Multidimensional resources management enhances the efficiency of low-Earth orbit (LEO) satellite systems by dynamically allocating wireless resources. However, the majority of existing studies have overlooked the evolutionary trend of terrestrial terminals (TTs) from homogeneous to heterogeneous types. For instance, energy efficiency (EE) requirements for TTs inherently vary due to differences in their operating environments and deployment costs. Thus, this article investigates the design of beam hopping (BH) patterns, frequency allocation, and power control for LEO satellite uplinks, considering the diverse EE sensitivity (EES) among TTs. Specifically, a resource management problem is formulated to maximize the weighted sum EE, which captures the differences in EES among TTs. Next, an undirected graph is constructed based on EES and spatial isolation, and matching algorithms are proposed according to the many-to-one matching theory to obtain BH patterns. After that, to solve the joint frequency allocation and power control problem, a penalty function is applied to handle the binary variable utilized for expressing frequency allocation. Finally, quadratic transform and minorize–maximization are employed to concave the original problem, which guarantees to obtain a suboptimal solution. Simulation results demonstrate that proposed BH design methods provide a higher optimization ceiling for joint resources management than relevant benchmarks. Besides, the proposed uplink resources management significantly improves the EE by 34% in LEO satellite uplink compared with ignoring EES scheme. This work establishes a novel paradigm for energy-efficient uplink resource management in heterogeneous LEO systems.

Beam hopping (BH) is a widely adopted technique in multi-beam satellite communication systems, and it can effectively improve the system capacity. However, conventional BH with full frequency reuse requires spatial isolation to avoid inter-beam interference, and it will impose restrictions on the flexibility of BH and achievable system capacity. By combining the BH and multi-beam precoding, it is beneficial to satisfy the uneven traffic demands and keep the flexibility of beam management. Different from the existing works that focus on the joint BH and precoding for a single satellite, we investigate the joint multi-satellite cell association, BH pattern design and multi-beam precoding problem for dense low earth orbit (LEO) satellite communication systems. To tackle the complex problem, we decompose it into two subproblems. First, a many-to-one matching based multi-satellite cell association algorithm is proposed, which balances the load among satellites and mitigates interference among activated cells. Second, many-to-many matching based BH pattern design and quadratic transform based precoding algorithms are proposed and alternately optimized to solve the joint BH and precoding problem. Lastly, a multi-satellite joint BH and precoding algorithm based on alternating optimization (MJBHPAO) is proposed to solve the entire problem. The effectiveness of the proposed algorithm is verified with various parameters, and simulation results show that the proposed algorithm performs better than the referred schemes.

Multibeam technology, as one of the crucial technologies for implementing high throughput satellite (HTS) systems, can flexibly allocate the beam resources to enhance communication quality and transmission rates. Due to the variability in service demand, traditional resource allocation methods struggle to accommodate the dynamic changes in user service requirements. Thus, heuristic algorithms, such as the genetic algorithm (GA) and deep reinforcement learning (DRL) are commonly used in current research for communication resource allocation. However, existing resource allocation strategies mainly focus on radio resource allocation in a single timescale, neglecting the differentiated time granularity resource scheduling requirements arising from the distinct characteristics of variability in beam-level and user-level resource demands. In this article, we investigate bandwidth allocation strategies in multibeam satellite (MBS) networks. Considering the potential drawbacks of single-agent algorithms in terms of the high complexity for bandwidth allocation in dynamic user demands, a two-timescale hierarchical bandwidth allocation algorithm based on multiagent DRL is proposed to minimize the total system delay and guarantee the delay fairness for ground users. Simulation results demonstrate that the proposed algorithm requires shorter training time to converge and exhibits superior performance in communication delay and system fairness compared with the single-agent DRL algorithm.

In order to satisfy the requirements for service transformation and upgrading toward industrial digitization, networking, and intelligence, sixth generation-enabled industrial Internet of Things (IIoT) imposes new requirements on deterministic delay. However, the existing best-effort communication networks increase the uncertainty of transmission, making it difficult for users to ensure deterministic delay performance. In this article, we propose a deterministic delay guarantee strategy (DDGS) under reconfigurable intelligent surface (RIS)-assisted communication networks to ensure network performance in IIoT scenarios. In particular, we utilize stochastic network calculus (SNCs) to derive the probability that the delay falls within a specific time window, characterizing the probabilistic bounds of deterministic delay. Then, we explore the relationship between delay determinacy and wireless resources by jointly optimizing the transmit power, the channel blocklength allocation, and the phase-shift matrix at the RIS to maximize delay determinacy. Based on the interdependence of action choices among users and past experience, this article proposes a performance guarantee parameterized deep Q-network (PG-PDQN) algorithm to solve the complex problem containing a mixture of discrete and continuous action spaces. Simulation results show that the DDGS strategy significantly improves the delay determinacy compared to other strategies, and the PG-PDQN algorithm has good convergence, thus effectively improving the network performance.

Sparse code multiple access (SCMA) is a new nonorthogonal multiple access scheme, which effectively exploits the constellation shaping gain of multi-dimensional codebook. In this paper, we propose a new SCMA architecture of uplink satellite communication system. At the transmitter, we group the users and design a practical spherical codebook. At the receiver, a low-complexity multi-user detection algorithm, namely logarithm domain message passing algorithm (Log-MPA) is implemented. The results show that the reliability of the proposed codebook outperforms the existing SCMA codebook schemes in both AWGN and Rayleigh channels.

