Multi-cell Systems Research Articles

Molecular modification and assembly at cell surface for biochemical analysis

<p indent=0mm>Nature has presented multi-scale, multi-level, and multi-functionally assembled systems during evolution, from single-cell to the higher organisms of multi-cell systems. In the course of both physiological and disease processes, cells carry out a series of molecular events at their surface to promote intracellular communication, thus driving cell proliferation, differentiation and migration. A collection of these interacted cells of different types can help to construct a living system with complete functions. Therefore, the development of molecular modification and assembly technologies at cell surface is the key to explore cell interfacial behavior at the molecular level, which may help to understand interfacial molecular events and biochemical reactions. Meanwhile, molecular modification and assembly technologies can greatly promote the advance of many research and application fields, including biosensing, disease diagnosis, translational medicine, and tissue engineering. Therefore, this review will present the latest research progress, so as to systematically describe the use of molecular probes and assembled materials at the cell surface, such as DNA and peptides, and thus give an outlook on their prospects for analytical applications.

IoT based FRAN Architecture using Cloud and Edge Detection System

A multi-cell Fog-Radio Access Network (F-RAN) architecture that takes into consideration the noisy interference from Internet of Things (IoT) devices and transmission takes place in the uplink with grant-free access. An edge node is used to connect the devices present in every cell and will hold a reasonable capacity in the central processor. The reading obtained from the IoT devices are used to determine the field of correlated Quality of Interests in every cell, transmitting using the Type-Based Multiple Access (TBMA) protocol. This is in contrast to the conventional protocols that are used for diagnostic purpose. In this proposed work, we have implemented the multi-cell F-RAN using cloud or edge detection in analysing the form of information-centric radio access. In a multi-cell system, cloud and edge detection are implemented and analysed. We have implemented model-based detectors and the probability of error for the asymptotic behavior in edge as well as cloud is determined. Similarly, cloud and edge detectors that are data driven are used when statistical models are not available.

SVM‐based online learning for interference‐aware multi‐cell mmWave vehicular communications

This paper proposes a data-driven method of mmWave beam selection in multi-cell systems to achieve a near-optimal fast beam allocation with low complexity. In particular, an online learning algorithm based on support vector machine (SVM) equipped with the radial basis function kernel, namely SVM-based online beam selection (SBOS) algorithm is proposed. The proposed algorithm starts with an adaptive beam selection process for certain traffic pattern that uses an SVM learning model to adaptively refine the beam selection strategy. Specifically, SVM-based model labels the feedback (the average information rate) from the cellular system, then learns from samples, and makes the scheme space smaller by maximising samples' minimum distances to all labelled samples in the sample space constrained by newly learned boundaries. Then, according to the aggregated data about the traffic patterns and the performance of corresponding beam selection strategy, SBOS algorithm exploits beam selection schemes recorded in the database or explores new schemes for unknown situations, respectively, and how to tune the hyperparameters for the SBOS algorithm is discussed. Furthermore, the extensive simulation results show that the proposed algorithm achieves a better performance versus upper confidence bound and Random methods.

Investigation of Ideal Number User Terminals with Spectrum Efficiency in Next Generation Wireless Communication Systems

Suppose that a multi-user multiple-input multiple-output (MIMO) system is developed from scratch to equally envelop a defined region with optimal spectrum efficiency (SE) in next generation wireless communication systems such as sixth-generation (6G) and beyond networks. What are the ideal number of user terminals U, number of base stations antennas, and used pilot reuse factor? The purpose of this paper is to address this specific issue. Three interference levels are specified for this. Based on these interference levels, signal-to-interference-and-noise ratios (SINRs) are extracted. Closed-form spectrum efficiency equations are thus obtained. As a function of the base station (BS) antenna number, simulations are carried out considering multiple pilot reuse factors and diverse processing schemes such as Maximum Ratio Combining (MRC) and Zero-Forcing (ZF). From the results, it is understood that U varies according to the processing schemes. Therefore, evaluating the results considering the fixed number of users K will not give an accurate result in determining the design parameters for the next generation communication systems. In general, these results are useful statements that spectrum efficiency is maximized when the ideal number of users U is used in multi-cell systems.

