With the development of 5G-Advanced, traditional industries are gradually transforming towards intelligentization. However, the uncertainty of wireless communication has an impact on the stability of industrial wireless network control system (IWNCS). Therefore, this paper mainly studies the stability optimization problem of IWNCS in ultra-reliable and low-latency communications (URLLC). We quantify system stability through real-time instability probability, which accounts for control-communication dependencies, and propose an interference-aware greedy clustering and deep reinforcement learning (IAGC-DRL) optimization algorithm to enhance control stability. Specifically, in large-scale networks, we propose a Proximal Policy Optimization (PPO) combined with greedy search to dynamically allocate uplink resource blocks (RBs) and power resources for each sampling period. This approach enables real-time adjustment of transmission performance across diverse industrial equipment, thereby optimizing the system’s instability probability. Simulation results show that the proposed algorithm outperforms other algorithms in improving the stability. And the real-time instability probability demonstrates superior performance in enhancing IWNCS stability when used as the optimization objective.

Uplink grant-free (GF) transmission with K-repetition can facilitate hyper-reliable and low-latency communication (HRLLC) among industrial internet of things (IIoT) devices by saving the time of requesting/waiting for the scheduling grant. Reliability analysis based on packet loss rate (PLR) establishes essential theoretical foundations for system evaluation. However, assessing the PLR in GF uplink IIoT networks for multiple HRLLC services in dynamic interference environments remains a critical challenge. This paper proposes a unified framework for evaluating the PLR, which first employs an interference-aware optimal clustering algorithm to systematically identify all possible interference patterns affecting a terminal. For each identified interference pattern, the PLR is analyzed independently. Therefore, a comprehensive PLR of each service is obtained by combining these individual PLR values through probability-weighted summation. It differs from most studies that rely on interference-averaging methods. The numerical results indicate that the proposed evaluation method enhances PLR accuracy by 10 dB compared to the benchmark.

Millimeter wave (mmWave) has emerged as a key technology for next-generation communications to address spectrum scarcity in traditional low-frequency wireless systems. However, its dynamic channel, high propagation losses and blocking susceptibility impose higher demands on resource allocation in complex interference environments, especially in factory scenarios. Efficient resource allocation in mmWave systems relies on accurately identifying and modeling interference, but current prevalent research remains constrained by idealized channel state information (CSI) assumptions and statistical modeling approaches that focus on stable CSI and consistent interference and noise. These conventional methods are inadequate for transient systems with instantaneous channel variations. To overcome these challenges, this paper proposes an interference identification model based on directional beams, which integrates the cluster scattering characteristics of mmWave signals and formulates the received power as the sum of the directional beam gain-weighted multipath powers. Using intelligent algorithms, we train this model to precisely identify interference and match arbitrary input resource relationships to achieve accurate signal-to-interference-plus-noise ratio (SINR) prediction for supporting the subsequent resource allocation. Numerical results demonstrate that the proposed algorithm improves the prediction accuracy by 4.7% − 35.1% compared to the benchmark scheme.

In sixth-generation (6G) networks, the dense deployment of femtocells will result in significant co-channel interference. However, current studies encounter difficulties in obtaining precise interference information, which poses a challenge in improving the performance of the resource allocation (RA) strategy. This paper proposes an intelligent scheme aimed at achieving energy-efficient RA in uplink scenarios with unknown interference. Firstly, a novel interference-inference-based RA (IIBRA) framework is proposed to support this scheme. In the framework, the interference relationship between users is precisely modeled by processing the historical operation data of the network. Based on the modeled interference relationship, accurate performance feedback to the RA algorithm is provided. Secondly, a joint double deep Q-network and optimization RA (DORA) algorithm is developed, which decomposes the joint allocation problem into two parts: resource block assignment and power allocation. The two parts continuously interact throughout the allocation process, leading to improved solutions. Thirdly, a new metric called effective energy efficiency (EEE) is provided, which is defined as the product of energy efficiency and average user satisfaction with quality of service (QoS). EEE is used to help train the neural networks, resulting in a superior level of user QoS satisfaction. Numerical results demonstrate that the DORA algorithm achieves a clear enhancement in interference efficiency, surpassing well-known existing algorithms with a maximum improvement of over 50%. Additionally, it achieves a maximum EEE improvement exceeding 25%.

The proliferation of connected devices in wireless systems result in severe intercell interference (ICI). To mitigate ICI, an accurate interference model of link-granularity is highly beneficial. The data generated by wireless devices during networks operation can contribute to mining the interference relationship if properly utilized. Existing frameworks of data-driven interference modeling require data collected from the entire network. Such data are unobtainable in a large network due to the communication constraints of a single network node. This paper proposes an uplink interference modeling algorithm that takes as input area-limited wireless big data instead of global data. With interference modeling formulated as a supervised learning task, the proposed algorithm can precisely model the interference relationship between an interested user equipment (UE) and any of its primary interfering users. Simulation shows that under high interference scenario the proposed algorithm achieves the same signal to interference plus noise ratio (SINR) prediction accuracy with significant reduction in training complexity compared to baseline algorithms.

This article addresses the problem of identifying disconnected agents in multiagent systems via external estimators. Specifically, we employ external estimators with an appropriately designed decision rule to identify the disconnectedness (i.e., the status of being disconnected) between two arbitrarily chosen agents in formation-control multiagent systems. The design of the decision rule is inspired by the unit-root testing problem of autoregressive time series. To make the best possible decision, a best-effort procedure is also proposed. Then, by introducing the concept of connected components (or just components) in graph theory, and using the methods of consensus analysis and time-series analysis, we develop an analytical framework to show the theoretical performance of the designed decision rule. A particularly important result shown by our analysis is that the miss probability of the decision rule can converge to 0 as the number of data samples increases. Finally, simulation results validate the performance of the decision rule and the best-effort procedure, showing that they can perform well even in small samples.

