High reliability and low latency are essential to railway wireless communications, which transmit train control and dispatch commands to ensure operational safety. However, as railway systems become increasingly electrified and more complex, the exposure to electromagnetic interference (EMI) also grows, potentially causing service disruptions and compromising safety. Intentional EMI (IEMI), which is deliberately and often maliciously generated, further increases the vulnerability of these critical communication networks. Real-time detection and classification of EMI and IEMI therefore become increasingly important. This paper presents composite models that reflect realistic railway scenarios and proposes an adaptive classification approach for EMI and IEMI using a deep learning algorithm based on bidirectional long-short-term memory (BiLSTM) networks and attention mechanisms. By employing time-series feature extraction to analyze both time and frequency information at fine resolution, the proposed method demonstrates a classification accuracy of 94.98%. Simulation results outperform existing techniques with a 3% improvement in accuracy, showcasing its adaptability across four typical railway scenarios at train speeds of up to 500 km/h. Moreover, online monitoring phase performs real-time detection in just 7.43 ms, meeting the stringent latency requirements for railway systems. Validation using real-world data further confirms the practical applicability of the proposed methods under actual operating conditions.

This paper proposes a spatial chirp-based joint optimization method for analog beamforming in RIS-assisted THz ultra-wideband communication systems. The THz band (0.1-10 THz) offers significantly wider bandwidth, enabling Tbps data rates, which are crucial for 6G communications. However, traditional phase shifters are optimized for a single frequency. In ultra-wideband systems, the phase response deviates as frequency varies, leading to beam squinting problem, which significantly degrades system performance. Existing solutions, such as true-time-delay architectures, impose excessive complexity, making them impractical for THz systems. In this work, we propose leveraging polynomial expansion to optimize the spatially dependent phase functions of RIS, Tx, and Rx. We formulate the optimization problem as maximizing system throughput, and develop a gradient ascent-based iterative algorithm to solve it. Simulation results demonstrate that our method outperforms conventional and state-of-the-art approaches in metrics including phase response, throughput, and BER, providing a cost-effective solution for optimizing analog beamforming.

Abstract Reconfigurable intelligent surfaces (RISs) have emerged as propitious solution to configure random wireless channel into suitable propagation environment by adjusting a large number of low‐cost passive reflecting elements. It is considered that narrowband downlink millimeter wave (mmWave) multiple‐input multiple‐output (MIMO) communication is aided by deploying an RIS. Large antenna arrays are used to counter the huge propagation loss suffered by the mmWave signals. Hybrid precoding in which precoding is performed in digital and analog domains is employed to reduce the number of costly and power‐consuming radio frequency (RF) chains. Passive beamforming at RIS is designed together with precoder and combiner through joint optimization problem to minimize the mean square error between the transmit signal and the estimate of signal at the receiver. The optimization problem is solved by an iterative procedure in which solution to the non‐convex reflecting coefficients design problem is approximated by extracting the phases of the solution to unconstrained problem without unit amplitude constraint of the reflecting elements. It is shown that the proposed design principle also applies to the wideband channel. Simulation results show that the proposed design delivers performance better than existing state‐of‐the‐art solutions, but at lower complexity.

To improve channel estimation (CE) for reconfigurable intelligent surface (RIS)-assisted systems in time-varying mmWave channels, this paper proposes a two-stage adaptive CE scheme. This is the first attempt to develop a CE scheme without assumptions on specific timescales for channel variations. In the first stage, the adaptive scheme incorporates the estimation of partial channel state information and a channel status check process. The introduced check process can monitor the changing status of the channels and provide information for the second stage. In the second stage, based on the results from the check process, the adaptive scheme adaptively selects from two proposed candidate CE algorithms: Two-Phase orthogonal matching pursuit (TP-OMP) and Structured-Shift OMP (SS-OMP). Simulation results show that both TP-OMP and SS-OMP can reduce pilot overhead by around 33%, and respectively lower the computational complexity of existing works by about 55% and 65%. Additionally, the check process obtains an accuracy rate of approximately 92% so that the proposed CE scheme can maintain stable CE performance in time-varying channels.

Large-scale natural disasters or public security incidents frequently cause substantial damage to both human life and property, as well as terrestrial communication infrastructure. As a result, this disruption often cuts off communication, leaving the victims isolated from the outside world. Timely completion of Search and Rescue (SAR) operations within the first 72 hours following a disaster is of critical importance, as it can significantly protect human lives and reduce property damage. Note that conducting SAR operations in post-disaster areas requires not only communication support but also computing support. In light of this, it is particularly important to rapidly establish an emergency communication system with computing resources, which offers high reliability, low latency, and high capacity. Such a system is crucial for reducing the threat posed by disasters to human lives. Given the challenges in rapidly restoring terrestrial networks, flexible aerial networks and existing satellite networks emerge as optimal candidates for emergency communications. Meanwhile, the integration of aerial platforms, such as High Altitude Platforms (HAPs) and Low Altitude Platforms (LAPs), can effectively reduce the transmission latency associated with satellite networks and alleviate capacity constraints in terrestrial emergency communication networks. The Space-Air-Ground Integrated Network (SAGIN)-based emergency communication system can utilize the advantages of each segment, including the extensive coverage provided by the space network, the flexibility of the air network, and the high transmission data rates and low latency of the ground network. Consequently, this represents an exemplary paradigm for supporting SAR operations in the future. In this paper, we provide a comprehensive survey of SAGIN-based emergency communication systems, identify key challenges, and discuss promising technologies. Furthermore, future research directions are outlined from multiple perspectives.