Cell-free (CF) massive MIMO is considered one of the key technologies for 6G to achieve high spectral efficiency (SE) and ultralow latency. However, as the number of users increases, pilot contamination becomes more serious, and the optimal SE can not be achieved when the number of users exceeds the access points (APs). Therefore, we study the CF massive MIMO-NOMA system. Specifically, we design a user clustering algorithm based on the average Signal to Interference plus Noise Ratio (SINR), using orthogonal pilots between different clusters, and different users in the cluster using the same pilot, thereby reducing pilot contamination. Then we propose a flexible power allocation problem to maximize the system SE while taking into account user fairness. We model the problem as a Markov Decision Process (MDP) and then solve it using the asynchronous advantage actor-critic (A3C) algorithm in deep reinforcement learning. Simulation results show that the proposed A3C based power allocation scheme in CF massive MIMO-NOMA outperforms the baseline schemes in terms of fairness and SE.

The large-scale Low Earth Orbit (LEO) satellite communication systems are considered the most promising communication technologies in satellite-terrestrial integrated networks. LEO satellite systems have low latency, high throughput and wide coverage, which means they can provide telecommunication services for rescue and disaster scenarios. Since the LEO satellite communication systems and IMT systems are envisaged to share the same spectrum, the potential harmful interference issues must be analyzed. In this paper, the Walker constellation model is adopted in the satellite constellation design. Considering the large-scale deployment of LEO satellite systems and IMT systems, millions of base stations are scattered all over the place. Furthermore, we aim at evaluating the mutual interference of two systems in the Ka band and utilize terrain data to deploy IMT systems in the Contiguous United States. Specifically, an innovative method called electronic fences is proposed in the paper, which is conducive to alleviating the interference in satellite-terrestrial communication systems.

With the commercialization of International Mobile Telecommunications (IMT) technology, this paper studies the problem of ensuring coexistence between new frequency bands and existing satellite systems, which has led to an increasing demand for spectrum resources. Specifically, the research evaluates the potential interference between IMT and Earth-Exploration Satellite Service (passive)-EESS (passive) in the 10-10.5 GHz frequency band. This paper presents an innovative methodology for constructing a dynamic EESS (passive) system model based on orbital elements. In addition, the IMT system model was built by analyzing satellite imagery data. The results show different levels of interference sensitivity among passive sensors, highlighting the need to impose necessary restrictions on IMT systems to protect EESS (passive) systems. Overall, this paper introduces an innovative method for modeling the dynamic interaction between satellites and large-scale base stations, which is beneficial for analyzing the coexistence of IMT and EESS (passive) systems.

As complementary way of mobile communication, Mobile Ad Hoc Network (MANET) has developed rapidly and been utilized for various scenarios, where the energy consumption and load balancing are considered as key issues. To address this problem, based on Ad hoc On-demand Multipath Distance Vector (AOMDV)routing protocol, we propose a multipath routing protocol based on energy aware and constrained queue length (AOMDV-EC). We define the congestion state into several levels according to the queue length in the MAC layer, and select paths considering residual energy and hop count. The simulation results show that the performance of the proposed routing protocol is significantly improved in terms of packet delivery ratio, average end-to-end delay, throughput, routing overhead and energy exhausted nodes.

Benefiting from the development of satellite on-board processing capability, the orbital computing can be realized by deploying edge computing servers on satellites to reduce the task processing latency. However, edge computing based on geostationary Earth orbit (GEO) or low-Earth orbit (LEO) alone can hardly meet the latency requirements of Satellite-assisted Internet of Things (SIoT) services. Moreover, the uneven distribution of tasks generated by SIoT devices will also cause the load unbalancing among different satellites. In this article, hybrid GEO–LEO SIoT networks is investigated with joint computing and communication resource allocation. To tackle the load unbalancing problem, tasks generated by SIoT devices can be processed by collaborative LEO satellites or forwarded to gateways on ground via GEO satellite. Thus, the joint task offloading, communication and computing resources allocation for the hybrid SIoT network can be formulated as a mixed integer dynamic programming problem with satellites-ground cooperation and intersatellite cooperation via the intersatellite links. Then, an intelligent task offloading and multidimensional resources allocation algorithm (TOMRA) is proposed to minimize the latency of task offloading and processing. First, a method base on deep reinforcement learning is utilized to solve the subproblem of task offloading and channel allocation. And then, convex optimization is adopted to solve the subproblem of computing resource allocation under fixed offloading and channel allocation decisions. Simulation results show that the proposed TOMRA can achieve better performance than the reference schemes.

Nonorthogonal multiple access (NOMA) and symbiotic ambient backscatter communications (AmBCs) are both considered promising technologies for beyond 5G mobile communication technology by enabling low-powered and spectrum-efficient access in large-scale Internet of Things. In this article, we investigate the uplink symbiotic communication in AmBC enabled NOMA system. In contrast with existing works, we assume the carrier transmitter (CT) transmits information while providing energy to the backscatter devices (BDs), which improves the energy efficiency. However, the signal power transmitted by the CT to integrated receiver (IR) through the direct link is much larger than the signal reflected by the BD, which can seriously affect the detection of the reflected signal by BD. Therefore, we jointly design the signal waveforms of CT and BDs, and propose a multiuser blind detection algorithm based on interference cancellation at IR. The simulation results demonstrate that the proposed multiuser detection algorithm achieves improved performance even the active BD number is unknown or without direct link.