The Cell-Free Massive Multiple-Input Multiple-Output (CF-MIMO) system is a promising technology for beyond-fifth-generation (B-5G) networks. It involves deploying multiple access points (APs) with multiple antennas to serve groups of users (UEs) cooperatively. In this paper, we introduce two algorithms that utilize big data technology for interference identification and signal-to-interference-plus-noise ratio (SINR) prediction. These algorithms effectively identify user-level interference information and provide support for resource allocation. They outperform traditional machine learning methods in terms of accuracy, time efficiency, and computation complexity by a significant margin. To establish the theoretical foundation, we derive the closed form of the average SINR based on large-scale fading coefficients (LSFCs). Our results demonstrate that our algorithms significantly enhance prediction accuracy by 50%-75% and achieve an impressive 8-to 80-fold improvement in training efficiency compared to the benchmark scheme.

Satellite constellation is envisioned as an important approach to enhancing the capability of existing networks. Technologies such as Network Function Virtualization (NFV) and Service Function Chain (SFC) have been proposed to make satellites much more tolerant to different kinds of service requests. Owing to Software-Defined Networks (SDN), satellite constellations can be managed in a more flexible manner. However, due to the high variability of constellation and multi-types of Virtual Network Functions (VNFs), it becomes a major challenge to select appropriate instances of VNFs to concatenate for routing when orchestrating resources to map SFC requests. In this paper, we investigate SFC Mapping Problem (SFC-MP) in a dynamically changing logical segmented network to provide stable communication service. We formulate the problem as a Binary Integer Programming (BIP) model. In addition, we propose a new Continuous Logical Segmented Network Resource Aware Routing (LSNRAR-Continuous) algorithm for SFC requests routing to achieve network load balancing and reduce the impact of VNF migration on services. Simulation results show that the algorithm can effectively solve the SFC-MP problem, and the proposed algorithm has higher performance in terms of average delay, mapping cost and service stability compared with existing literature algorithms.

The fifth-generation (5G) wireless network is expected to support emerging applications requiring ultra high reliability and low latency, such as self-driving cars, factory automation (industry 4.0), and smart grid, known as ultra-reliable and low-latency communications (URLLC). Uplink grant-free (GF) transmission is considered as a promising technology for supporting the rigorous requirements of URLLC by saving the time of requesting/waiting for the scheduling grant and supporting the K-repetition transmission. Besides, the intercell interference (ICI) in uplink multi-cell GF transmission is another critical issue to be solved. In this paper, we propose an interference-aware based radio resource configuration framework of URLLC uplink GF transmission which means that we can configure the radio resources by utilizing the available interference information to mitigate the impact of severe ICI on the transmission performance in URLLC. Numerical results show that, the proposed scheme can greatly improve the total transmission reliability and has higher scalability and robustness compared to prior art solutions under the condition of satisfying the transmission delay requirement and resource constraint.

The severe inter-cell interference (ICI) problem in ultra-dense networks seriously restricts the capacity of the wireless network. The major bottleneck of this problem is the inability to obtain accurate interference information. However, interference information is hidden in data generated in wireless network. Motivated by this, this paper proposes a novel neural-network-based uplink interference identification algorithm to accommodate interference model. We design neural network based on interference model, use data generated in operating wireless network to mine inter-user signal-to-interference ratio information in wireless network, and realize accurate prediction of signal-to-interference-plus-noise ratio (SINR) without extra radio resource consumption. The simulation results show that the proposed algorithm could achieve the same level of prediction accuracy as the baseline algorithms with the training time two orders of magnitude lower, which is suitable for practical applications.

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-$Q$-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.

Ultra-dense network (UDN) is considered to be the key technology for the fifth generation (5G) networks to provide high capacity. However, intensive deployment of femtocells bring severe inter-cells interference (ICI), which greatly limits the performance of the network and the capacity gain the system can obtain. Therefore, the key to solve this problem is to obtain accurate interference information through accurate interference modeling. In fact, the wireless big data generated during the operation of the wireless network contains rich wireless interference information. Based on this, this paper proposes an uplink interference identification and signal-to-interference-plus-noise ratio (SINR) prediction algorithm based on XGBoost and interference model. The proposed algorithm uses the wireless big data generated during network operation to train the XGBoost algorithm, mining the signal-to-interference ratio (SIR) and signal-to-noise ratio (SNR) information between links in the wireless network without increasing the overhead of wireless resources, and then combining with the proposed interference model to achieve accurate prediction of the SINR. The simulation results show that when the training data of the target user reaches 5000 pieces, the prediction error of its SINR will be reduced to less than 0.5dB, which effectively reduces the requirement of data quantity and computing power, and can meet the practical application requirements.

Energy efficiency (EE) is acknowledged as a key performance indicator for 5G networks. This paper mainly studies the problem of energy efficient power allocation in 5G Ultra-dense network (UDN). The existing power allocation algorithms mainly focus on the downlink, and most of them are based on analytic algorithms, which has high computational complexity and is difficult to meet the needs of large-scale deployment in UDN. In order to reduce the complexity, this paper proposes an uplink power allocation algorithm based on multi-agent reinforcement learning (MARL). Each user acts as an agent and all the users interact with the communication environment simultaneously. In the MARL framework of the proposed algorithm, we add a performance estimator to help train Q-network. Simulation results show the proposed algorithm performs efficiently in term of energy efficiency as well as maintaining a high network throughput at the same time. The complexity of the proposed algorithm is proved to be reduced by at least two magnitudes compared with the analytic algorithms.

Ultra-dense network (UDN) is recognized as a key technology for the fifth generation (5G) network to deliver high capacity. Due to cell densification, UDN suffers from heavy intercell interference (ICI). To mitigate ICI, an accurate interference modeling is essential. Compared to distributed mechanism, centralized interference management is more adept at addressing server ICI problem, especially in dense deployments. Hence, we adapt centralized radio access network (C-RAN) architecture to obtain global channel state data. In this paper, a novel regression-based uplink interference identification and signal-to-interference ratio (SINR) prediction algorithm is proposed for C-RAN based 5G UDN. By leveraging big data technology with in-depth analysis of the cause of ICI, this algorithm can precisely model the interference relationship and thus achieve very accurate SINR prediction. In addition, the proposed algorithm is highly efficient in computing, as demonstrated by complexity analysis.