To meet the demand for an improved quality of experience for user equipment (UE) and improved spectral efficiency offered by mobile network operators, 5G Networks have been designed to employ advanced technologies such as beamforming, Massive MIMO, etc. However, the major issue facing 5G systems is interference due to the implementation of universal frequency reuse for all cells in the 5G Network. This work proposes a coordinated beamforming technique for a two-tier downlink 5G massive MIMO system to tackle inter-cell interference and consequently improve the system spectral efficiency of the 5G system. The choice of coordinated beamforming vectors that optimized the weighted sum spectral efficiency of the system while satisfying the constraints was formulated as a non-convex, non-polynomial hard (NP-Hard) optimization problem and reformulated to a convex problem by fixing the signal-to-interference-and--noise ratio (SINR) to a threshold and solved using CVX. The results showed that the proposed method attained at least the theoretical minimum achievable spectral efficiency for a 5G system which is 30 bit/s/Hz, outperforming other methods based on same parameters: Kr = 9, N = 20, SNR = 30dB, (where Kr and N are the total number of UEs in the system and the total number of transmit antenna at the base stations). The results also showed that with an increase in Kr and N, spectral efficiency is improved.

The proliferation of high-speed rail (HSR) networks and railway electrification has advanced the integration of the latest wireless communication networks with railway systems. Ensuring a reliable bidirectional communication link between moving trains and base stations is crucial to maintaining the safety of real-time rail operations. However, the growing complex-ity of railway systems and increased exposure to electromagnetic emissions present substantial challenges. In particular, railway wireless communication networks are vulnerable to various kinds of electromagnetic interference (EMI) and intentional EMI (IEMI), which could cause operational disruptions and safety hazards. This paper proposes a real-time classification method for EMI and IEMI, using deep learning-based bidirectional long-short-term memory (BiLSTM) networks. By employing multivariate time-series characteristics, the method can simul-taneously learn both time and frequency information at a finer resolution, offering better performance than existing methods. The simulation results demonstrate a high accuracy of 93.4% and adaptability at different speeds and in various scenarios.

Lately, Reconfigurable Intelligent Surface (RIS) is becoming an emerging technique that supports wireless communications with higher transmission quality via adjustable propagation environment. To fully actualize its functionality, the design of its phase shifter (PS) is critical. However, most existing RIS PS design methods and algorithms are very complex and consume considerable processing time, which is unfavorable for the efficient real-time communication systems. In this paper, we aim to realize low-complexity joint optimization for precoder, combiner and RIS PS. We consider a RIS-aided point-to-point (P2P) millimeter-wave (mmWave) fully digital massive Multiple-Input-Multiple-Output (MIMO) downlink system. When designing the RIS PS, we employ the method of minimizing mean square error (MMSE) between the transmitted and received signals. To reduce computational complexity, the non-convex MMSE problem is converted to a convex trace maximization problem under the application of MMSE combiner and SVD precoder. The closed-form solution for RIS subproblem is derived, and a low-complexity alternative joint optimization algorithm is proposed. The simulation results show that the proposed algorithm can achieve high spectrum efficiency in comparison with the state-of-the-art methods, requiring much less computational complexity, as demonstrated by complexity analysis and runtime comparison.

The integration of machine learning (ML) techniques has catalyzed significant advancements in the realm of autonomous vehicle technology, particularly in the domain of Intelligent Transport Systems (ITS) and the evolution of Connected and Automated Vehicles (CAVs). This study focuses on a downlink communication network characterized by a single-antenna Base Transceiver Station (BTS) and autonomous vehicles, with the BTS transmitting information at varying power levels. The primary objective is to predict optimal transmit power for vehicles across diverse channel conditions using machine learning methodologies, aimed at mitigating interference within the system. This interdisciplinary research endeavors to optimize transmit power from the BTS to vehicles through the synergy of machine learning and optimization techniques. By addressing this imperative, we aim to enhance vehicle safety, efficiency, and reliability within modern transportation networks. Leveraging advanced ML models, including Long Short-Term Memory (LSTM) and Feedforward Neural Network (FNN), our investigation reveals promising insights into the efficacy of these algorithms in advancing autonomous driving technologies. The paper presents comparative analyses of two prominent machine learning models, with the Mean Square Error (MSE) computed at 17.2516 for LSTM and 13.8562 for the Feedforward Model. These results underscore the potential of ML-driven approaches in optimizing transmit power for autonomous vehicle communication networks, thereby contributing to the ongoing evolution of intelligent transportation systems.

After a disaster, a network of unmanned aerial vehicles (UAVs) and low Earth orbit (LEO) satellites can provide communication services rapidly to the affected area. In this emergency communication system, UAVs frequently encounter network hand over (HO) issues while conducting post-disaster situational awareness (SA) tasks such as searching and monitoring during their flight. Due to the high demand for uplink data rates and the time-sensitive nature of sensing data, transmission disconnections are prone to occur if UAVs connect to an inappropriate network, potentially affecting subsequent rescue operations. To tackle this problem, a vertical HO scheme based on the Analytic Hierarch Process-Entropy (AHP-Entropy) weighting and Technique for Order Preference by Similarity to Idea Solution (TOPSIS) is proposed. In this work, received signal strength, data rate, and latency are taken into account. The comprehensive attribute weights are obtained by the AHP-Entropy weighting method, and then TOPSIS is applied to rank the candidates to select the best network. Moreover, the satellite-ground distance is obtained by the Mean Value Theorem for Integrals and the maximum and minimum elevation angles during the movement of LEO satellite. Meanwhile, mobile edge computing (MEC) is introduced on LEO satellite for data compression to further reduce the feeder link delay of LEO satellite. The simulation results show that, compared with the benchmark method, the proposed scheme can significantly improve throughput and reduce disconnections without increasing delay excessively. This ensures the stability and reliability of real-time post-disaster SA.