Sparse Activity Detection in Multi-Cell Massive MIMO Exploiting Channel Large-Scale Fading

This paper studies the device activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system, in which the active devices transmit signature sequences to multiple base stations (BSs) that are connected to a central unit (CU), and the BSs cooperate across multiple cells to detect the active devices based on the sample covariance matrices at the BSs. This paper demonstrates the importance of exploiting the knowledge of channel large-scale fadings in this cooperative detection setting through a phase transition analysis, which characterizes the length of signature sequences needed for successful device activity detection in the massive MIMO regime. It is shown that when the large-scale fadings are known, the phase transition for the multi-cell scenario is approximately the same as that of a single-cell system. In this case, the length of the signature sequences required for reliable activity detection in the multi-cell system can be made to be independent of the number of cells through cooperation, in contrast to the case where the large-scale fadings are not known. Further, this paper considers the case in which the fronthaul links between the BSs and the CU have capacity constraints and proposes a novel cooperation scheme based on the quantization of preliminary detection results at the BSs and the reconstruction of the sample covariance matrices at the CU. Simulations show that the proposed method significantly outperforms the scheme of directly quantizing the sample covariance matrices.

Human Blood-Brain-Barrier In Vitro Models: Overview and Applications.

The human blood-brain-barrier (BBB) is a vital structure for brain health. Conversely it represents a challenge in drug development programmes that require breaching of the barrier in order to access the central nervous system. Very often brain disorders have early dysfunction of the BBB implicating an important role in pathogenesis and disease progression. The development of human in vitro models is a major advance to allow experimental studies and screening assays, although there remain outstanding questions for the field. In this chapter, the current state of the art will be reviewed, with the complementary innovative approaches to in vitro modelling described, from simple 2D-cultures to more complex multi-cell type micro-physiological systems.

A Smart Pilot Assignment in Multi-Cell Massive MIMO Systems Using Virtual Modeling of Assigning Cost

In this paper, a smart pilot sequence assignment method is proposed to minimize inter-cell interference generated in a massive multi-input multi-output (MIMO) system due to pilot contamination in uplink TDD (Time Division Duplex) mode. The proposed method employs a zero-one integer linear programming method as the assignment algorithm. The amount of intercell interference imposed on the target cell users is considered as assigning cost. The introduced assigning cost is composed of the steering vectors of the desired users in the target cell, and the sum of the channel correlation matrices of interference users in adjacent cells. By exploiting the virtual channel modeling technique, we develop a simple alternative approach to calculate the assigning cost, which has low complexity. We prove that the new assigning cost, called virtual assigning cost, is equal to the assigning cost defined based on the physical channel model. Moreover, we develop a token-based protocol to manage the coupling between cells in the whole multicellular network based on a distributed manner without the need for data exchange between the BSs. Simulation results demonstrate that the proposed low complex smart pilot sequence assignment method achieves a good performance, which is better than those of some other related works in multicellular design regarding normalized mean square error (NMSE) and achievable rate criteria.

Energy Efficiency Maximization Under Minimum Rate Constraints in Multi-Cell MIMO Systems With Finite Buffers

Having recognized the dramatic increase in the number of mobile devices and infrastructure nodes, standards organizations and regulatory bodies have adopted energy efficiency (EE) as a key performance metric for fifth-generation networks. Recent works on multiple input multiple output (MIMO) systems have suggested that it is important to use finite-buffer models, because they may lead to better transceiver designs and more accurate performance analyses than full-buffer traffic models. Therefore, this article addresses the MIMO transceiver design problem for EE maximization in the downlink of finite-buffer multicell systems. Unlike previous works, our problem formulation takes into account per-user minimum rate requirements. We arrive at a nonconvex fractional optimization problem, which is hard to tackle. By exploiting the properties of fractional programming, and using Dinkelbach's method, the resulting fractional form optimization problem is transformed to an equivalent optimization problem in subtractive form. Next, the nonconvexity of this problem is handled using successive convex approximation, leading to iterative centralized and decentralized resource allocation solutions. Finally, considering a realistic channel model with space, frequency and time correlations, numerical results confirm the effectiveness of the proposed algorithms and indicate significant performance gains in terms of achieved EE over existing solutions for full and finite-buffer models.