Due to the characteristics of being license-free, the ISM (Industrial, Scientific, Medical) bands are frequently utilized by wireless communication systems. However, the ubiquitous and unmanaged IEEE 802.11 systems deployed in the ISM bands may bring non-negligible interference to emergency communications. This paper considers suppressing the transmission of 802.11 systems in 2. 4GHz ISM band for protecting the public safety in emergency scenarios. To this end, two kinds of suppression frames that comply with the 802.11 protocol are analyzed, which respectively borrow the ideas of virtual carrier-sense attack and spoofing. The validity of each of the frames is then verified both qualitatively and quantitatively by the designed experiments with multiple commercial off-the-shelf (COTS) 802.11 devices. Based on which we propose an integrated suppression method that is effective for all tested devices. Further experiments and analysis show that the presented method outperforms the state-of-the-art suppression methods in terms of energy consumption and applicability.

Spectrum prediction has recently gained a lot of attention due to its extensive applications in cognitive radio networks. However, most of the related research assumed that the spectrum occupancy pattern is time-invariant, which limits the performance of proposed prediction methods for the high burst frequency bands, e.g. the ISM (Industrial, Scientific, Medical) bands. In order to improve the prediction accuracy for them, this paper first analyzes the characteristics of the collected real WiFi data in 2.4GHz ISM band, and shows the burstiness of the band from multiple aspects. Based on the analysis, we then propose a MultiLayer Perceptron based Reinforcement Learning (RLMLP) method which could adaptively select the corresponding predicting action according to the state to which any piece of data belongs. The state space of the method consists of multiple data categories that are determined by the results of feature partitioning, while the action space is composed of multiple MLPs with the same mapping structure. Finally, numerical results on the collected data show that the proposed RLMLP method is significantly better than the state-of-the-art algorithms in terms of the prediction performance.

The ultradense network (UDN) has been identified as a promising technology to address the challenge of the ever increasing demands on data rates or massive accesses, especially for Internet of Things (IoT)-oriented applications. However, the severe co-channel interference (CCI) generated by densely deployed femtocells in UDN poses a critical issue. The conflict graph is widely recognized as an effective representation of the underlying interference constraints in the network and a powerful tool for interference management. Different from most prior studies that construct conflict graphs based on accurate geographical distance information, which is usually hard to obtain in reality, an accurate and practical machine-learning-based conflict graph construction approach is proposed in this article. Based on the constructed graph, the throughput maximization problem, which is NP-hard, is decoupled into a user clustering subproblem and a subchannel allocation subproblem. The former is solved by proposing a low complexity user clustering algorithm with modified balanced Mink-Cut, which identifies low-interference entities (i.e., clusters) for spectrum reuse; and the latter is solved by presenting a subchannel allocation algorithm with accumulative intercluster interference considered, which could further reduce the interference caused by spectrum reuse. Moreover, to further improve the spectrum efficiency, a supplementary allocation algorithm is deployed to allocate the remaining subchannels. The simulation results show that the proposed approach improves the aggregate throughput by up to 186.68%, compared with the other existing methods.

We address the problem of learning hidden graph structure from spatiotemporal signals which are prevalent in distributed sensor networks. Based on a space-time representation model that takes into account correlated properties in dynamic evolution, we formulate the graph learning problem as a regularized multi-convex optimization problem. A correlation-aware and differential smoothness-based graph learning method (CADS) is proposed, which simultaneously estimates the time correlation of each vertex and refines the graph under the differential smoothness prior. The proposed method promotes such smoothness property in each learning step, leading to an improvement of learning accuracy. Experiments on synthetic and real-world datasets demonstrate the effectiveness of the proposed CADS which outperforms the state-of-the-art graph learning methods in classification and prediction tasks.

Sparsity based methods have gained its popularity in two-dimensional (2D) direction-of-arrival (DOA) estimation in recent years. However, these methods suffer from the off-grid problem, and also need additional angle pairing process, which results in degraded performance when applied in practice. In this paper, to address these problems, a novel and effective method named grid adaptive sparse Bayesian learning (GASBL) is proposed for 2D-DOA estimation with L-shape array from the perspective of sparse Bayesian learning. Specifically, an off-grid DOA model is proposed to enable grid adaptive refinement, and the auxiliary compound electric angle (CEA) is introduced to achieve automatic angle pairing of the elevation angles and azimuth angles. Then, a hierarchical probability framework with Laplacian prior is imposed. Finally, the 2D-DOA estimation problem is solved by Bayesian inferences. Compared with the state-of-the-art approaches, numerical results highlight the proposed method with more superior performance in terms of high angle resolution and robustness against the noise.

Graph learning often boils down to discovering the hidden structure of data that is characterized by a graph. Most of the previous works mainly focus on static data processing. However, the distributed sensor network gives rise to space- time data which exhibits spatiotemporal correlation and evolves smoothly over time. In this paper, we address the problem of learning graphs from space-time sensing data. Based on a dynamic model that takes into account both the spatial and temporal correlated property in temporal evolution, we propose a spatiotemporal smoothness-based graph learning method (GLSS), which novelly introduces the spatiotemporal smoothness to the field of space-time data analysis. By simultaneously recovering data and refining graphs under the spatiotemporal smoothness prior, the graph learning accuracy can be efficiently improved. Experiments on synthetic data and real-world data in weather sensor networks demonstrate that joint space-time analysis in proposed GLSS can bring benefits to graph learning and outperforms current the state-of-the-art methods.