Traditional cellular system deploys base station at the center of each cell, which leads to inter-user/cell interference and limited spectral efficiency. In contrast, the cell-free massive MIMO can effectively mitigate these interference by deploying multiple access points distributed in the coverage that jointly serve all the users. In this paper, we investigate a cell-free massive MIMO system assisted by the non-orthogonal multiple-access (NOMA) and propose a sparse signal reconstruction algorithm based on extended approximate message-passing (EAMP). The simulation results show that the proposed algorithm outperforms the traditional baselines in terms of recovery rate, calculation time and system capacity.

Sparse code multiple access (SCMA) is an up-and-coming non-orthogonal multiple access technology. The message passing algorithm (MPA) is utilized for multi-user signal detection in the SCMA system to achieve a performance close to the maximum a posterior algorithm. However, higher iterations will lead to the higher computational complexity of the MPA. In this paper, a dynamic threshold and stability judgment based MPA scheme (DTS-MPA) is proposed to reduce the computational complexity by simplifying the factor graph and reducing the number of iterations. Firstly, an adaptive dynamic threshold is set to minimize the number of iterations in the different channel state information. Secondly, an additional iteration is proposed to determine whether the variable nodes that meet the dynamic threshold reach stable convergence to improve the decoding reliability. Variable nodes that meet the above conditions will be decoded in advance. Numerical results show that the DTS-MPA can significantly reduce the computational complexity of the MPA and achieve a significant trade-off between bit error rate performance and computational complexity.

The ground traffic demand of multi-beam satellites is growing rapidly, but resources such as satellite power and bandwidth are limited. Beam hopping (BH) alleviates the problem of insuffident satellite transmitters, but how to improve the utilization of limited resources to increase satellite throughput is still a challenge in the future. In this paper, starting from beam hopping, the bandwidth allocation in beam hopping systems is analyzed first. The results show that the effect of full frequency reuse works better than that of partial frequency reuse in the beam hopping system with fewer beams, but the former can reduce the complexity of problems. Afterwards, a deep reinforcement learning strategy combining power allocation and beam hopping (PABH) is proposed, which improves power utilization and satellite throughput to a certain extent, which proves that power allocation is feasible.

Unmanned aerial vehicle (UAV) assisted emergency communication is an important technique for future B5G/6G scenario. The UAV is usually considered as a mobile relay to forward information from the macro base station (MBS) to the users in emergency area. In this paper, the MBS power allocation, the UAV service zone selection, and the user scheduling are jointly investigated to enhance the sum spectrum efficiency. We formulate the MBS power allocation and UAV service zone selection problem as an Markov Decision Process (MDP) in the delay ignored system (DIS) and propose a deep reinforcement learning (DRL) algorithm based on Q-learning and Convolutional Neural Networks (CNN). Then the proposed DRL-based scheme is extended in time delay system (TDS) to estimate the current optimal action with the outdated channel information. We also formulate the user scheduling as a 0-1 optimization problem and solve it by dividing into sub-problems. Simulation results demonstrate that the proposed DRL-based resource scheduling scheme can effectively improve the spectrum efficiency compared with the existing schemes.

Power amplifier (PA) models, such as the neural network (NN) models and the multilayer NN models, have problems with high complexity. In this article, we first propose a novel behavior model for wideband PAs, using a real-valued time-delay convolutional NN (RVTDCNN). The input data of the model is sorted and arranged as a graph composed of the in-phase and quadrature ( I/Q ) components and envelope-dependent terms of current and past signals. Then, we created a predesigned filter using the convolutional layer to extract the basis functions required for the PA forward or reverse modeling. Finally, the generated rich basis functions are input into a simple, fully connected layer to build the model. Due to the weight sharing characteristics of the convolutional model's structure, the strong memory effect does not lead to a significant increase in the complexity of the model. Meanwhile, the extraction effect of the predesigned filter also reduces the training complexity of the model. The experimental results show that the performance of the RVTDCNN model is almost the same as the NN models and the multilayer NN models. Meanwhile, compared with the abovementioned models, the coefficient number and computational complexity of the RVTDCNN model are significantly reduced. This advantage is noticeable when the memory effects of the PA are increased by using wider signal bandwidths.

With the rapid development of 5G mobile communication and intelligent vehicles, efficient and reliable vehicle-to-vehicle communication is recognized as one of the key technologies for the next generation of wireless networks in the future. Due to the rapid changes in the wireless channel due to the high mobility of vehicles, the traditional resource allocation technology is no longer suitable for the current vehicle network. This paper proposes a joint resource allocation method for the internet of vehicles communication based on the proximal policy optimization algorithm. In this paper, a joint optimization scheme of discrete and continuous resources suitable for the internet of vehicles communication is designed to effectively meet the strict requirements of vehicle-mounted communication. Simulation results show that the proposed allocation method can maximize the system capacity and satisfy the delay constraints of vehicle-to-vehicle communication.