Due to the rapid growth of smart devices and 5G technology increase the requirement of computational tasks, unmanned aerial vehicles (UAVs) assisted Mobile Edge Computing (MEC) networks are designed to improve the task processing by reducing latency. Considering the necessity to complete the task quickly, the UE must seamlessly handover (HO) to the optimal BS, which may lead to frequent HO. This paper introduces a Fuzzy Inference System (FIS) based HO decision-making scheme for UAV-assisted Mobile Edge Computing (MEC) networks, addressing the increasing demand for computational tasks. At first, the proposed method optimizes HO decisions using RSS, distance, and number of serving users. Then, it employs a two-layer FIS to select the target base station (BS), considering SINR, time of stay (ToS), distance, and user connectivity. Simulation results demonstrate the FIS-based method can achieve less HO frequency and task delay compared to RSS-based and TOPSIS-based schemes.

Abstract In order to meet the growing demands and extend network coverage for high‐speed railway (HSR) system, the dense deployment of a large number of small cells (SCs) is considered for 5G networks. However, the deployment of dense SCs and the high speed of trains result in challenging problems such as interference, frequent handovers (HOs), increased HO failure rate, and consequently the deteriorated overall quality of service (QoS). In order to address the challenges in handover, an improved handover decision strategy is proposed based on Support Vector Machine (SVM). The HO decision making is considered as a classification problem taking into account available states that they may have in the HSR network. From the simulation results, it is observed that the proposed scheme is capable of decreasing the number of HO, HO failure rate and enhancing the network performance remarkably.

The vehicular networks are envisioned to support the future intelligent transportation system (ITS). Content dissemination is vital to achieving efficient data traffic management and real-time decision making. Limited resources constrain the vehicles and edge network, but the vehicle-to-vehicle (V2V) communication technology can support the conventional cellular communication link in content dissemination. To explore the full benefit of V2V, relay vehicles can be selected to support direct V2V links in situations where direct communication is not feasible due to distance or shadowing. However, not all vehicles will agree to participate in relaying without any reward. To counter this problem, we introduce an incentive mechanism to entice vehicles to assist in relaying. The trustworthiness between users is considered when establishing a V2V communication link. We jointly consider the content distribution mode and relay selection problems to maximize system utility. Moreover, improved auction algorithms were proposed to improve user utility. The proposed scheme is compared against some closely related baseline schemes.

In an attempt to alleviate the cost and power consumption in millimeter wave (mmWave) multiple input multiple output (MIMO) systems, hybrid precoding has been put forth as a possible solution. The hybrid precoding replaces the conventional fully digital precoding with a combination of analog and digital precoding to achieve good performance using a significantly smaller number of RF chains compared to the number of antennas. In this paper, we consider a narrowband downlink multiple user multiple-input single-output (MU-MISO) system and propose a hybrid precoding method in order to minimize the mean squared error (MSE) for all users. The analog and digital precoding design subproblems are isolated from each other. Analog precoder design is cast as a trace maximization problem, and solved by an iterative procedure based on truncated singular value decomposition (SVD). The digital precoder is determined subsequently after fixing the analog precoder, using Lagrange’s method. The proposed hybrid precoder produces spectral and bit error rate (BER) performances quite close to fully digital precoder, and almost the same as existing high performance hybrid precoders, albeit at a much lower complexity. The proposed precoding method is extended to operate in wideband channel, where it exhibits equally good performance with less complexity.

High-speed railways (HSRs) has become one of the most preferable modes of transportation. In the evolution of the railway wireless communication system from Long Term Evolution for Railway (LTE-R) to the 5th Generation Wireless System (5G), the rapid increase in the train speed and number of base stations along the railway track led to challenging handover (HO) problems, such as high failure rate and frequent HOs. In order to address this challenge, an improved handover decision strategy is proposed based on Q-learning algorithm. The simulation results demonstrate that our proposed scheme is capable of reducing the number of unnecessary handover and improving the network performance remarkably.

A vertical heterogeneous network (VHetNet) is an integration of aerial base stations (ABS) into the terrestrial cellular networks. ABSs are anticipated to enhance the communications performance of the network because they are mobile and are quick to deploy suitable for on demand coverage. They promise a low-cost solution for ubiquitous connectivity especially in developing countries where telecommunications infrastructure and the power grid are limited. Accurate and tractable frame-works are required to adequately quantify the communications performance of a VHetNet. In this paper we develop an analytical framework for determining the coverage probability and average rate of a heterogeneous network with aerial, macro and small base stations. We considered a scenario with low density of ABSs suited to financially constrained countries and those with security concerns over flying machines. The results show that there is an optimum ABS altitude and density that maximize the coverage probability at a specific signal-to-interference ratio (SIR) threshold. The average rate increases with density at low SIR threshold of -10 dB while at 0 dB the optimum density decreases with altitude.