Cognitive radio (CR) has received wide attention for enhancing the spectrum utilization. Spectrum prediction can be fully utilized in both time and frequency domains for cognitive access. In this paper, Long Short-Term Memory (LSTM) networks method is adopted for spectrum prediction. Based on prediction of the power of future time slots, the method can help to achieve spectrum utilization flexibility by calculating throughput of shared channels. A new spectrum access strategy, which integrates optimal spectrum sensing interval in time domain and channel selection based on the presented LSTM networks method in frequency domain, is proposed. In particular, for multiple channels of the shared spectrum, the optimal sensing interval of each channel is calculated, which is then adopted as the reference output length of the LSTM networks method, with predicted results, a feedback which consists of a preferred channel list will be conducted for spectrum access. With both real-life and generated data, the proposed LSTM networks method is verified, which outperforms commonly used Neural Network (NN) and Hidden Markov Model (HMM) methods. Numerical results also show that the proposed spectrum access strategy is capable of increasing throughput of the cognitive system and reducing energy consumption of spectrum sensing significantly.

In this paper, a power allocation scheme based on a stackelberg game model is proposed to coordinate complicated interference in heterogeneous network. Firstly, the power allocation problem is modeled as a stackelberg game where base stations (BS) are treated as competitive players. Then a two-stage pricing-based power allocation scheme is proposed, where macro-BS and micro-BS cooperate with each other in order to provide optimal power allocation strategy. Theoretical analysis and simulation results both validate that the proposed scheme not only makes improvement in spectral and energy efficiency, but also reduces computational complexity, especially in dense deployment scenario.

Cognitive satellite terrestrial networks have received widespread attention recently for improving the spectrum utilization. In this paper, a hybrid interweave-underlay spectrum access (HIUSA) scheme based on spectrum sensing in 5GHz license-exempt spectrum is proposed, which eliminates the performance degradation brought by inaccurate parameters estimation of the interference link in a large extent, and enhances the throughput of the cognitive system substantially. While analyzing relevant details, we also focus on the resource allocation (RA) problem in this scenario, the RA problem takes the maximum throughput of the cognitive satellite communications (SatCom) as target, and subjects to the requirements of cognitive terminals. Then, the convex RA problem is solved by a simplified iteration algorithm. Extensive simulation results are given to demonstrate the performance and effectiveness of the proposed HIUSA scheme as well as the RA approach, by contrasting with the legacy system as well as the common method.

Fractional frequency reuse (FFR) is an efficient management technique to eliminate inter-cell interference (ICI) in multi-cell orthogonal frequency division multiple access (OFDMA) networks through inter-cell coordination. Generally, the derivations of throughput and coverage probability are treated as separate problems or investigated in different ways. In this paper, we derive average cell throughput and coverage probability expression for both round robin (RR) and maximum SINR (MSINR) scheduling strategies with different subcarrier allocation schemes from the same starting-point, the cumulative distribution function (CDF) of the instantaneous SINR . Results of static and dynamic subcarrier allocation schemes are compared to verify that latter is more reasonable because it takes both throughput and fairness into consideration. Analytical and simulation results also show that average cell throughput increases and coverage probability decreases with the distance threshold. Based on this, we investigate the optimal distance threshold to attain as good balances between throughput and coverage with different number of users in a cell. In terms of base station (BS) power control, we demonstrate that there exists an optimal power to satisfy coverage probability and obtain maximum throughput no matter what scheduling strategy is applied.

Inter-Cell Interference (ICI) is always a key problem in Fractional Frequency Reuse (FFR) system to be focused on, which should be well resolved to pursue higher throughput and better power efficiency. In this paper, we investigate throughput and power efficiency of RR scheduling in FFR system by both closed-form expression and Monte Carlo trials method. We improve a previous throughput model by applying variable subcarrier distribution scheme and upgrading expression for the Cumulative Distribution Function (CDF) of SINR. Then we propose a new closed-form expression of average cell throughput. The fairness performance of this model is better than that using fixed subcarrier distribution scheme. Meanwhile, an optimal radius threshold is selected so as to achieve best throughput performance. Then we manage to explore the relationship between cell power efficiency and transmit power based on the optimal radius threshold. From both analytical and simulation results, it is shown that power efficiency is monotonically decreasing with transmit power. Hence operational power is introduced into power efficiency as energy consumption for base station. After that, the highest power efficiency can be reached by setting a proper and reasonable transmit power.

This paper analyzes a spectrum access scheme based on region division for Device-to-Device (D2D) communication underlaying cellular networks with outage constraint for cellular communication. The D2D pairs will access the spectrum by sharing the cellular uplink (UL) frequency resources in the networks. By utilizing stochastic geometry, D2D accessing the frequency spectrum can be modeled as the probability that the cellular user (CeUE) SINR is below an appropriate threshold. And the constraint of the probability appears as a transcendental equality, so we figure out the solution with the help of Lambert W function and obtain the closed-form solution of the region division in the whole cell when the cellular location are determined. Then in the simulation, we analyze the relationship between system capacity and key parameters including the CeUE location, the D2D density and D2D output power in detail. The results show that the proposed scheme can make the D2D pairs access the frequency spectrum in the networks effectively.

A resource allocation scheme involving several Device-to-Device (D2D) multicast groups underlaying OFDMA-based systems is proposed in this paper. In order to guarantee Quality of service (QoS) of cellular users (CeUEs), we define the signal-to-interference-pulse noise ratio (SINR) threshold value for CeUEs. Meanwhile, the minimum throughput of a single D2D multicast group is predetermined to maintain fairness of each D2D multicast group. Since the subcarrier assignment and power allocation are a joint optimization problem with complex calculation, the proposed scheme is divided into two steps. First, subcarriers are allocated under the premise of ensuring the minimum throughput of the D2D multicast groups and the QoS of CeUEs. Then by applying an approximation form of Shannon capacity, the power allocation problem can be solved by convex optimization. Simulation results show that the proposed scheme improves sum-throughput significantly as well as fairness. Besides, the sum-throughput increases with the increasing number of D2D multicast groups.

Cognitive relay networks have been studied for the advantages of improving the throughput of secondary users. Relay location and power allocation are the key issues in cognitive relay networks, which impact on the system performance. In this paper, we analyze the problem of optimal relay location and power allocation in cognitive relay networks. Firstly, we deduce an ergodic capacity formula for the secondary users reuse the resource of one primary user in Rayleigh-fading channels, and the formula is used in the terms of high signal-to-noise ratio (SNR) and high interference-to-noise ratio (INR). Then, the expressions for optimal relay location and power allocation are derived from the deduced formula when the distances among primary users and secondary users are known. At last, an ergodic algorithm for finding the results is given. The simulation proves the proposed ergodic capacity formula is correct. The ergodic algorithm is also verified by simulation, and the optimal relay location and power can be also determined.