Mobile crowdsensing (MCS) is a promising diagram for data collecting based on smart mobile terminal. Nowadays, vehicles with embedded multiple sensors have been increasingly adopted as participants to complete various sensing tasks. Most existing researches are conducted in terms of MCS coverage, energy consumption or incentive mechanisms etc. In this paper, a new utility function F(Ω) for measuring the quality of MCS coverage is proposed, F(Ω) includes coverage percentage and coverage degree. We formulate the selection of taxis as an optimization of coverage quality. Therefore, an improved greedy algorithm to optimize the coverage quality (CQO) is proposed. We evaluate the proposed algorithm with trajectory dataset and study several factors influencing coverage quality. The results show that the proposed algorithm achieves a better coverage quality.

In this paper, a low-feedback-sampling-rate digital predistortion (DPD) method is proposed for wideband wireless transmitters and radio-frequency power amplifiers (PAs). This DPD method inserts a non-ideal real band-pass filter into the feedback loop to limit the feedback bandwidth. Meanwhile, to recover the band-limited feedback signal, it introduces a signal-recovery module that is based on the deep neural network (DNN). A data-preprocessing technique is proposed to reduce the amount of input data for the DNN and thereby significantly reducing its structural complexity. Also, the use of DNN makes it feasible to implement off-line training. Both the simplicity of the proposed DNN and the availability of off-line training reduce the system complexity in DPD. Experimental validation was performed on a PA driven by wideband orthogonal-frequency-division-multiplexing (OFDM) signals. The results demonstrated the superiority of the proposed method in under-sampling applications. The proposed DPD method achieved the same linearization performance as the state-of-art DPD methods while requiring a sampling rate that was approximately 5% less. Meanwhile, the proposed DPD method has been validated to have stronger anti-noise and generalization capabilities.

Interference alignment (IA) is thought to be an effective technique on reducing interference in wireless communications. To mitigate the interference and improve the system performance, this paper proposes a two-stage hybrid interference alignment (THIA) scheme for the two-tiered multi-user multiple-input-multiple-output (MIMO) heterogeneous network (HetNet). Specifically, we focus on the downlink transmission of two-tiered HetNet. Firstly, we align the cross-tier interference from macro base station (MBS) to the users in the same small cell to a specific subspace, through which the first-stage transmit beamforming matrix at MBS is designed. Then, an equivalent virtual distributed method is applied to mitigate the co-tier interference among macro users. Numerical results demonstrate that the proposed THDIA scheme outperforms the conventional schemes in degrees of freedom and system sum rate.

Beam hopping (BH) is the key technology to improve the system throughput and decrease the transmission delay in multibeam satellite systems. The objective of this study is to find a policy to maximise the expected long‐term resource utilisation. The BH illumination plan (BHIP) optimisation problem aimed at minimising the transmission delay is formulated and modelled as a partially observable Markov decision process. To tackle the issue of unknown dynamics and prohibitive computation, an artificial intelligence method named deep reinforcement learning (DRL) is first proposed to solve the BHIP problem in multibeam satellite systems. The proposed DRL‐BHIP algorithm considers a series of realistic conditions, including the traffic demands in spatial distribution and temporal variation, ModCod constraints, antenna radiation pattern and inter‐beam interference. The state reformulation concept is adopted to characterise the traffic spatial and temporal features. Simulation results show that the proposed DRL‐BHIP algorithm can decrease the transmission delay and improve the system throughput compared with existing algorithms.

Sparse Code Multiple Access (SCMA) is a new air interface technology that is proposed for lower delay, higher capacity and spectrum utilization in 5G communications. The inter-symbol interference(ISI) in wireless communications is serious due to the effects of multipath propagation and background noise. In order to reduce inter-symbol interference, many wireless air interface and radio access technologies adopt cyclic prefixes to obtain channel estimation. However, the spectrum utilization of this method is too low. Considering the blind equalization algorithm is often used in the environment where spectrum resources are scarce, in this paper we adop the blind equalization algorithm in the SCMA technology. In SCMA, the equalization cannot placed on receiver, so we innovatively put the equalization algorithm in the signal transmitter according to the actual situation. In addition, we propose a channel estimation algorithm. Theory and simulation show that applying blind equalization to SCMA can reduce bit error rate and improve spectrum utilization.

Millimeter wave (mmWave) communications are an important candidate technique in 5G networks for features, supporting ultra-dense small cells and mobile data offloading. However, ultra-dense nodes and increasing data traffic bring in vast interference. This paper investigates low complexity non-iterative interference alignment (IA) schemes for multiple-input multiple-output (MIMO) interference broadcast channels in mmWave communications. The authors focus on the three-cell mobile network model in which each base station supports no more than two users within its cell. There is already a closed-form IA solution for the case that one cell has two users, while the other two have one user in each, which can be denoted as {2,1,1}. This paper considers different settings and proposes corresponding IA schemes. First, two IA schemes based on multi-step for the asymmetric setting {2,2,1} are presented, five degree of freedom (DoF) could be achieved. Then, for the symmetric setting {2,2,2}, a novel IA solution with lower complexity and a joint method combining IA with non-iterative multi-user MIMO technique are proposed, and they can achieve six DoF. The simulation results indicate that our non-iterative schemes have similar sum-rate capacity performances with the iterative ones in existing work, and the complexity is effectively reduced.