Abstract Millimeter wave (mmWave) frequency spectrum can mitigate severe spectrum shortage caused by the explosive growth of mobile data demand. To overcome the high propagation loss of mmWave signals, massive multi‐input multi‐output (MIMO) and hybrid architecture are employed. As in microwave communication systems, channel state information (CSI) is essential to fully achieve the advantages of mmWave communication. However, due to the massive number of antennas and hybrid architecture, the CSI acquisition is challenging. The sparsity of mmWave channel can be utilized to reduce the training overhead. In addition to sparsity, real‐world measurements in dense urban propagation environments reveal that the mmWave channel may spread in form of cluster of paths over the angular domains, namely the angular spread. In this paper, it is utilized to formulate the channel estimation as a block‐sparse signal recovery problem. The block orthogonal matching pursuit (BOMP) is used to validate the model. Then, block fast Bayesian matching pursuit (BFBMP) algorithm is proposed to solve the above problem. Compared with other existing channel estimation methods, simulation results show that the angular spread feature and the proposed BFBMP can considerably improve the CSI estimation with less complexity.

Relay‐assisted device‐to‐device (D2D) communications have been proposed as a supplement for direct D2D communications to enhance traffic offloading capacity. In this study, the authors propose a joint mode selection, relay selection and resource allocation for relay‐assisted D2D communications. They aim at maximising the overall system throughput while guaranteeing the power limitation and signal‐to‐noise‐and‐interference ratios of all cellular and active D2D links. Since this optimisation is NP‐hard, they then propose a graph colour‐based resource allocation algorithm to effectively solve it. Simulation results show that the proposed algorithm can produce close‐to‐optimal performance with acceptable computational complexity.

Since D2D (Device-to-Device) communication was proposed in cellular network as a new paradigm for enhancing network performance, many works have been done on resource allocation to improve system throughput and energy efficiency (EE) for underlay D2D communications. However, the system long-term average fairness as one of the system main performance metrics was rarely considered especially when users are moving. In this paper, we formulate the joint power and channel allocation problem aiming at maximizing the system fairness subject to the minimum required SINRs (Signal to Interference and Noise Ratios) and power consumption limits of cellular and active D2D links. To solve the above problem practically, we first decompose our original problem into two sub-problems (power and channel allocation), then solve them sequentially. Simulation results show that our proposed algorithm can dramatically enhance the system fairness and slightly improve the system throughput comparing with existing method.

Adoption of hybrid precoding is the key element of reducing the hardware cost of radio-frequency compared with the conventional full-digital precoding approach in millimeter wave (mmWave) MIMO systems. For hybrid precoding, channel state information (CSI) is needed. However, the use of analog precoding in hybrid architecture and the large antenna array make channel estimation difficult for mmWave system. It has been shown that mmWave channels exhibit sparsity, thus compressive sensing (CS) techniques can be leveraged to conduct channel estimation. Conventional CS based channel estimation methods for mmWave MIMO are based on quantized angle grid. However, the performance would be severely affected by off grid angles which can not be improved by increasing the resolution because this will increase the coherence between the grid points. In this paper, we propose an Interior Point (IP) aided orthogonal matching pursuit (OMP) algorithm. It significantly improves the channel estimation accuracy by reducing the estimation error of angle-of-departure (AoD) and angle-of-arrival (AoA). The simulation results demonstrate the advantage of the proposed IP-OMP over the existing methods such as least squares and the conventional OMP.

Ultra‐dense small cell (SC) deployment in the future 5G network makes the architecture of the network as heterogeneous networks (HetNets). This is a good solution to boost the capacity of the network and extend its coverage. However, the dense SCs deployment has brought new challenges to the network including interference, frequent unnecessary handovers, and handover failures. Therefore, user equipment will suffer from a degraded quality of service. In this paper, the authors propose a grey rational analysis‐based handover (GRA‐HO) method in dense SCs HetNet. The proposed method combines the analytical hierarchy process technique to obtain the weight of the handover metrics and the GRA method to rank the available cells for the best handover target. The performance of the proposed method is evaluated and compared with the traditional multiple attribute decision‐making methods including simple additive weighting and VIKOR methods. Results show that the GRA‐HO method has outperformed the existing methods in terms of reducing the number of frequent handovers and link failures, in addition to enhancing energy efficiency.

In this study, the authors formulate a non‐cooperative game approach in which all base stations compete in a selfish manner to transmit at higher power. Each base station in the network is considered as a player in the game. The solution of the game is obtained by finding the optimal point, namely the Nash equilibrium. The proposed method, named efficient handover game theoretic, targets to manage the handover in dense small cell heterogeneous networks. Each player in the game optimises its payoff by adjusting the transmission power so as to enhance the overall performance in terms of throughput, handover, energy consumption, and load balancing. In order to choose the preferred transmission power for each player, the payoff function takes into account the gain of increasing the transmission power, energy consumption, base station load, and unnecessary handover. The cell selection is performed using the technique for order preference by similarity to an ideal solution (TOPSIS). A game theoretical approach is implemented and evaluated for dense small cell heterogeneous networks to validate the enhancement achieved in the proposed method. Results show that the proposed game theoretical approach provides a throughput enhancement while reducing the power consumption in addition to minimise the unnecessary handover and balance the load between base stations.

The heterogeneous cellular network (HetNet) has emerged as a promising technology for the fifth generation mobile networks that can be used to meet high demand of data rate and better quality of service (QoS) performance. However, the performance of HetNet will depend on how scarce resources such as frequency, time, power and spatial resource are shared among user equipments (UEs) in the system and also how interference is controlled. In this work, we utilise UE‐Centric clustering as a tool to effectively determine the interfering base stations (BSs) that cause significant interference to each UE in the network. These interfering BSs together with the serving BSs of these interfered UEs will coordinate and make resource allocation (RA) decisions together to allocate spatial directions to each UE in the network in order to manage interference in the network. We formulate the RA problem as maximizing the weighted sum‐rate of the HetNet while fulfilling power, QoS and interference constraints. This optimization problem is non‐convex. We readily split the RA problem into two sub‐problems: the spatial direction allocation problem and the power allocation problem, respectively. We are able to solve these problems efficiently using SeDumi. Simulation results of our proposed method, show significant improvement.