This paper analyzes the optimal power and density allocation for D2D (Device-to-Device) communication in heterogeneous networks on multi-bands with target of maximizing D2D achievable transmission capacity. The heterogeneous networks contain one or several cellular systems, and D2D communication shares uplink resources with them. By utilizing stochastic geometry, the problem is formed as sum capacity optimization for D2D network with constraints that guarantee outage probabilities of both cellular and D2D transmissions. The original problem is non- convex but we find the D2D transmission capacity has a remarkable property: it is sectional continuous with limited discontinuous point in the feasible region of power and density allocation and the maximum capacity is reached on the critical point. Based on this, we propose a algorithm as follows: first we figure out all of critical points as a candidate set and then obtain the optimal solution from them. The numerical results demonstrate the effectiveness of the algorithm and it shows that optimal parameters of D2D transmission is affected by outage constraints as well as the interference from cellular systems.

This paper analyzes the optimal density and power allocation for D2D (Device-to-Device) communication in heterogeneous networks on multi-bands with target of maximizing D2D transmission capacity. The heterogeneous networks contain one or several cellular systems, and D2D communication shares uplink resources with them. By utilizing stochastic geometry, it is formed as a sum capacity optimization problem for D2D network with constraints that guarantee outage probabilities of both cellular and D2D transmissions.Since the original problem is non-convex, we divide the proof into two steps: first we prove the power allocation problem is convex when the D2D density is fixed, which can be solved by lagrangian method; then we prove that the optimal D2D density exists in a semi- closed interval.We propose a linear searching algorithm based on the former conclusions:with discretizing the interval of D2D density, a series of solutions can be obtained by solving the optimization problem of each D2D density; hence the global optimal capacity can be selected from them.The simulation results demonstrate the effectiveness of the proposed algorithm and it also shows that optimal parameters of D2D transmission is affected by outage constraints as well as the interference from cellular systems.

The development of the storage capacity and electricity of handheld devices makes it possible to share multimedia data (e.g. video) through Device-to-Device (D2D) communication. In this paper, we present a cluster-based multicast transmission method for D2D communication with the target of decreasing the data distribution time. We consider the users who store multimedia data serve as cluster head (CH) and distribute data to the users around them in D2D multicast mode. The CHs and users in the system compose different clusters and there is only one CH in a cluster. We derive out outage capacity of a single cluster and establish a clustering selection model for the whole system. Since the clustering selection is a non-convex problem, we propose a practical clustering strategy with theoretical analysis using game theory. Simulation results show that the clustering strategy lowers the average transmission time effectively and applies to the situation of high user density.

This paper proposes the problem that the direct communication between two D2D UEs is impossible since the outage probability of D2D communication goes beyond the threshold defined by system. The problem is solved by applying relay to Device-to-Device (D2D) communication, which is defined as D2D communication with relay mode. In this communication mode, relay is selected from idle Cellular User Equipments (CeUEs), and both of two hops in it are D2D communication. In this paper, we provide the criteria of employing this communication mode. First we derive out the interference constrained precondition for applying the mode of D2D communication with relay based on outage probabilities analysis, and then the criterion of employing D2D communication with relay mode is proposed by comparing the sum-capacity of this mode with that of cellular communication mode on the basis of the optimal relay selection. By simulating, we compare the occurrence probabilities of the modes of D2D communication, cellular communication and D2D communication with relay. The numerical results also reveal the D2D communication with relay mode can improve the average sum-capacity in contrast with cellular communication mode based on the proposed criteria.

This study concerns the approximate distribution of the random variable log 2 ( A + χ 2 ) and its applications in the performance analysis of communication systems where A is 0 or 1 and χ 2 a chi‐square distributed random variable. The authors prove that log 2 ( A + χ 2 ) is approximately Gaussian distributed and derive the expressions of the mean and variance of the approximate distribution. The approximate Gaussian distribution provides a new way to simplify the derivation of performance metrics and obtain analytical results. Then the authors utilise the approximate results in two applications, which are the approximation of the Gaussian Q ‐function and capacity analysis, respectively. For one thing, the approximate distribution can be explored to deduce a new approximate equivalent expression of the Q ‐function on the basis of which an accurate approximation of the Gaussian Q ‐function can be developed. For another, the approximate Gaussian distribution provides an intuitive approach to analyse the channel capacity of Rayleigh‐fading multiple antenna systems. The approximate statistical distributions of the channel capacities of orthogonal space‐time block codes, single‐input multiple‐output, multiple‐input single‐output, transmit antenna selection with maximal‐ratio combining and multiple‐input multiple‐output systems are presented. The applicability of the approximate distribution can be demonstrated through simulation results in both applications.

This paper analyzes the optimal D2D user allocation over multi-bands in the heterogeneous networks. The heterogeneous networks contain one or several cellular systems and D2D communication shares uplink resource with them. By allocating D2D users on different bands, it can reduce the interference between D2D and cellular systems and improve D2D transmission capacity at the same time. Through utilizing stochastic geometry, the problem is formed as sum D2D transmission capacity on each band with constraints that guarantee outage probilities of both cellular and D2D transmission. The primal problem is first proved to be convex and then solved by constructing Lagrange function and KKT conditions. The optimal D2D user densities over multi-bands are derived and we propose a D2D scheduling algorithm base on this conclusion for the dynamic D2D access process. Simulation results show the superiority of optimal user allocation over average allocation method.

This paper analyzes the underlay and overlay mode selection of Device-to-Device (D2D) communication in the LTE-Advanced single-cell scenario. The two cases that the cell contains relay node or not are considered, and the study focuses on the location relationship between cellular UE and D2D UE. Our evaluation shows that whether D2D communication can reuse the cellular uplink resource or not is affected by the system parameters. Specifically the underlay mode is preferred when the cellular user is closer to the BS or relay node than the D2D user. The simulations show that the introduction of relay node enables D2D pairs to have more chances to share the resources with cellular user in the underlay mode.