In this paper we present the requirements, design and pre-deployment testing of a transportation bus as a Mobile Enterprise Sensor Bus (M-ESB) service in China that supports two main requirements: to monitor the urban physical environment, and to monitor road conditions. Although, several such projects have been proposed previously, integrating both environment and road condition monitoring and using a data exchange interface to feed a data cloud computing system, is a novel approach. We present the architecture for M-ESB and in addition propose a new management model for the bus company to act as a Virtual Mobile Service Operator. Pre-deployment testing was undertaken to validate our system.

Abstract In Long Term Evolution Advanced networks with Type I in‐band half‐duplex decode‐and‐forward relay nodes, proportional fair (PF) resource allocation is aiming at guaranteeing two‐hop match and optimising global proportional fairness. The two‐hop match is defined as equal data rates in the access links and the corresponding backhaul links. The global proportional fairness is between all the user equipments served by the evolved nodes B and the relay nodes. Existing centralised schemes achieve these targets at the cost of enormous channel state information (CSI) exchange. Existing distributed schemes focus on resource partitioning and employ a traditional single‐hop PF scheduling algorithm in access links, with less CSI exchange. The traditional PF scheduling algorithm maximises single‐hop proportional fairness between the data rates in the access links rather than two‐hop proportional fairness between the end‐to‐end data rates in the two hops. In order to reduce CSI exchange and at the same time to maximise the two‐hop proportional fairness, a distributed two‐hop PF resource allocation scheme is proposed. The proposed scheme includes two‐hop PF resource scheduling algorithms and adaptive resource partitioning algorithms, applied in different two‐hop transmission protocols. Simulation results demonstrate the proposed scheme is better than the existing distributed schemes in obtaining better proportional fairness and larger cell‐edge user equipment throughputs. Copyright © 2014 John Wiley & Sons, Ltd.

Automatic modulation identification (AMI) is a significant technique to identify the modulation type of a received signal, and it is of great importance in information war and communication countermeasure. In this paper, we propose a constellation-wavelet transform automatic identifier (C-WT AMI) to identify the order of the received QAM signal. QAM is a widely used modulation type due to its high spectrum efficiency. From the characteristics of QAM constellation, we perform Haar wavelet transform of the received signal with the most appropriate scale, and then calculate the ratio of the maximum and minimum value q̌M of the Haar wavelet transform magnitude of received signal, and we can finally determine the signal order M according to the relationship between q̌M and M. To compute the most appropriate wavelet scale, estimating carrier frequency and sampling frequency is necessary. In this paper, we propose a method to estimating sampling frequency in practical engineering implement. Considering that distinct peaks may occur in the Haar wavelet transform magnitude due to symbol phase changes, and it could impact the identification correct rate, so an effective algorithm to eliminate the influence of the peaks is also proposed. Simulation results show that the proposed scheme is of good performance, and it can almost be inerrable when SNR>4dB. Besides, compared to the methods proposed in other papers, our scheme shows a better performance with higher correct rate.

In satellite communication system, the large Doppler frequency shift and the accumulated oscillator uncertain frequency offset will influence the accuracy of timing advanced estimation with Zadoff-Chu sequence. In order to mitigate the adverse impacts of frequency offset for timing advanced estimation, a novel estimation algorithm is put forward in this paper. This novel proposed algorithm utilizes the symmetric properties of Zadoff-Chu sequences and applies Discrete Fourier Transform (DFT) spectrum analysis at the receiver to find out the indices of peaks which are related to the frequency offset. By executing operation on the obtained peak indices, it can not only eliminate the frequency offset, but also determine the value of transmission delay. Simulation results show that this proposed timing advanced estimation scheme can achieve better performance than the conventional methods with low complexity.

In full-duplex systems, radio circuit's impairments is a significant factor that limits the cancellation of self-interference power. In this paper, we derive a closed-form expression of digital domain self- interference for a full-duplex orthogonal frequency division multiplexing (OFDM) system considering the RF impairments at the transmitter and the receiver. A digital-domain self-interference cancellation scheme (DSICS) that accounts for the power amplifier nonlinearity, IQ imbalance and phase noise effects at the transmitter and receiver is proposed. The DSICS can suppress the self-interference power effectively by compensating for the RF impairments associated with the self-interference signal.

The base station (BS) density influences the coverage performance of cellular communication system. In massive multiple input multiple output (MIMO) system, each BS is equipped with large scale antenna array. The stochastic geometric model is employed because of its analytical tractability. This paper demonstrates that BS density has to be below a certain bound in order to satisfy the coverage requirement in massive MIMO system. Closed-form expression for the BS density bounded by maximum outage probability is derived using Lambert W function. Moreover, the expression for the upper bound of successful area spectral efficiency (ASE) is also derived. Our study claims that increasing BS density will lead to the deterioration of coverage performance in massive MIMO system.

In this paper, an advanced anti-collision algorithm in RFID-sensor network based on inter-tag communication mechanism is proposed to improve the performance of the system. By employing the "Tag Communication Slot" (TCS) and designing the relevant hardware, the tags can communicate with each other, transmit useful data to fewer tags and reduce the number of tags participating in the identification process. In this way, we can efficiently reduce the collision rate with the increase of tag number and improve the tag starvation problem in Dynamic Framed-Slotted ALOHA algorithm (DFSA). The simulation results show that the TCS algorithm can greatly improve the system stability and identification efficiency especially in the case of large tag number.

