In a Flying Ad-hoc Network (FANET), the resilience of a network is defined as the ability to rebound from a threat event. The bi-connectivity of topology which can keep the network connected after a single unmanned aerial vehicle (UAV) fails, is an important feature of resilience. Due to the high dynamic and the limited node energy of FANET, it is difficult to generate and maintain the bi-connectivity. In this paper, in order to enhance network resilience, we proposed a distributed energy-efficient bi-connectivity generation and restoration mechanism based on double reference nodes (DEBGR-DRN) through adjusting UAV's transmission power. Our algorithm can save the transmission power and communication cost by selecting the shortest edge to be added and limiting the path length. NS3 network simulations demonstrate the validity of the proposed algorithm.

The ultra-dense low earth orbit satellite network (UD-LSN) is composed of numerous satellites with different orbital parameters, providing dense overlapping coverage to smartphone-type user equipment (UE) via direct-to-cell technology. The selection of the handover (HO) target and UL transmission power control after HO can significantly impact the energy consumption and quality of service (QoS) of UE. To this end, this paper proposes a user-centric energy-efficient HO (UCEEH) scheme based on multi-agent dueling double deep recurrent Q-network (MAD3RQN), which jointly optimizes the HO target selection and the transmission power of UE, while considering both uplink and downlink objectives and constraints to minimize UE energy consumption and ensure the QoS. A user-centric HO control framework based on UE's independent observation is designed without increasing additional energy consumption for training the UCEEH. To better balance the exploration and exploitation of historical decision experiences, a utility-referenced heuristic experience accumulation (UHEA) algorithm is proposed. The simulation results indicate that the UCEEH algorithm with UHEA can significantly reduce the energy consumption of UE while ensuring the highest QoS.

In existing deep learning-based semantic communication systems, centralized training of semantic models brings a risk of privacy leakage, whereas distributed training imposes a huge computational burden on user equipments (UEs). To address these challenges, we propose a hybrid federated learning (Hybrid-FL) framework to alleviate the computational burden on UEs while protecting the user privacy. Specifically, each UE uploads local gradients and semantic symbols to the base station for the collaborative training of global and local semantic models. Furthermore, we propose a joint communication and computation scheme for supporting the model aggregation and semantics transmission. To gain deep insights, we expose the joint impact of communication and computation on the convergence behavior of Hybrid-FL by deriving an upper bound. Then, we formulate a mixed-integer nonlinear programming problem to improve the convergence performance of Hybrid-FL, which is then effectively solved by using our proposed algorithm that developed based on alternating and matching theory. Experimental results demonstrate that Hybrid-FL outperforms conventional FL by achieving a 20% accuracy gain and a 80% latency reduction.

In this letter, we study the beamforming design for channel estimation of multi-functional reconfigurable intelligent surface (MF-RIS)-aided multi-user communications that supports simultaneous signal reflection, refraction, and amplification. A least square (LS) based channel estimator is proposed for MF-RIS by considering both the coupled MF-RIS beams and the introduced thermal noise. With the discrete fourier transform (DFT)-matrix, the MF-RIS beamforming design problem is simplified under the proposed LS channel estimator. The optimal MF-RIS beamforming design that achieves the Cramér–Rao lower bound (CRLB) of channel estimator is obtained with the proposed alternating optimization algorithm. Simulation results demonstrate the effectiveness of the proposed beamforming design in reducing the impact of thermal noise.