In this paper, we propose admission control and power optimization algorithms for Device-to-Device (D2D) communication which share uplink resources with cellular system in underlay mode. In the presence of quality of service (QoS) protection (both for D2D and cellular users) and transmit power limit, we propose a set-based admission control (SAC) algorithm in which some novel strategies of sets adjustment are presented to handle the violations of constraints. By using SAC, we get a set which contains admitted D2D links (DLs) as many as possible, and other DLs are rejected, but the D2D system capacity is not optimal. Then, we propose a distributed power optimization (DPO) algorithm to maximize the D2D system capacity after implementation of SAC. In DPO, QoS protection is also provided, each DL uses gradient descent method to update its transmit power, and DLs only need to exchange “price” messages which indicate the effect of interference. Through the numerical results, we demonstrate the feasibility and satisfying performance of proposed algorithms compared to traditional algorithms.

In this paper, the early-termination fixed-complexity sphere detector (ET-FSD) that is a channel-adaptive version of FSD, is developed to exploit scale effects for the multiple-antenna system which has to detect signals of multiple users under the constraint of sum complexity (e.g., the base-station systems always encounter the run-time limit of signal detection of all the users). In order to apply the concept of scale effects in microeconomics to the study of ET-FSD, the corresponding mathematic model primarily based on the large deviation principle is established. The key evidence that tells the existence of scale effects in ET-FSD is presented instead of the complete proof due to the space limitation. We also argue that within multiple-user systems the scale effects can be utilized to realize ET-FSD that has both the optimal performance in the point view of diversity order and the polynomial time unit complexity in the worst-case sense (one can think of unit complexity as the complexity per user in multiple-user scenario). Finally, we provide the numerical results to support the theoretical analysis.

Geometry-based optimal power control was proposed in [14] to transform the power-control problem to a new geometrical problem on the position relationship between a line and some points. This scheme provides a novel visual perspective and lowers the complexity of optimization. We generalize this scheme to a larger class of power-control optimization problems so as to maximize the network utility with multiple average and peak power constraints in wireless networks. To facilitate the handling of the geometrical model, we define a subset of geometrical models with specified characteristics, called a regular geometrical model, and derive the type of power-control problems eligible for the regular geometrical model. For such a type of problems, two strategies are proposed for the construction of the regular geometrical model. Utilizing geometrical properties, we propose a novel geometry-based optimization scheme for the general power-control problem. Its computational complexity is significantly lower than the conventional algorithms. We also provide a further discussion on irregular geometrical model cases. Finally, we provide two examples of deploying the proposed geometry-based power-control scheme.

In this paper, the theoretical insight into Gaussian approximation of channel capacity for several classes of Rayleighfading multiple-antenna systems is presented. In particular, a theorem, which has not been developed by the former studies of Gaussian approximation, is introduced to these systems. Then guided by this theorem, a new approach of determining the approximate mean and variance for Gaussian approximation is proposed, which is simpler and more intuitive than the existed approach. The simulation results show the availability of such new approach.

The convergence of non-cooperative distributed power control in Gaussian interference channel is analysed in this study. Firstly, the existing distributed power control schemes are categorised as two types: gradient projection type and non-linear type, according to the iterative steps. A unified mathematical formulation is then provided for each type. The objective is extended from rate maximisation, which has been studied a lot in iterative water-filling game, to more general form of utility maximisation. Based on it, a set of sufficient conditions are derived for each type of the schemes, guaranteeing the uniqueness of the fixed point and the global convergence in a totally asynchronous manner. These convergence requirements can be interpreted as different degree of separation, according to the concrete form of utility function. To get insight into the derived convergence conditions, some numerical results are presented in the end.

The convergence of non-cooperative distributed power control in Gaussian interference channel is analyzed in this paper. Firstly, the existing distributed power control schemes are categorized as two types: gradient projection type and non-linear type, according to the iterative steps. A unified mathematical formulation is then provided for each type. The objective is extended from rate maximization, which has been studied a lot in iterative water-filling game, to more general form of utility function. Based on it, a set of sufficient conditions are derived for each type of the schemes, guaranteeing the uniqueness of the fixed point and the global convergence in a totally asynchronous manner. These convergence requirements can be interpreted as different degree of separation, according to the concrete form of utility function. To get insight into the derived convergence conditions, some numerical results are presented in the end.

It is expected that Device-to-Device (D2D) communication is allowed to underlay future cellular networks such as IMT-Advanced for spectrum efficiency. However, by reusing the uplink spectrums with the cellular system, the interference to D2D users has to be addressed to maximize the overall system performance. In this paper, a novel method to deal with the resource allocation and interference avoidance issues by utilizing the network peculiarity of a hybrid network to share the uplink resource is proposed and the implementation details are described in a real cellular system. Simulation results prove that satisfying performance can be achieved by using the proposed mechanism.

A hybrid system consisting of a cellular network and a device-to-device (D2D) network is considered in this paper where the D2D users operate in an underlay mode and reuse the spectrums with the cellular users. Most researches focus on reusing the uplink spectrums but how to share the downlink frequency bands is seldom addressed. To share the downlink spectrums and avoid the interference to the primary cellular devices, a labeled time slots based mechanism is proposed and the implementation details are described in a real cellular system. Simulation results prove that satisfying performance can be achieved by using the proposed mechanism.

In this letter, a power-control scheme for maximum sum-rate is proposed for the fading multiple access channels by considering the presence of primary users. Both the average transmit-power constraints and the peak interference-temperature constraints are considered. The interference caused by cognitive users must be under a pre-specified threshold for protecting primary users. The power-control optimization is considered as a novel geometrical problem which investigates the relationship of positions of a line and a few points. At most two users transmit simultaneously for optimality and the corresponding conditions are provided for both cases. Based on the analysis, the optimal power control is given for each specific fading state. For lowering computational complexity, the power-control optimization problem is divided into two categories according to different tight constraints. Simulation results are provided for the optimal power-control performance.