Barrier coverage is a critical issue in Wireless Sensor Networks (WSNs) for various battlefield and homeland security applications. In this paper, we address the problem of how to efficiently heal coverage holes in hybrid Wireless Sensor Networks (WSNs) by relocating mobile nodes, and propose a hole recovery strategy called Minimal Patching Barrier Healing Strategy (MPBHS). In our strategy, a definition of Minimal Patching Barrier (MPB) is proposed, which can be healed by least number of nodes. Under this definition, our strategy can be divided into two steps: first is finding out the MPB among all possible barriers; second is dispatching the selected mobile nodes, which means moving mobile nodes to appropriate points in MPB to heal coverage holes, according to our Coverage Holes-Mobile Nodes Matching (CHNM). The experimental evaluation shows the efficiency of our proposed strategy with the respect of both sensor nodes' efficiency and low energy efficiency.

A new coarse synchronization scheme for 3rd Generation Partnership Project Long Term Evolution (3GPP-LTE) is proposed. Existing coarse synchronization schemes based on central symmetry utilize either primary synchronization signal (PSS) and secondary synchronization signal (SSS) to fulfill initial timing synchronization. Though these two schemes perform well with relative low complexity, they can be further enhanced. Therefore a new scheme is proposed in this paper, which combines PSS with SSS and utilizes hierarchical computing to implement initial timing synchronization. Simulation results show that the new scheme can acquire higher detection accuracy rate without increasing computational complexity.

This paper investigates joint relay selection (RS) and power allocation (PA) in cognitive radio (CR) systems, in which relay nodes operate in amplify-and-forward (AF) mode. In contrast to the conventional schemes, we take into account the performance of the secondary user (SU) which acts as the relay. A two-step optimization scheme is proposed to maximize system throughput with minimum rate guarantees for the source and the relay while the interference introduced to the primary user (PU) is kept below a specified threshold. The PA optimization in the first step is non-convex, and hence hard to solve in general. To tackle this, we introduce a power proportionality factor at the relay to decompose the non-convex optimization into two levels. Based on dual method and search method at inner level and outer level respectively, a closed-form solution for the optimal PA between the source and the relay is derived. In order to reduce complexity of repeating the PA procedures at all the relay candidates while maintaining reasonable performance, we also develop a suboptimal approach. Furthermore, simulation results and comparisons are presented to illustrate the performance of the proposed scheme.

Carrier aggregation (CA) is an effective approach to achieve high transmission rate and wideband communication, which are the significant features of Long Term Evolution-Advanced (LTE-A). Component carrier (CC) selection plays an important role in CA technology. An appropriate CC selection method is necessary to improve the system performance in terms of load balancing and channel quality. A Hierarchy Based Component Carriers Selection (HCS) is proposed for homogeneous scenarios in LTE-A. In this scheme, microeconomic model is used to identify the status of CCs in order to make sure that all the CCs aggregated are in good quality. Al the CCs and eNBs are graded so as to carry out the aggregation in corresponding hierarchy. Macro cell downlink system level simulation is utilized to evaluate the performance of the proposed HCS scheme. Simulation results demonstrate that the HCS scheme outperforms the traditional ones from the aspects of both load balancing and channel quality.

Cooperative sensing can improve the performance of spectrum sensing in cognitive radio. This paper focuses on the design of hard information fusion strategy in cooperative sensing when detecting nodes work under varying degrees of reliability. A Cooperative Spectrum Sensing Scheme based on Detecting Reliability Statistics (CSS-DRS scheme) is proposed in this paper, in which fusion center estimates the reliability of each detecting nodes by means of probability statistics. The reliability estimation is based on the historical sensing results and does not need extra data transmission in report channel. Simulation results show that CSS-DRS scheme has significant advantage over traditional K-out-of-N strategy when detecting nodes work under varying degrees of reliability.

In this paper, we consider the problem of hierarchical bandwidth sharing in cognitive radio (CR) environment. In the system model under consideration, a primary service provider (PSP) can sell its available spectrum bandwidth to a secondary service provider (SSP). In contrast to the conventional schemes, the SUs served by the SSP can also share the unsold bands of the PSP through the approach of opportunistic spectrum access (OSA), which will introduce interference to the PUs and make the PSP suffer a profit loss due to the sensing imperfections in practical networks. Based on microeconomic theory, this problem of bandwidth allocation is formulated as a market model, and an equilibrium where both the PSP and SSP are satisfied with the amount of allocated bandwidth and the price is derived by using the concept of demand and supply functions. Moreover, an iterative algorithm for distributed implementation of our scheme is also proposed. Furthermore, simulation results and comparisons are presented to illustrate the performance of the proposed scheme.

As conventional single-layered network cannot satisfy the increasing demand in high-speed indoor data service, femtocell has been introduced to provide higher network capacity and better user experience. However, the downlink transmit signal from femtocell base station (FBS) may result in severe cross-tier interference to macrocell users (MUE). To address this problem, we propose a dynamic femtocell exclusion mechanism to alleviate the downlink cross-tier interference for MUE. The radius of the femtocell exclusion area, which is defined as reuse distance, is analyzed while taking account of the target outage probability and random position of the interfering FBSs. We derive the closed-form expression of the outage probability as a function of reuse distance. This expression provides a simple method to plan the exclusion area around the victim MUE. In addition, we introduce reuse table to avoid unpredictable interference caused by random appeared active FBS. Simulation results verify our theoretical analysis and show that the proposed mechanism can effectively alleviate the downlink cross-tier interference and improve the user capacity of the network.