In this study, the channel estimation problem for the uplink massive multi‐input multi‐output (MIMO) system is considered. Motivated by the observations that the channels in massive MIMO systems may exhibit sparsity and the channel support changes slowly over time, the authors propose one efficient channel estimation method under the framework of compressive sensing (CS). By exploiting the channel impulse response (CIR) estimated from the previous orthogonal frequency division multiplexing symbol, they firstly estimate the probabilities that the elements in the current CIR are non‐zero. Then, they propose the probability‐weighted subspace pursuit algorithm exploiting these probability information to efficiently reconstruct the uplink massive MIMO channel. Moreover, noting that the massive MIMO systems also share a common support within one channel matrix due to the shared local scatterers in the physical propagation environment, an antenna collaborating method is exploited for the proposed method to further enhance the channel estimation performance. Simulation results show that compared to the existing CS methods, the proposed methods could achieve higher spectral efficiency as well as more reliable performance over time‐varying channel.

In this paper, channel estimation problem for downlink massive multi-input multi-output (MIMO) system is considered. Motivated by the observation that channels in massive MIMO systems may exhibit sparsity and the path delays vary slowly in one uplink-downlink process even though the path gains may be quite different, we propose a novel channel estimation method based on the compressive sensing. Unlike the conventional methods which do not make use of any a priori information, we estimate the probabilities that the paths are nonzero in the downlink channel by exploiting the channel impulse response (CIR) estimated from the uplink channel estimation. Based on these probabilities, we propose the Weighted Structured Subspace Pursuit (WSSP) algorithm to efficiently reconstruct the massive MIMO channel. Simulation results show that the WSSP could reduce the pilots number significantly while maintain decent channel estimation performance.

Heterogeneous cellular networks (HetNets) is key technology in 5G used to tackle the ever increasing demand of data rate. The most critical problem of HetNet is interference. In this paper, we utilize the coordinated beamforming technique to mitigate the interference problem. We also suggest that reference signal receive power (RSRP) based cell association scheme has limitation when applied to multi-tier cellular networks. Therefore, we propose a new cell selection approach for HetNets, which is based on average channel gain. Simulation results of our designed beamformers show improvement over other schemes in terms of achievable information rates per cell.

We formulate the resource allocation in the downlink of heterogeneous orthogonal frequency division multiple access (OFDMA) networks. Our primary objective is to maximize the system sum throughput subject to service and system constraints, including maximum transmit power, quality of service and per-user subchannel allocation. Due to the intercell interference, the corresponding optimization problem is, in fact, nonconvex, that cannot be solved using standard convex optimization techniques. Here we propose an algorithm based on local search method and use of penalty function to approximate the formulated constrained optimization problem by an unconstrained one. To approximate a global optimal, we set escaping procedure from the critical point based on constraint function conditions. The result shows that the proposed method might achieve optimum conditions by a hybrid of split and shared spectrum allocation. Numerical analysis indicates that the proposed algorithm outperform the other conventional methods in the scenario of the high level of inter-cell interference. Moreover, the proposed method approximates the global optimum by considering both channel gain and inter-cell interference with a fast rate of convergence.

This paper proposes a number of methods to determine potential small cell site locations using geo-located UE measurement reports in order to maximise the traffic offload from the macrocell network onto the small cells. The paper also shows how the information contained within the measurement reports can be used to create “RF fingerprints#x201D; which in turn can be used to discard UE measurement reports with erroneous location information and by doing so increase the effectiveness of the small cell placement algorithm. Simulations are presented which suggest that when addressing traffic hotspots in central London using small cells with coverage radii of 50m and 100m, the gains provided by the placement algorithms using simple RF fingerprinting technique are significant for UE reports with large location errors (>100m RMS error) when compared to techniques not using RF fingerprinting.

This paper presents a number of Self-Organising Network (SON) based methods using a 3GPP Minimisation of Drive Testing (MDT) approach or similar and the analysis of these geo-located UE measurements to maximise traffic offload onto lamppost mounted 3G/WCDMA microcells. Simulations have been performed for a real 3G/WCDMA microcell deployment in a busy area of central London and the results suggest that for the network studied a traffic increase on the microcell layer of up to 175% is achievable through the novel SON methods presented.

The aim of this paper is to examine the benefit of rapid dynamic antenna tilting on a WCDMA cellular network, where dynamic antenna tilting is applied as either a SON implementation or a MIMO implementation. The potential network performance improvement of this technique is evaluated using a dynamic WCDMA/LTE simulator and the results presented suggest that downlink performance gains of up to 89% may be possible through this simple but novel technique.

In uplink orthogonal frequency division multiple access (OFDMA) systems with carrier frequency offsets (CFOs), there always be a dilemma that high performance and low complexity cannot be obtained simultaneously. In this study, in order to achieve better trade‐off between performance and complexity, the authors propose a grouped minimum mean squared error (G‐MMSE)‐based multi‐stage interference cancellation (MIC) scheme. The first stage of the proposed scheme is a G‐MMSE detector, where the signal is detected group by group using banks of partial MMSE filters. The signal group can be either user based or subcarrier based. Multiple novel interference cancellation (IC) units are serially concatenated with the G‐MMSE detector. Reusing the filters in the G‐MMSE detector significantly reduces the computational complexity in the subsequent IC units as shown by the complexity analysis. The performance of the proposed G‐MMSE‐MIC schemes are evaluated by theoretical analysis and simulation. The results show that the proposed schemes outperform other existing schemes with considerably low complexity.