Deep Learning-Based CSI Feedback for Beamforming in Single- and Multi-Cell Massive MIMO Systems

The potentials of massive multiple-input multiple-output (MIMO) are all based on the available instantaneous channel state information (CSI) at the base station (BS). Therefore, the user in frequency-division duplexing (FDD) systems has to keep on feeding back the CSI to the BS, thereby occupying large uplink transmission resources. Recently, deep learning (DL) has achieved great success in the CSI feedback. However, the existing works just focus on improving the feedback accuracy and ignore the effects on the following modules, e.g., beamforming (BF). In this paper, we propose a DL-based CSI feedback framework for BF design, called CsiFBnet. The key idea of the CsiFBnet is to maximize the BF performance gain rather than the feedback accuracy. We apply it to two representative scenarios: single- and multi-cell systems. The CsiFBnet-s in the single-cell system is based on the autoencoder architecture, where the encoder at the user compresses the CSI and the decoder at the BS generates the BF vector. The CsiFBnet-m in the multi-cell system has to feed back two kinds of CSI: the desired and the interfering CSI. The entire neural networks are trained by an unsupervised learning strategy. Simulation results show the great performance improvement and complexity reduction of the CsiFBnet compared with the conventional DL-based CSI feedback methods.

Extending the Welch Bound: Non-Orthogonal Pilot Sequence Design for Two-Cell Interference Networks

Interferences due to non-orthogonality of signals usually exist in wireless networks when the number of users is larger than the sequence length, such as non-orthogonality of the pilots in multi-cell systems and non-orthogonality of the signature sequences in overloaded code-division-multiple-access (CDMA) systems. We address this effect from the perspective of non-orthogonal sequence design in a two-cell multiple-antenna network. Specifically, we aim at designing pilot sequences to minimize the sum mean-squared-error (MSE) of channel estimation with a given sequence length <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> where <inline-formula> <tex-math notation="LaTeX">$\tau \in [K,2K]$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> is the number of users per cell. Considering the strength disparity between channels originating from the home cell and the neighbor cell, this problem boils down to minimizing the sum of squares of weighted correlations among sequences, whose lower bound is obtained in <i>closed form</i> and can be regarded as a generalization of the well-known Welch bound (Welch, 1974). We prove this extended Welch bound is achievable, and design an algorithm based on the Davies-Higham method to generate the interference-minimizing sequences. Three fundamental properties of the proposed sequences are presented. Finally, we derive closed-form expressions of the average signal-to-interference-plus-noise-ratio (SINR) and rate for data transmission, based on which the optimal training duration can be found.

Nowcasting Multicell Short-Term Intense Precipitation Using Graph Models and Random Forests

AbstractShort-term intense precipitation (SIP; i.e., convective precipitation exceeding 20 mm h−1) nowcasting is important for urban flood warning and natural hazards management. This paper presents an algorithm for coupling automatic weather station data and single-polarization S-band radar data with a graph model and a random forest for the nowcasting of SIP. Different from the pixel-by-pixel precipitation nowcasting algorithm, this algorithm takes the convective cells as the basic units to consider their interactions and focuses on multicell convective systems. In particular, the following question could be addressed: Will a multicell convective system cause SIP events in the next hour? First, a method based on spatiotemporal superposition between cells is proposed for multicell systems identification. Then, the graph model is used to represent cell physical attributes and the spatial distribution of the entire system. For each graph model, a fusion operation is used to form a 42-dimensional graph feature vector. Finally, combined with the machine learning approaches, a random forest classifier is trained with the graph feature vector to predict the precipitation. In the experiment, this algorithm achieves a probability of detection (POD) of 79.2% and a critical success index (CSI) of 68.3% with the data between 2015 and 2016 in North China. Compared with other precipitation nowcasting algorithms, the graph model and random forest could predict SIP events more accurately and produce fewer false alarms.

MUSIC-Based Pilot Decontamination and Channel Estimation in Multiuser Massive MIMO System

This paper addresses the problem of channel estimation in a multiuser multi-cell wireless communications system in which the base station (BS) is equipped with a very large number of antennas (also referred to as “massive multiple-input multiple-output (MIMO)”). We consider a time-division duplexing (TDD) scheme, in which reciprocity between the uplink and downlink channels can be assumed. Channel estimation is essential for downlink beamforming in massive MIMO, nevertheless, the pilot contamination effect hinders accurate channel estimation, which leads to overall performance degradation. Benefitted from the asymptotic orthogonality between signal and interference subspaces for non-overlapping angle-of arrivals (AOAs) in the large-scale antenna system, we propose a multiple signals classification (MUSIC) based channel estimation algorithm during the uplink transmission. Analytical and numerical results verify complete pilot decontamination and the effectiveness of the proposed channel estimation algorithm in the multiuser multi-cell massive MIMO system.