Cognitive radio makes it possible for an unlicensed user to access a licensed spectrum opportunistically on the basis of non-interfering. This paper addresses the problem of joint route selection and resource allocation in OFDMA-based multihop cognitive radio networks, in the objective of optimizing different types of end-to-end performance. Aiming to solve it optimally, we first show that this problem of optimal resource allocation can be formulated as a convex optimization problem and identify its necessary and sufficient conditions. Based on this conclusion, we propose an iterative algorithm that can be implemented in a distributed manner. This algorithm applies Lagrangian duality theory and the Frank-Wolfe method. The scheme thus converges to a globally optimal solution. We present numerical results from using the algorithm to provide insight into the optimal cross-layer design, e.g., the relationship between bottleneck throughput and hops, and the effect of interference temperature constraints.

A hybrid system of cellular mode and the peer-to-peer (P2P) mode is considered in this paper, where the cellular uplink resource is reused by the P2P transmission. In the objective of overall system throughput maximization, we addresses the problem of distributed power control of P2P transmission in the hybrid system model. Some preliminary results of the problem are presented first to investigate the optimal solution of the problem. Based on these results, by applying the game theory approach, that is, potential game, an asynchronously distributed power control scheme is devised. The associated properties of the proposed scheme are analyzed, including the global convergence, the conditional optimality and the Lyapunov stability. In the end, simulation is conducted to study the performance of the proposed scheme, which shows satisfying results.

A hybrid system of cellular mode and device-to-device (D2D) mode is considered in this paper, where the cellular uplink resource is reused by the D2D transmission. In order to maximize the overall system performance, the mutual interference between cellular and D2D sub-systems has to be addressed. Here, two mechanisms are proposed to solve the problem: One is mitigating the interference from cellular transmission to D2D communication by an interference tracing approach. The other one is aiming to reduce the interference from D2D transmission to cellular communication by a tolerable interference broadcasting approach. Both mechanisms can work independently or jointly to synergy the transmission in the hybrid system for the efficient resource utilization. In the end, simulation is conducted to study the performance of the proposed schemes, which shows satisfying results.

In cognitive radio networks, the limitation of control channel bandwidth is a challenge of cooperative spectrum sensing when the number of cognitive users becomes very large. Cluster- based architecture is applied for cooperative sensing to avoid the congestion on control channel and reduce the sensing delay. In this paper, we propose a cluster-based cooperative spectrum sensing scheme to improve the efficiency of the network. The number of clusters effects both the system efficiency and detection performance significantly. By balancing the tradeoff between the communication overhead and sensing reliability, we can obtain the optimal number of clusters, which can minimize the cooperation overhead without any performance loss of reliability. Moreover, a clustering strategy is proposed based on a given number of clusters and simulation results show the superiority of the proposed strategy.

The MAC-layer sensing, as a key component of spectrum sensing in cognitive radio, concerns the sensing mechanism design to determine when to sense and access which channel. An important issue of MAC-layer sensing is to find an available channel with a minimized searching delay for the cognitive transmission without any harmful interference. In this paper, we focus on this issue and propose adaptive searching scheme and cluster-based searching scheme to reduce the searching time when channel conditions are various and the cooperative sensing is applied. Moreover, the optimization of these two schemes are also developed in this paper. By adaptive selecting the searching parameters according to the current environment, the average consumed time of channel searching can be minimized, while the searching quality is also satisfied. The simulation results verify the confidence of proposed schemes and show a better searching performance when combining these two schemes.

In this paper, we investigated the end-to-end throughput of a chain in Cognitive Radio Networks (CRNs). We found that, the end-to-end throughput is dependant on both the primary usage patterns and the transmission scheduling scheme being used. In addition, to increase the end-to-end throughput of a Cognitive Radio (CR) chain, the scheduling scheme should be adjusted according to the primary usage patterns of the CR links in the chain. In the paper, firstly, we proposed an algorithm to approximate the achievable end-to-end throughput considering the primary usage patterns by abstraction and iteration. Then, a novel layered packets transmission scheduling scheme was proposed in attempt to realize the approximated end-to-end throughput. Finally, extensive simulations were conducted and results showed that, using proposed transmission scheduling scheme, the achievable end-to-end throughput of a CR chain is increased by considering the primary usage patterns and the final end-to-end throughput is close to the approximation.

Cognitive radio makes it possible for an unlicensed user to access a licensed spectrum opportunistically on the basis of non-interfering. This paper addresses the problem of resource allocation for multiaccess channel (MAC) of OFDMA-based cognitive radio networks, taking into account of the interference temperature constraints. The objective is to maximize the system utility, which is used to quantify different quality-of-service (QoS) requirements of different users. Firstly, a theoretical framework is provided, where necessary and sufficient conditions for optimal subcarrier assignment and power allocation are presented under certain constraints. Then, an effective algorithm is devised for more practical conditions based on Lagrangian duality theory, where subgradient/ellipsoid method is applied for Lagrangian multipliers iteration. With polynomial time complexities, the proposed resource allocation algorithm is proved to achieve optimal system performance by numerical results.

In this paper, the Cramer-Rao lower bound (CRLB) for spatial correlation matrices is derived based on a rigorous model of the doubly selective fading channel for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Adopting an orthogonal pilot pattern for multiple transmitting antennas and assuming independent samples along the time, the sample auto-correlation matrix of the channel response is complex Wishart distributed. Then, the maximum likelihood estimator (MLE) and the analytic expression of CRLB are derived by assuming that temporal and frequency correlations are known. Furthermore, lower bounds of total mean squared error (TMSE) and average mean squared error (AvgMSE) are deduced from CRLB for asymptotically infinite and finite signal-to-noise ratios (SNR's), respectively. According to the lower bound of AvgMSE, the amount of samples and the order of frequency selectivity show dominant impact on the accuracy of estimation. Besides, the number of pilot tones, SNR and normalized maximum Doppler spread together influence the effective order frequency selectivity. Numerical simulations demonstrate the analytic results.