Wireless technologies are well known to provide tremendous benefits to the healthcare sector. In the evaluation of the wireless technologies, ZigBee is found specifically suitable for monitoring due to its low cost, long battery life and widely accepted standard. But ZigBee uses the same ISM free band with other wireless techniques, such as Wi-Fi, which is also widely deployed in health care. The coexistence of these techniques in the crowded ISM band will cause mutual interference, which in medical environment has death indication. This paper aims at providing a comprehensive evaluation of the interference between Wi-Fi and ZigBee technologies. Constructing on earlier work led by researchers in Schneider Electric [2], real-time tests were performed in research laboratory and residential environment and results were analyzed to provide a reference for the deployment of multiple wireless technologies to avoid mutual interference.

In this paper, the antenna beam pattern modulation (ABPM) is presented as a transmission scheme which uses antenna patterns in addition to the conventional amplitude/phase modulation (APM) symbols to convey information and increase spectral efficiency. The transmitted bits per symbol period are divided into two blocks: the first block contains the antenna beam patterns and the second APM symbols. Then, the second block is mapped to an APM symbol, which is transmitted by the antenna pattern selected by the first block. The beam pattern and the transmitted symbol are detected using the maximum likelihood (ML) by the receiver, in order to de-map the block of information bits. The bit error rate (BER) performance of the proposed ABPM is evaluated over independent and identically distributed (iid) Rayleigh fading channel. The simulation results show that with appropriately designed beam patterns, the transmission scheme can provide a better performance and enhanced throughput when compared to conventional spatial modulation (SM) and APM.

The Femtocell is an attractive solution to improve the indoor coverage of cellular systems. By applying spectrum reuse, femtocells can improve the efficiency of spectrum usage. However, since femtocells are deployed by end-users, insufficient coordination between base stations leads to the reuse of the same frequency resources and results in CCI (co-channel interference), which seriously complicates the synchronization problem, particularly in OFDM based systems, such as WiMAX and LTE. In this paper, we investigate the timing synchronization problem in the presence of CCI of an OFDMA macro/femto uplink scenario. We consider the cross-correlation based timing synchronization and propose a pre-filter to mitigate the CCI effect. The simulation results show that this method effectively eliminates the effect of CCI on the cross-correlation based timing synchronization and provides a performance close to that of no CCI case.

Geographic routing (GR) algorithms are attractive in ad hoc wireless networks owing to their efficiency, scalability, and in particular energy efficiency. In a two-dimensional ad hoc wireless network, the authors propose the optimal range forward (ORF) algorithm, which is based on the optimal transmission range to reduce the energy consumption of the network. Furthermore, the energy balance of each node based on the ORF algorithm is considered, and an optimal forward with energy balance (OFEB) algorithm is proposed to balance the residual energy of each node and to prolong the network lifetime. The authors compare the proposed algorithms with the existing GR algorithms, such as the most forward within radius and the nearest forward progress algorithms. The network lifetime, the network throughput, the number of packets successfully received by the destination and the average energy cost of each successfully received packet, resulting from all the algorithms mentioned above, are compared based on different node densities. It is shown that the performance of the OFEB algorithm is significantly better than the others.

In this paper, a generalized phase spatial shift keying (GPSSK) modulation is introduced as an improvement of the generalized space shift keying (GSSK) modulation which lays the concept of using the antenna indices to convey information. The idea behind the proposed scheme is to map the bit vector corresponding to one symbol in two information bit blocks: the first block selects the antennas used during the transmission and the second block chooses the transmitted symbols, which are distinguished by their phases. The receiver estimates the indices of the active antennas and the transmitted signal which are used to de-map the block of information bits. To validate the results, an upper bound on the bit error rate (BER) of GPSSK is derived. Both the analytical and simulation results show that the proposed scheme provides performance gain and improved spectrum efficiency over GSSK with low detection complexity.

Time synchronization is an important challenge for femtocell design. An inaccurate clock can introduce interference, degrade spectrum accuracy and disrupt handovers. Among the potential solutions, wireless network-based time synchronization turns out to be cost effective because no extra provisions are required to ensure synchronization even on a regular basis. In wireless networks, receiver-receiver synchronization schemes offer very good performance. In this paper, we design an MS-assisted receiver-receiver synchronization strategy for femtocells. Two schemes are proposed for different scenarios to ensure a more comprehensive availability. Analysis and simulation results demonstrate that our proposed schemes provide sufficient synchronization accuracy for practical scenarios.

In this paper, we consider interference cancellation (IC) schemes for uplink orthogonal frequency division multiple-access (OFDMA) systems with carrier frequency offsets (CFOs). To improve the detection performance without increasing the computational complexity of the detector, we propose a low-complexity multi-stage interference cancellation (MIC) scheme based on a grouped minimum mean squared error (GMMSE) algorithm. In the proposed scheme, a novel IC strategy is employed. The simulation results show that the proposed IC scheme outperforms other existing schemes with considerable low complexity in both subband carrier assignment scheme (CAS) and generalized CAS scenarios.