In component carrier (CC) selection of carrier aggregation, it is critically significant to take the effect of user mobility on system performance into consideration. In the previous CC selection schemes, in order to guarantee channel quality and avoid frequent CC handover caused by user mobility, the eNB always select the CC with the highest Reference Signal Receiving Power (RSRP) for users. However, this method would face carrier overload when the CC with high RSRP was selected for a majority of users. On the contrary, in conventional schemes considering carrier load balancing, the system would face the problem of frequent CC reselection/handover as user mobility was usually neglected, which would face unaffordable signaling overhead and time delay. In this paper, a user-mobility based CC selection (UM-CS) scheme is proposed to analyze the influence of user mobility on CC selection and achieve a compromise of carrier load balancing and handover number. In the proposed UM-CS scheme, in order to avoid frequent CC handover, the eNB-UE separation after a given time is estimated according to the user's mobility and CCs that can satisfy the coverage margin are selected into the user's candidate CC set. In the set, the CC with larger coverage takes higher priority in CC selection. Carrier load are balanced as the user can only select CCs from its own candidate CC set and a CC can be selected only when its carrier load balancing factor is not higher than the threshold. Simulation results validate the effectiveness of the proposed scheme in terms of carrier load balancing and handover number under different user mobility.

Channel reservation is a valid technique to reduce dropping probability when spectrum handover happens in cognitive radio. Most of the existing channel reservation schemes focus on proper number of the channels reserved and the number is fixed for a certain system. These static channel reservation schemes bring high blocking probability. In this paper, reusing reserved channels is considered in dynamic channel reservation (DCR) scheme. Secondary users are divided into two classes according to different time-delay-sensitivity in order to use reserved channels dynamically. Renewal theory is used to obtain the forecast results of licensed channels to determine the number of channels reserved. The DCR scheme is analyzed with a Markov chain. Numerical results show that the DCR scheme performs lower blocking probability and higher throughput with dropping probability basically keeping unchanged compared with the schemes based on static channel reservation.

In studies of cooperation between primary users (PUs) and cognitive users (CUs), compared with non-transparent protocol, the applicability of transparent protocol to existing communication system is higher, while almost no changes are needed to the transmission protocol of primary system. But there are also some disadvantages in existing transparent protocol: 1) the throughput of cognitive relay (CRL) is significantly lower compared with non-transparent protocol; 2) retransmission on spectrum holes, proposed in traditional transparent protocol, may bring signification delay. In this work, based on retransmission of primary packets on retransmission slots configured by primary system, a near-transparent cooperation protocol between PUs and CU is proposed to mitigate the retransmission delay. According to this protocol, an optimal choosing scheme of primary links for CRL is proposed. Based on prediction of CRL's available throughput in each primary link, we analyze and simulate the performance of this optimal choosing scheme in scenario with single CRL and multi primary links. Simulation and analysis show that the near-transparent cooperation scheme can make the retransmission delay relatively low and constant, and significantly improve the throughput of CRL.

As a promising technology to effectively mitigate interference and improve the performance of a wireless communication network, interference alignment (IA) has been attracted extensive concern. In order to solve the problem that perfect IA needs a large number of antennas and facing the cell selection, this paper proposes a partial interference alignment scheme based on user grouping for a network with multi-cell multi-user multiple- input and multiple-output (MIMO) under downlink transmission scenario. In the proposed scheme, the interference at different directions does not need to be aligned to the same subspace. Furthermore, each BS eliminates inter-user interference within its cell and the dominant interference to users in its neighboring cells. Analysis and simulation results show that the proposed approach outperforms the extension of grouping method in terms of antenna number and sum capacity.

Femtocell is on the way of being extensively deployed in households to provide better indoor coverage and higher user capacity. However, as the coverage of femtocell is overlapped with that of conventional macrocell, the cross-tier interference may result in a considerable degradation of system performance in terms of spectral efficiency. This paper focuses on the improvement of cell spectral efficiency in two-tier network. We first derive the explicit expressions of cell spectral efficiency and average spectral efficiency of single user in each tier. Some remarks and observations are provided after analyzing numerical results of the expressions. Then based on the theoretical analysis, we propose a dynamic cross-tier interference coordination (D-CTIC) scheme. D-CTIC can provide high cell spectral efficiency by maximizing the number of coexistent femtocell base stations (FBSs) with macrocell base station (MBS) while still satisfying the QoS requirement of macrocell user (MUE) by restricting cross-tier interference under an acceptable level. Finally, simulation results verify the theoretical analysis and show that D-CTIC can enhance cell spectral efficiency significantly.