Hybrid Precoding for Massive MIMO With Low Rank Channels: A Two-Stage User Scheduling Approach

To fully reap the benefits of massive multiple-input multiple-output hybrid analog and digital precoding in frequency division duplexing single-cell systems, a two-stage precoder is developed utilizing the signal-to-leakage-plus-noise ratio metric. The main idea of this technique is to jointly design the analog precoder based only on the long-term channel statistics information at the transmitter, i.e., the channel mean and reconstructed reduced rank covariance statistics, while the digital precoder is designed based on the instantaneous channel state information of the reduced dimensionality effective reconstructed channel. Consequently, we can significantly reduce the downlink training and uplink feedback overhead analogously to the rank of the resultant effective channel. The two extremes of full channel state information at the transmitter (CSIT) and statistical CSIT are also investigated. The performance gap between the full and statistical CSIT corroborates the importance of the proposed two-stage CSIT approach. These precoders are then extended to multi-cell systems. It is shown that the digital baseband precoder design problem reduces to the generalized Rayleigh quotient problem, while the analog precoder design problem reduces to the quotient trace problem, also known as the ratio trace problem. These dimensionality reduction problems are solved via the generalized eigenvalue decomposition method. Finally, in the presence of multiuser diversity where only a subset of the users are scheduled, to considerably alleviate the channel estimation and feedback overhead burden, a low-complexity one-stage and two-stage CSIT joint user scheduler and precoder algorithms are developed.

Modelling Grid Constraints in a Multi-Energy Municipal Energy System Using Cumulative Exergy Consumption Minimisation

Efficiency measures and the integration of renewable energy sources are key to achieving a sustainable society. The cumulative exergy consumption describes the resource consumption of a product from the raw material to the final utilisation. It includes the exergy expenses for energy infrastructure as well as the imported energy. Since consumers and renewable potentials are usually in different locations, grid restrictions and energy flows have a significant impact on the optimal energy system design. In this paper we will use cumulative exergy minimisation together with load flow calculations to determine the optimal system design of a multi-cell municipal energy system. Two different load flow representations are compared. The network flow model uses transmission efficiencies for heat, gas and electricity flows. The power flow representation uses a linear DC approximated load flow for electricity flows and a MILP (mixed integer linear programming) representation for heat and gas flows to account for the nonlinear pressure loss relation. Although both representations provide comparable overall results, the installed capacities in the individual cells differ significantly. The differences are greatest in well meshed cells, while they are small in stub lines.

Performance analysis of linear precoders with imperfect channel covariance information for multicell system

Comparison and analysis of the regularised zero forcing precoder, rapid numerical algorithms-based precoder and the truncated polynomial expansion-based precoder are done for massive multiple-input multiple-output wireless system for multicell scenario. The analysis was done for the imperfect channel covariance information. The achievable signal-to-interference-and-noise ratio, spectral efficiency and energy efficiency were investigated. The simulated outcome of the rapid numerical algorithms, regularised zero forcing and truncated polynomial expansion precoders for multicell massive MIMO system was analysed. The rapid numerical algorithms-based precoder gave the best performance followed by the regularised zero forcing precoder, and the truncated polynomial expansion-based precoder had the lowest performance for the multicell massive MIMO system. The increase in spectral efficiency per cell can be attributed to the fact that the pre-log factor reduces with the increased number of pilots. Also, this leads to increased instantaneous signal-to-interference-and-noise ratio as the channel estimates become better with reduced pilot contamination. Again, for truncated polynomial expansion precoding there is a reduction in spectral efficiency because improvement in approximation quality do not overshadow the reduction in pre-log factor. The performance is evaluated for uncoordinated and coordinated massive MIMO.

Efficient Techniques for In-Band System Information Broadcast in Multi-Cell Massive MIMO

We consider joint beamforming of data to scheduled terminals (STs) and broadcast of system information (SI) to idle terminals (ITs) on the same time-frequency resource in multi-cell multi-user massive MIMO systems. We consider two different types of SI broadcast, i) synchronous broadcast of common (i.e., same) SI symbols from all cells, and ii) synchronous broadcast of cell-specific SI symbols from each cell. Through analysis we derive expressions for the achievable sum rate to STs in each cell and the rate of SI transmission to an IT for both these types of SI broadcast. We also derive expressions for the sum rate to STs and the rate to an IT for traditional orthogonal access (OA) where a fraction of physical resource is reserved for broadcast of SI. Simulations reveal that, just as in the single-cell scenario, for the multi-cell scenario also, joint beamforming and broadcasting (JBB) is more energy efficient than OA.