Abstract This paper examines the carrier frequency offset (CFO) estimation problem in the tile‐based orthogonal frequency division multiple access (OFDMA) uplink systems, which is very challenging due to the presence of multiple CFOs. The existing solutions to this problem are either too complex to implement or not flexible in subcarrier allocation. To solve these problems, this paper proposes a tile‐structure based iterative multi‐CFO estimation technique. The proposed method is developed based on a special training sequence with repetitive structure. The inherent multi‐user interference (MUI) compression provided by the tile structure allows us to utilize the repetitive property of the training sequence to jointly estimate the CFOs in the frequency domain with low complexity. Combining the CFO estimation with an interference cancellation scheme and performing iteratively, the algorithm achieves high estimation accuracy and fast convergence. The proposed algorithm is suitable for any subcarrier assignment schemes. In addition, as compared with other existing time domain based algorithms, which achieve the Cramer Rao Bound (CRB) at the price of unaffordable complexity, it closely approaches their performance with over 70% computational saving, which is significantly important for practical implementation. Copyright © 2010 John Wiley & Sons, Ltd.

The IEEE 802.16e standard for nomadic wireless metropolitan area networks adopts orthogonal frequency-division multiple-access (OFDMA) as an air interface. In these systems, residual carrier frequency offsets (CFOs) between the uplink signals and the base station local oscillator give rise to interchannel interference (ICI) as well as multiple access interference (MAI). Accurate CFO estimation and compensation is thus necessary to avoid a serious degradation of the error-rate performance. In this work, we address the problem of CFO tracking in the IEEE 802.16e uplink and present a closed-loop solution based on the least-squares (LS) principle. In doing so, we exploit a set of pilot tones that are available in each user's subchannel. The resulting scheme can be implemented with affordable complexity and is able to reliably track the CFOs of all active users. When used in conjunction with a frequency offset compensator, it can effectively mitigate ICI and MAI, thereby allowing channel equalization and data detection to follow directly. Numerical simulations are used to demonstrate the effectiveness of the proposed solution in the presence of residual time-varying frequency offsets.

In his work, we propose a relatively low-complexity iterative algorithm for the detection of transmitted symbols at the uplink of a multiple-input multiple-output (MIMO) orthogonal frequency-division multiple-access (OFDMA) system. The algorithm allows distinct frequency-offsets (FO)s beween each user and the base-station (BS). FOs cause inter-carrier-interference (ICI), which degrades the performance of the receiver and increases the computational-complexity o decode the transmitted symbols. In order o decrease the computational-complexity at each receive antenna the proposed algorithm accounts for the interference of subcarrier k onto only ±D nearby subcarriers and ignores the interference on remaining subcarriers. This yields a banded structured ICI matrix, which is exploited o design a low-complexity soft-interference-cancellation minimum mean-squared error (SIC-MMSE) equalizer. The effects of ignoring the subcarriers are compensated for by increasing the number of receive antennas. Simulation results show ha for an uncoded system the bit-error-rate (BER) performance of the proposed algorithm outperforms the MMSE equalization and is very much close to the no FO scenario.

This paper examines the carrier frequency offset (CFO) estimation problem in the orthogonal frequency division multiple access (OFDMA) uplink systems, which is very challenging due to the presence of multiple CFOs. The existing solutions to this problem are either too complex to implement or not flexible in subcarrier allocation. To solve these problems, this paper proposes a tile-structure based iterative multi-CFO estimation technique. The proposed method is developed based on a special training sequence with repetitive structure. The inherent multi-user interference (MUI) compression provided by the tile structure allows us to utilize the repetitive property of the training sequence to jointly estimate the CFOs in the frequency domain with low complexity. Combining the CFO estimation with an interference cancellation scheme and performing iteratively, the algorithm achieves high estimation accuracy and fast convergence. The proposed algorithm is suitable for any subcarrier assignment schemes. In addition, as compared with other existing time domain based algorithms, which achieve the Cramer Rao Bound (CRB) at the price of unaffordable complexity, it closely matches their performance with over 70% computational saving.

In this work, we propose an iterative algorithm for the detection of transmitted symbols at the uplink of an orthogonal frequency-division multiple access (OFDMA) system. The algorithm allows distinct frequency-offsets (FO)s from each user that cause self- and multiple-access-interference. The proposed algorithm squeezes the interference of subcarrier k into 2D + 1 nearby subcarriers by preprocessing the received signal, which yields a banded structure interference matrix. This banded structure is exploited to realize a low complexity iterative soft-interference-cancellation minimum mean-squared error (SIC-MMSE) equalizer that can be used in Turbo equalization. Simulation results show that the bit-error-rate (BER) performance of the proposed algorithm outperforms existing detection algorithms and is very much close to the zero-FO frequency- domain-equalization (zero-FO-FDE) at low computational cost.

In this work, we propose an iterative algorithm for the detection of transmitted symbols at the uplink of an orthogonal frequency-division multiple access (OFDMA) system. The algorithm allows distinct frequency-offsets (FO)s from each user that cause multiple-access and self interference. The proposed algorithm squeezes the interference of subcarrier k into 2D + 1 nearby subcarriers by preprocessing the received signal and yields a banded structure interference matrix. Here, the value of D depends on the FO and determines the squeezing depth. The proposed algorithm exploits this banded structure and realizes a low complexity iterative soft-interference-cancellation minimum mean-squared error (SIC-MMSE) equalizer that can be used in Turbo equalization. Simulation results show that the bit-error-rate (BER) performance of the proposed algorithm outperforms existing detection algorithms and is very much close to the zero-FO frequency-domain-equalization (zero-FO-FDE).

