactivation states of that BS between past multiple consecutive discrete times. Since BS activation control increases the system throughput by improving the tradeoff between the reduction in inter-cell interference and the traffic off-loading effect, the activation of a BS whose neighbor BSs are deactivated is likely to result in improved system performance and vice versa. The proposed method newly introduces a metric, which represents the effective ratio of the activated neighbor BSs considering their transmission power and distance to the BS of interest, to the update control of the activation probability. This improves both the convergence rate of the iterative algorithm and throughput performance after convergence. Computer simulation results, in which the mobility of the user terminals is taken into account, show the effectiveness of the proposed method.

QoE-Driven Distributed Resource Optimization for Mixed Reality in Dynamic TDD Systems

With the full development of intelligent mobile communications, wireless mixed reality (MR) provides a more visually immersive experience and stronger interaction with environments than virtual reality (VR) and augmented reality (AR). However, the asymmetric characteristic of wireless MR traffic creates a huge challenge to current mobile networks. Dynamic time division duplex (D-TDD) is considered as a promising technology to improve wireless MR users’ quality of experience (QoE) due to its potentials and advantages in delivering asymmetric traffic. Therefore, in this paper, we propose a QoE-driven distributed multidimensional resource allocation (MRA) supplemented by inter-cell interference (ICI) mitigation scheme for wireless MR in multi-cell D-TDD systems. First, to improve QoE of MR users, we formulate the joint optimization of subframe configuration, channel assignment and computation offloading as a mixed-integer nonlinear programming problem. A novel fully-decentralized multi-agent deep Q-network (DQN) algorithm is developed to solve the problem. Then, to mitigate ICI, a water filling based power control algorithm is investigated to minimize the total power of each small base station and its associated MR users. Simulation results demonstrate that our proposed scheme improves QoE of MR users in a realizable way as compared to existing schemes.

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.

An Analysis of Distributed Joint Spectrum Resource Allocation With Power Control in Ultra-Dense Cellular Networks

In the view of spatial statistical average, when users are associated with their nearest small base stations (SBSs), they are mainly interfered by their second closest SBSs, especially for edge users. To reduce inter-cell interference (ICI) caused by the second closest SBSs, a distributed joint spectrum resource allocation strategy with power control in the downlink of one-tier ultra-dense networks (UDNs) is proposed in this paper. According to the distance from users to their nearest SBSs, users are divided into inner users and edge users. We then try to make the subchannel randomly allocated so they be different from that of edge users of their second closest SBS. Meanwhile the transmission power of SBSs on different subchannels is adjusted. Using stochastic geometry we derive the analytical expressions of the meta distribution of signal-to-interference ratio (SIR) to obtain more fine-grained information such as the mean and variance of conditional success probability, the local delay. Furthermore, we revise and redefine the area spectrum efficiency (ASE) and energy efficiency (EE) to accurately evaluate the benefits. Compared with the traditional random resource allocation strategy, which does not exploit resource allocation and interference coordination scheme, the proposed strategy can balance the performance of users and improve the whole network EE. Our analytical results are validated through simulation.

CoMP-Assisted NOMA and Cooperative NOMA in Indoor VLC Cellular Systems

In this paper, we investigate the dynamic power allocation for a visible light communication (VLC) cellular system consisting of two coordinating attocells, each equipped with one access-point (AP). The coordinated multipoint (CoMP) between the two cells is introduced to assist users experiencing high inter-cell-interference (ICI). Specifically, the coordinated zero-forcing (ZF) precoding is used to cancel the ICI at the users located near the centers of the cells, whereas the joint transmission (JT) is employed to eliminate the ICI at the users located at the edge of both cells and to improve their receptions as well. Furthermore, two multiple access techniques are invoked within each cell, namely, non-orthogonal-multiple-access (NOMA) and cooperative non-orthogonal-multiple-access (C-NOMA). Hence, two multiple access techniques are proposed for the considered multi-user multi-cell system, namely, the CoMP-assisted NOMA scheme and the CoMP-assisted C-NOMA scheme. For each scheme, two power allocation frameworks are formulated each as an optimization problem, where the objective of the former is maximizing the network sum data rate while guaranteeing a certain quality-of-service (QoS) for each user, whereas the goal of the latter is to maximize the minimum data rate among all coexisting users. The formulated optimization problems are not convex, and hence, difficult to be solved directly unless using heuristic methods, which comes at the expense of high computational complexity. To overcome this issue, optimal and low complexity power allocation schemes are derived. In the simulation results, the performance of the proposed CoMP-assisted NOMA and CoMP-assisted C-NOMA schemes are compared with those of the CoMP-assisted orthogonal-multiple-access (OMA) scheme, the C-NOMA scheme and the NOMA scheme, where the superiority of the proposed schemes are demonstrated. Finally, the performance of the proposed schemes and the considered baselines is evaluated while varying various system parameters.