In this paper, we consider the problem of joint relay selection and power allocation in a cognitive radio (CR) network, where a secondary user (SU) source communicates with a secondary access point (AP) assisted by a set of SU relays. Each SU in this set not only acts as a potential relay but also has its own data transmission requirement. Keeping the interference to the primary user (PU) below a specified limit, we propose an optimal scheme with quality of service (QoS) support. On the premise of meeting the minimum transmission rate requirements of all SUs, the goal of the proposed scheme is to maximize the weighted sum rate of the network under transmission power and energy constraints. A closed-form solution for the optimal power allocation between the source and the relay can be obtained by the Karush-Kuhn-Tucker (KKT) conditions. Furthermore, simulation results and comparisons are presented to illustrate the performance of the proposed scheme.

Joint User and Antenna Selection (JUAS) with global optimal solution for multi-cell Multi-User Multiple Input Multiple Output (MU-MIMO) systems needs the centralized processing and full Channel State Information (CSI) sharing that both require high capacity backhaul links. In this paper, decentralized JUAS algorithm is considered for multi-cell MU-MIMO systems with Block Diagonalization (BD) precoding. First, by introducing the eigenvalue-based lower bound on the capacity, the global optimal problem of JUAS is reformulated to a new problem that can achieve local optimum. Second, a decentralized fairness-based algorithm is proposed to select the antenna subset with no CSI sharing. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms, such as capacity-based antenna selection and interference-aware user selection, with the same computation complexity. Moreover, the performance losses compared to centralized JUAS with full CSI sharing are marginal.

Each UE can aggregate multiple component carriers (CCs) as a wider band to support the demand of higher data rates in LTE-Advanced (LTE-A) systems with carrier aggregation (CA). An appropriate CC selection is necessary in order to improve the utility of CA, since not all CCs benefit from CA due to bad channel quality of some CCs. Most of existing methods only take one aspect of problems into account like channel quality or traffic load separately. To improve the system capacity under a good load balance condition, a Utility-Balance based CC Selection (UB-CS) strategy is proposed for the non-continuous diverse coverage deployment scenario in LTE-A CA system. Simulation results demonstrate that the given scheme is prior to the traditional ones.

Uplink Inter-Cell Interference (ICI) and interference fluctuation degrades the uplink throughput of LTE/LTE-Advanced systems seriously. To deal with these problems, a coordinated scheduling method with resource sorting is proposed. In the proposed method, the uplink interference fluctuation is resisted by exploiting resource sorting and limiting the ICI generated by a given user to a predefined range. Meanwhile, the resources are allocated by differentiating the users' ability to bear the interference. Simulation results show that the proposed method outperforms traditional ICIC schemes, such as Fractional Frequency Reuse, fractional Transmit Power Control (TPC), and overload indicator based TPC.

Femtocell technology features prominently in future communication system owing to its characteristic advantages. However, the coexistence of femtocells and traditional cellular system causes severe contradictions between spectrum utilization and interference. This paper studies the cross tier resource management and intra tier resource management in hybrid macro/femto network, and a novel Cross-Cognition scheme for cross tier resource management is proposed in order to achieve better spectrum utilization and lower interference. Moreover, a resource allocation scheme based on graph Coloring is also proposed for intra tier resource management to decrease the intra tier interference. Simulations show that the proposed schemes significantly increase the throughput of femtocell system, and outperform traditional cognitive resource allocation schemes.

Carrier Aggregation (CA) adds a new scheduling domain which makes the radio resource management more flexible for future LTE-Advanced system, and also provides a new path to solve the intra-tier interference among femtocells. This paper proposes a Cooperative Power Setting Scheme to mitigate the intra-tier interference among CA-based femtocells. In the proposed scheme the impact of transmission power is considered, and femtocells cooperatively adjust their transmission power to optimize the total throughput on each Component Carrier. Simulation proves that the proposed scheme is effective and compensates for the deficiency of existing researches. The advantage of the proposed scheme is obvious especially when the deployment of femtocells is dense.

This paper investigates downlink frequency resource coordination for LTE network that comprises of macrocell and femtocell. Due to the requirement of high data rate for indoor coverage, femtocells have been considered as a potential solution recently. However, owing to their random and uncoordinated deployment, cross-tier interference exists and affects the system throughput seriously. A hybrid resource allocation (HRA) technique based on measurement reports both from macro users (MUEs) and femto users (FUEs) is proposed. Simulation results show that proposed scheme can enhance overall throughput and mitigate major interferences in different scenarios.

Relay aided wireless transmission realizes extended coverage and increases capacity for wireless communication networks, especially for the TDD/FDD LTE-A system. The relay node processes the received signal from source under certain rules and forwards the signal to destination. Amplify-and-Forward (AF) and Decode-and-Forward (DF) are two of the conventional relay schemes commonly used and cited. Estimate-and-Forward is a well-know but much less studied approach. The basic idea is to forward an estimate version of original signal without hard decision. A soft symbol estimating approach by solving linear equation set structured with received symbol probabilities. This algorithm makes a least squares fit of the estimated symbol probabilities to a single shot noisy symbol a posteri probability (APP) model. In the simulation, we take frame error rate (FER) to demonstrate the proposed scheme outperforms existing relay schemes.

In interference limited CDMA systems, call admission control plays a very important role because it directly controls the number of users. We introduce a interference-based CAC algorithm according to the characteristics of HCS. This algorithm adopts a global strategy in macro layer, while using a local strategy in the micro layer. Meanwhile, in order to make full use of resources in the macro layer, different threshold values in the two layers are used. Simulation results show that the performance has been greatly improved.