For the uplink transmission of an OFDMA system, frequency synchronization is particularly challenging due to the presence of multiple Carrier Frequency Offsets (CFOs). This problem is investigated in this paper, and we propose a novel low complexity pilot aided frequency synchronization algorithm based on two consecutive OFDMA blocks, which are assigned with identical tile structure and pilot symbols. The pilot symbols can be used for both the CFO estimation and the channel estimation and hence effectively increases the spectral efficiency, since no additional pilot symbols or training sequence are needed exclusively for the CFO estimation. However, the solution to this multi-parameter estimation problem is prohibitively complex since it demands a multi-dimensional search. To solve this problem, we propose an iterative joint CFO estimation and compensation algorithm, which reduces the multi-dimensional search to only one simple one-dimensional search. In addition, the proposed scheme allows the CFO estimation and compensation to operate without the channel information and thereby further reduces the computational complexity. We analyze the identifiability in the CFO estimation and derive the analytical mean square error (MSE) of the CFO estimates to serve as the performance benchmark. The simulation results show that the synchronization algorithm achieves fast convergence in two iterations, and the accuracy of the CFO estimates matches the benchmark performance closely at moderate and high SNRs and outperforms an existing similar algorithm proposed in the literature.

In the uplink of orthogonal frequency division multiple-access (OFDMA) systems, before the data detection could be carried out, the carrier frequency offset (CFO) must be estimated and compensated to eliminate the inter-carrier interference (ICI) and multi-user interference (MUI), that is to restore the orthogonality among subcarriers. This paper proposed a novel pilot aided joint carrier frequency offset estimation and compensation algorithm for the uplink of OFDMA systems. By exploiting the pilot symbols, which are inserted for the purpose of channel estimation, the algorithm realizes band efficient, joint CFO estimation and compensation without using channel information. This algorithm provides more accurate estimation since the data symbols and pilot symbols are in the same block, this is particularly important in a fast time varying environment. The computational complexity is greatly reduced by avoiding too much matrix inverse calculation. Simulation results demonstrate that the algorithm could achieve the Cramer-Rao Bound (CRB) with a few iterations at moderate SNR.

This work investigates the up-link capacity of a multiple-input multiple-output (MlMO) wireless channel subject to finite multipath scattering in the presence of co-channel interference (CCI). We first consider single interferers with fixed position and then extend to cellular systems. We observe that the average capacity of MlMO system in the presence of interference is significantly greater than that of a single-input single-output (SISO) system and a single-output multiple-output (SIMO) system at moderate SNRs. It is presented that the link capacity of a MIMO system could be maximised by means of a spatially-whitened matched filter, which converts the interference uncorrelated between receive antennas. It is shown that the capacity of the finite scatterers channel can be even larger than that of the independent Rayleigh channel through the use of spatial pre-whitening filter.

This paper examines the problem of carrier phase recovery in turbo-coded systems. We introduce a new concept of "a priori probability aided phase estimation", where the extrinsic information (log-likelihood ratio) obtained from turbo decoder is used to aid an iterative phase estimation process, which is based on a maximum-likelihood strategy. The phase estimator operates jointly with the turbo decoding rather than separately prior to the decoder as in traditional approaches. This technique provides reliable phase estimation with variance of estimation errors approaching the Cramer-Rao bound at very low signal-to-noise ratio and allows robust decoding with a wide range of phase errors. This paper addresses its application in turbo-coded binary phase-shift keying and quaternary phase-shift keying systems over the additive white Gaussian noise channel. The bit-error-rate performance is investigated and shows that the performance of this technique is very close to the optimally synchronised system and significantly outperforms the traditional non-data-aided method without using additional pilot symbols.

A timing recovery system is proposed based on maximum likelihood estimation, suited to coding systems employing iterative soft-in/soft-out decoding like turbo codes, as this method utilises the soft-output from the decoder to assist in the timing estimation. Timing recovery is combined with decoding through an iterative technique in contrast to the traditional approaches where timing recovery is carried out prior to decoding. The timing estimate is one of the zeros on the S-curve (the log-likelihood function derivative) corresponding to the maximum of the log-likelihood function. It is resolved using an interpolation algorithm based merely on four samples and avoids generating the whole S-curve for every block. The synchroniser has effectively no acquisition period. The performance of the estimator is analysed in terms of the mean and the variance of the timing estimate, and it is shown that it provides a significant improvement over the non-data aided (NDA) method and approaches the results obtained from the data aided (DA) method (which ideally assumes the data is known and available all the time) after 5 iterations.

A new method of carrier phase recovery is implemented with the assistance of the extrinsic information from the turbo decoder. BPSK systems using turbo-codes over an AWGN channel with unknown phase are investigated. The phase recovery and turbo-decoding is implemented jointly and iteratively. The phase error can be corrected up to 82/spl deg/. The decoding can be made robust against phase uncertainty. The results show that this joint method gives a great improvement over the traditional techniques, which implement separate synchronisation prior to feeding the signal to the decoder. A further improvement of this method is also proposed, which can remove most of the phase errors completely or recover any phase error over 2/spl pi/ although it sacrifices some performance.

This paper proposes a carrier phase recovery approach when turbo codes are used. The phase estimation is implemented with the aid of the extrinsic information from the turbo decoder. A series of look-up tables are pre-computed to reduce the computation complexity, and thereby we avoid introducing delay to the decoding. Simulations are carried out both in BPSK and QPSK systems. If the block size is 1024, a phase error up to 82/spl deg/ in a BPSK system, and up to 37/spl deg/ in a QPSK system can be removed completely. Compared with the conventional method, which has separate phase recovery and decoding, this approach exhibits a great improvement. The effect of block size is also considered. The results demonstrate that the longer the block size, the better the performance.