Pilot Design and Allocation for Multi-Cell Massive MIMO Systems With Two-Tier Receiving

This paper investigates pilot design and allocation in multi-cell massive multiple-input multiple-output systems with two-tier receiving. As the length of pilot is limited, the pilots cannot be orthogonal and cause interference. The sum rate as well as the signal-to-interference-plus-noise ratio (SINR) of the system are firstly analyzed. The sum rate upper bound and the asymptotic SINR are derived. The analysis shows that the pilot correlation and the spatial correlation jointly affect the sum rate. Based on this observation, the discrete Fourier transform vectors are employed in designing pilots with various correlation values. Then, with the channel correlation information and the designed pilots, the pilots are allocated to the cells in a greedy way with the object of maximizing the sum rate upper bound. Moreover, the sum rate analysis, the pilot design, and the pilot allocation are extended to the scenario with grouped correlations. Simulation results show that the proposed pilot design and allocation performs better than other pilots and the derived upper bound is effective.

Partial Pilot Allocation Scheme in Multi-Cell Massive MIMO Systems for Pilot Contamination Reduction

Inter-cell interference has been identified as one of the major challenges of multiple-input–multiple-output (MIMO)-enabled cellular systems. This problem occurs when the same pilot sets are reused across adjacent cells to save bandwidth for data transmission. As a result, so-called pilot contamination occurs, which cannot be mitigated with an increased number of serving antennas. In this work, we proposed a partial pilot allocation scheme (PPA) to tackle the pilot contamination problem and consequently improve the uplink throughput of users in multi-cell massive MIMO systems. This was achieved by using the large-scale characteristics of the fading channel to keep users with a weak channel condition out of the effect of severe interference during the pilot allocation process. Simulation results showed that the proposed scheme outperformed both smart pilot allocation (SPA) and conventional schemes. In particular, PPA improved the uplink rate by 30% compared to the SPA—a recently proposed schema. Furthermore, our simulation results clearly showed that PPA improved the cumulative distribution function (CDF) of the signal-to-interference-plus-noise ratio (SINR) and uplink throughput.

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

The advancements in smart power grid and the advocation of “green communications” have inspired the wireless communication networks to harness energy from ambient environments and operate in an energy-efficient manner for economic and ecological benefits. This article presents a contemporary review of recent breakthroughs on the utilization, redistribution, trading and planning of energy harvested in future wireless networks interoperating with smart grids. This article starts with classical models of renewable energy harvesting technologies. We embark on constrained operation and optimization of different energy harvesting wireless systems, such as point-to-point, multipoint-to-point, multipoint-to-multipoint, multi-hop, and multi-cell systems. We also review wireless power and information transfer technologies which provide a special implementation of energy harvesting wireless communications. A significant part of the article is devoted to the redistribution of redundant (unused) energy harvested within cellular networks, the energy planning under dynamic pricing when smart grids are in place, and two-way energy trading between cellular networks and smart grids. Applications of different optimization tools, such as convex optimization, Lagrangian dual-based method, subgradient method, and Lyapunov-based online optimization, are compared. This article also collates the potential applications of energy harvesting techniques in emerging (or upcoming) 5G/B5G communication systems. It is revealed that an effective redistribution and two-way trading of energy can significantly reduce the electricity bills of wireless service providers and decrease the consumption of brown energy. A list of interesting research directions are provided, requiring further investigation.

Robust Joint Hybrid Analog-Digital Transceiver Design for Full-Duplex mmWave Multicell Systems

In this work, we investigate a full-duplex (FD) millimeter wave (mmWave) multicell system, where the BS of each cell receives signals from uplink (UL) users and transmits signals to downlink (DL) users at the same time, over the same frequency band. We maximize the sum rate lower bound of the FD multicell system by jointly optimizing the digital and analog beamforming matrices at the base station (BS) and the transmit power levels of the UL users under total transmit power constraints and unit-modulus constraints (due to the analog beamforming matrices), in the presence of imperfect channel state information (CSI). The problem under study is very challenging due to the highly non-convexity of the objective function and constraints. We transform this problem into an equivalent but more tractable form and propose a novel iterative algorithm based on the penalty dual decomposition (PDD) to solve it. The proposed algorithm is guaranteed to converge to the set of Karush-Kuhn-Tucker (KKT) solutions of the original problem. Moreover, we also extend our proposed algorithm to the structure of subarray. Simulation results validate the effectiveness of the proposed algorithm as compared with conventional nonrobust and half-duplex (HD) algorithms.