The communication security and covertness of legitimate vehicles in the sixth-generation (6G) mobile communication intelligent automatic transportation systems (IATS) face significant challenges, as the communication equipment and wireless signals are complex and susceptible to information leakage. To address the above issues, this paper proposes a novel IATS that integrates simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) with rate-splitting multiple access (RSMA). Concurrently, an eavesdropping vehicle, capable of monitoring and eavesdropping information from legitimate vehicles, is introduced to explore the joint security and covertness performance. A roadside device is also introduced to improve security and covertness performance by transmitting artificial noise. The closed-form expressions for outage probability (OP), detection error probability (DEP), and intercept probability (IP) are derived to characterize the reliability, covertness, and security of the proposed system. The impact of various system parameters on system performance has also been extensively conducted, including transmitted signal-to-noise ratio (SNR), interference power, the power allocation coefficients, and the number of elements in STAR-RIS. The simulation results reveal several key insights: 1) The OPs and IPs of the RSMA-IATS network gradually decrease and increase with the transmitted SNR, respectively; 2) Achieving an optimal balance in power allocation between monitoring and eavesdropping proves essential for effective eavesdropper management; 3) The energy efficiency (EE) of the proposed system exhibits dual peaks at higher STAR-RIS element numbers, contrasting with a single peak at lower element counts, underscoring the strategic deployment of STAR-RIS technology in RSMA-IATS network.

The rapid evolution of edge computing and artificial intelligence (AI) paves the way for pervasive intelligence in the next-generation network. As a hybrid training paradigm, federated split learning (FSL) leverages data and model parallelism to enhance training efficiency. However, existing FSL encounters unacceptable waiting latency due to device heterogeneity and synchronous model aggregation. To address this issue, we propose a semi-asynchronous FSL (SAFSL) framework that enables personalized model splitting and aperiodic model aggregation. We derive the convergence upper bound by considering factors such as the number of devices, training iterations, and data heterogeneity. To minimize the long-term average training latency while maintaining high energy efficiency in resource-constrained wireless networks, we formulate a stochastic mixed-integer nonlinear programming problem. By decomposing it into multiple sub-problems in each round, we propose a Lyapunov-based alternating optimization algorithm to solve it in an online manner. Numerical results demonstrate that our SAFSL achieves faster convergence with reduced communication overhead while maintaining high prediction performance under non-independent and identically distributed data, outperforming state-of-the-art benchmarks. Moreover, our algorithm achieves a low training latency, highlighting its superior performance and effectiveness.

The Low-Earth-Orbit (LEO) constellation has emerged as a promising component for seamless and fast global connectivity. With the increasing number of satellites in LEO constellations, multiple satellites can simultaneously cover the same geographical area. The selection of a user's handover (HO) target can significantly impact the quality of service (QoS) for communication and the load status of the satellite network. Motivated by this, the paper introduces a multi-slot load balancing (MSLB) scheme to offer users HO target selection strategies. An optimization problem is formulated to determine the HO sequence for users according to the MSLB scheme. Subsequently, a HO sequence graph mapping (HSGM) algorithm and an iterative shortest path solving (ISPS) algorithm are developed to address this optimization problem by transforming it into a shortest path searching problem. Simulation results demonstrate that, in comparison to traditional single-slot schemes, the MSLB scheme exhibits superior performance in load balancing and improving user communication QoS.

In existing federated learning (FL), the base station (BS) coordinates devices to collaboratively train a shared model by avoiding the transmission of raw data. To achieve communication-efficient model uploading, over-the-air computation (AirComp) is often employed to aggregate model parameters. However, in conventional AirComp assisted FL, the BS’s abundant computation resources are underutilized due to its non-involvement in model training. Meanwhile, transmission failures resulting from fluctuating wireless channels impair the quality of model aggregation. In this paper, we propose a retransmission-based semi-federated learning (SemiFL) framework, wherein devices upload model parameters and public privacy-free data for enabling a hybrid implementation of FL and centralized learning (CL). In our new framework, the BS leverages its abundant computation resources to aid CL model training, which mitigates the resource wastage while alleviating local computational burden of devices. Moreover, the proposed new retransmission mechanism effectively overcomes detrimental transmission failures resulting from the fluctuating quasi-static channel, aiming to guarantee improved learning performance of SemiFL. Successful transmission probabilities of both retransmission-based AirComp and retransmission-based digital communication are provided in closed forms. To attain deep insights, we derive an optimality gap to capture the convergence behavior of retransmission-based SemiFL. Then, we formulate a non-convex long-term problem to minimize a weighted sum of overall latency and energy consumption by jointly optimizing communication, computation, and learning parameters. Extensive experimental results show that our retransmission-based SemiFL obtains 21.9%, 30.5%, and 44.1% accuracy gains on three datasets, while efficaciously reducing latency and energy consumption compared to benchmarks. Meanwhile, our scheme enhances learning performance on the fluctuating quasi-static channel compared to state-of-the-art schemes.