In cognitive radio networks, the unlicensed users can utilize the unoccupied licensed spectrum opportunistically. In this paper, we propose a joint power and end-to-end rate control algorithm considering restricting the interference to licensed users. By duality theory, the optimal resource allocation solution is given for the unlicensed users while satisfying the interference temperature limits. An asynchronous algorithm is proposed to be implemented in practical networks. Finally, we give a discussion to the proposed algorithm's performance on fairness. Numerical results show that the proposed algorithm can limit the interference to licensed user under a predefined threshold while maintaining a satisfied data rate fairly.

In this letter, a novel maximum Doppler spread estimation algorithm is presented for OFDM systems with the comb-type pilot pattern in doubly selective fading channels. First, the least-squared estimated channel frequency responses on pilot tones are used to generate two auto-correlation matrices with different lags. Then, according to these two matrices, a Doppler dependent parameter is measured. Based on a time-varying multipath channel model, the parameter is expanded and then transformed into a nonlinear high-order polynomial equation, from which the maximum Doppler spread is readily solved by using the Newton's method. The delay-subspace is utilized to reduce the noise that biases the estimator. Besides, the subspace tracking algorithm is adopted as well to automatically update the delay-subspace. Simulation results demonstrate that the proposed algorithm converges for a wide range of SNRs and Dopplers.

Cognitive radio makes it possible for an unlicensed user to access a spectrum opportunistically. This paper addresses the problem of power control in cognitive radio networks, to maximize the system utility in the presence of interference temperature constraint. Penalty function is applied in the problem formulation to take into account the transmit power budget and interference temperature constraints, which is solved efficiently by geometric programming. In the proposed distributed power control scheme, users exchange "price" messages which indicate the negative effect of interference at the receivers. Given this set of prices, each transmitter updates power levels on multiple channels using gradient descent method. It is proved that the proposed algorithm converges to the global optimum when operated in an totally asynchronous manner. The performance of the proposed scheme is investigated by numerical results in the end.

In this paper, a novel maximum Doppler spread estimation algorithm is presented for OFDM systems with the comb-type pilot pattern. The least squared estimated channel frequency responses (CFR's) on pilot tones are used to generate the auto-correlation matrices with/without a known lag, from which the time correlation function can be measured. The maximum Doppler spread is acquired by inverting the time correlation function. Since the noise term will bias the estimator, the estimated CFR's are projected onto the delay subspace of the channel to reduce the bias term as well as the computation complexity. Furthermore, the subspace tracking algorithm is adopted to automatically update the delay subspace. Simulation results demonstrate the proposed algorithm can quickly converge to the true values for a wide range of SNR's and Doppler spreads in Rayleigh fading channels.

In this paper, a parametric channel estimation algorithm applicable to uplinks of orthogonal frequency division multiple access (OFDMA) systems whose subcarriers are pseudo-randomly allocated is proposed. By exploiting pilot hopping, estimation of signal parameters via rotational invariance technique (ESPRIT) is employed to estimate the path delays of the sparse multipath fading channel. From the delay information, a channel interpolator utilizing global pilots, which can be irregular distributed, is derived to estimate the channel state information on the desired tones. Moreover, a simple method of estimating the time correlation of the channel taps is introduced and integrated in the proposed algorithm. Simulation results demonstrate that the proposed algorithm outperforms the local linear channel interpolator within a wide range of SNRpsilas and Dopplerpsilas.

Current deployed emergency communications systems are only available to the rescuing workers. In this paper, a framework of wireless emergency communications is proposed for common communications in the disasters based on relaying and cognitive radio. In this framework, relaying provides small coverage expansion and high capacity for common communications. On the other hand, cognitive radio based frequency lowering provides large coverage expansion and low system capacity for special number communications. The coverage performance is evaluated by the calculation and simulation. To balance the tradeoff between coverage and capacity, both two-hop relaying and cognitive radio are adopted appropriately to satisfy the requirements of emergency communications according to their characteristics. The performance of the proposed framework is also investigated in this paper.

This paper investigates the multiple-input multiple-output (MIMO) antenna subset selection combined with space-time coding (STC) in retransmit system, e.g. hybrid automatic repeated request (HARQ). As we know space-time coding (STC), transmit antenna selection (TAS), as well as some receive diversity combining techniques such as selection combing (SC) and maximal ratio combining (MRC) offer considerable diversity gain. Therefore, they are combined in a system named TAS/STBC/HARQ which has been investigated in this paper. The performance of bit error rate (BER) is analyzed in flat fading channel. The numerical results based on the SC and MRC reveal that the scheme achieves great diversity gain. The diversity gain is greater than the MIMO transmit antenna subset selection with STC, when no HARQ is utilized, except for little coding gain loss and larger time delay. Moreover, our system also has the advantage of throughput with turbo encoder.

In cognitive radio network, power control is necessary to not only decrease the interference among the unlicensed users, but also avoid the negative effect to the licensed users. In this paper, a noncooperative power control model is proposed for the unlicensed users using game theory. In order to restrict the interference to the licensed users, an exponential part indicating the effect to the licensed users is added into the pricing function. Through game theoretic deduction, it is obtained that a unique Nash equilibrium solution exists under appropriating parameter value of the payoff function. Further, the Nash equilibrium solution of the proposed power control game with exponential pricing is Pareto optimality and achieves maximum total throughput under strict constraint of the interference temperature limitation. The appropriate value of the new parameter in the pricing function is discussed. The performance of the proposed power control algorithm is investigated by numeral results.

In cognitive radio network, the interference of the unlicensed users to the licensed users should be limited under interference temperature constraints. In this paper, the optimal power control scheme of a network is analyzed without interference temperature constraints firstly. Based on this, considering interference temperature constraints, the optimal power control in cognitive radio network is modeled as a concave minimization problem. Some useful properties of the power control optimization problem are exploited. According to these properties, an improved branch and bound algorithm which is more efficient than the general branch and bound algorithm is proposed for optimal power control optimization problem in cognitive radio network.