Cooperative Scheduler to Enhance Massive Connectivity in 5G and Beyond by Minimizing Interference in OMA and NOMA

The fifth-generation (5G) and beyond 5G (B5G) wireless networks introduced massive machine-type communications (mMTC) to cope with the growing demand of massive Internet of Things (IoT) applications. However, the heterogeneous characteristics of massive IoT and diverse quality of service (QoS) requirements may lead to severe interference that could degrade the expected QoS of the cellular ecosystem. Therefore, this article studies the impact of interference caused by mMTC connections. We theoretically model the intercell interference (ICI) minimization problem for the existing orthogonal multiple access (OMA) technique and propose its corresponding solution. Furthermore, we jointly solve the ICI and the cochannel interference minimization problem for the IoT users when the nonorthogonal multiple access (NOMA) technique is used. For the proposed OMA and NOMA schemes, we design a cooperative scheduler to reduce the impact of such interference. The results show that our proposed schemes provide up to 58%, 75%, and 100% more improvements in terms of user’s data rates, energy consumption, and connection density, respectively.

A Multi-Class Channel Access Scheme for Cognitive Edge Computing-Based Internet of Things Networks

Edge computing-based framework is capable of improving users’ quality of experience in cognitive Internet of Things (IoT) networks. To explore the advantages of this edge computing-based framework, possible offloading and processing delay resulting from computation bottlenecks, and the offloading latency caused due to inter-cell interference must be properly considered. This paper thus considered a multi-class channel access mechanism for cognitive edge computing-based IoT networks where IoT users were categorized based on their quality of experience requirements. Essential IoT devices are permitted to offload to the edge server at any time following the hybrid channel access model, while delay-tolerant IoT devices are only permitted to offload to the server when the channel is idle following the overlay channel access model. Analyses were obtained for transmission rate and offloading delay to demonstrate the performance of the proposed mechanism, while important metrics such as total offloading latency and total offloading cost were investigated. The total offloading costs were formulated through the mixed strategy Nash equilibrium method. The proposed mechanism achieves lower offloading latencies and costs for both type 1 and type 2 CUs when compared with existing methods. The obtained results showed that multi-class channel access mechanisms can reduce packet offloading delay in cognitive edge computing-based IoT networks.

Meta-Wall: Intelligent Omni-Surfaces Aided Multi-Cell MIMO Communications

Recently, reconfigurable intelligent surfaces (RISs) have been proposed as a novel solution to enhance wireless communications such as suppressing inter-cell interference. However, signals arriving at a conventional reflecting-type RIS can only be reflected towards one side, leading to a limited service coverage, especially in an indoor environment involving potential obstacles. In this paper, we consider an intelligent omni-surface (IOS) which can provide services for users on both sides by enabling simultaneous signal reflection and transmission. Specifically, we propose an IOS aided indoor communication system where an IOS is embedded in a wall between two independent access points (APs) to suppress inter-cell interference. Due to the independence of the APs, we design a distributed hybrid beamforming scheme consisting of digital beamforming at APs and IOS-based analog beamforming to maximize the sum rate without any exchange of channel state information (CSI) between APs. Simulation results indicate that the proposed system performs very close to an optimal centralized scheme, and has a better sum rate performance compared to existing schemes.

Interference Management Strategies for Multiuser Multicell MIMO VLC Systems

This paper investigates different precoding strategies for rate splitting multiple access (RSMA) in the downlink of multi-cell visible light communication (VLC) networks. Since classical Shannon capacity formula does not hold for VLC, we first provide a lower bound on the channel capacity for RSMA in such interfering networks. Then, we formulate a spectral efficiency maximization problem to jointly find the optimal rate-splitting and transmit precoding. Beside that, since cell-edge users suffer from additional inter-cell interference, we propose to design the precoders of different RSMA signals utilizing coordinated beamforming (CB). Subsequently, aiming to improve the performance of the CB design for RSMA, while maintain a reduced complexity, we introduce two enhanced precoding strategies for RSMA. To the best of the authors’ knowledge such a contribution has not been considered before in the open literature. It is shown in the paper that the formulated optimization problem is non-convex and a sub-optimal, yet, a low complexity solution can be obtained efficiently using semi-definite relaxation combined with successive convex approximation. Through analytical results, we illustrate the flexibility and superiority of the proposed precoding strategies for RSMA over conventional coordinated space division multiple access and non-orthogonal multiple access for different scenarios and network loads.