Internet-of-Vehicle (IoV) enabled Maritime Transportation Systems (MTS) communication is anticipated to support ultra-reliable and low latency, diverse quality-of-service (QoS) and large-scale connectivities. To meet such stringent demands, a cognitive ambient backscatter non-orthogonal multiple access (C-AmBC-NOMA) IoV-MTS network is proposed. We explore the reliable and secure performance of the proposed C-AmBC-NOMA IoV-MTS network with in-phase and quadrature phase imbalance (IQI) at radio-frequency (RF) front-ends and the existence of an eavesdropper. In particular, the analytical expressions on the outage probability (OP) and intercept probability (IP) are obtained after a series of calculations. For a deeper understanding, we discuss the asymptotic behavior of OPs in the high signal-to-noise ratio (SNR) region, the diversity orders of OPs, and IPs in the high main-to-eavesdropper ratio (MER) regime. The results of Monte-Carlo simulation and a series of corresponding theoretical analysis show that: i) As the SNR approaches infinity, the OPs tend to be fixed non-negative values, indicating that the diversity orders of the OPs have error floors; ii) When the MER approaches infinity, the IPs of legitimate users decrease continuously, while the IP of backscatter device (BD) increases; iii) Compared with the system performance under ideal condition, the system performance is less reliable under IQI condition, but the security performance is enhanced; iv) By carefully selecting the system parameters, a trade-off can be achieved between reliability and security.

Ultra-reliable low-latency communication (uRLLC) is envisioned to efficiently support mission-critical scenarios, especially for industrial Internet of Things (IIoT). Considering the requirements of high throughput and massive connectivity in machine-type communications, uRLLC traffic is usually coexisted with enhanced mobile broadband (eMBB) services for the data-intensive industrial cases. To strike a balance between the two distinct tasks, this paper investigates a multi-objective optimization problem by taking into account the performance of both uRLLC and eMBB. Specifically, we aim at maximizing eMBB data rate and uRLLC reliability, while minimizing the communication overhead of control channels caused by uRLLC puncturing. To solve this challenging problem, an analytic hierarchy process method is adopted to estimate the importance of each objective with expert knowledge. Then, a coalitional game is invoked to evaluate the preference degree of resource blocks allocated to uRLLC devices. Following this, an improved Gale-Shapley algorithm is proposed for efficient traffic scheduling. Simulation results demonstrate that the proposed algorithm can achieve better performance in terms of eMBB throughput and uRLLC reliability with the reduced signal overhead.

By exploiting the superiority of nonorthogonal multiple access (NOMA), NOMA-aided mobile-edge computing (MEC) can provide scalable and low-latency computing services for the Internet of Things. However, given the prevalent stochasticity of wireless networks and sophisticated signal processing of NOMA, it is critical but challenging to design an efficient task offloading algorithm for NOMA-aided MEC, especially under a large number of devices. This article presents an online algorithm that jointly optimizes offloading decisions and resource allocation to maximize the long-term system utility (i.e., a measure of throughput and fairness). Since the optimization variables are temporary coupled, we first apply Lyapunov technique to decouple the long-term stochastic optimization into a series of per-slot deterministic subproblems, which does not require any prior knowledge of network dynamics. Second, we propose to transform the nonconvex per-slot subproblem of optimizing NOMA power allocation equivalently to a convex form by introducing a set of auxiliary variables, whereby the time-complexity is reduced from the exponential complexity to$\mathcal {O} (M^{3/2})$. The proposed algorithm is proved to be asymptotically optimal, even under partial knowledge of the device states at the base station. Simulation results validate the superiority of the proposed algorithm in terms of system utility, stability improvement, and the overhead reduction.

This paper contributes improvements on both the defect of dot product and the imbalance of the datasets in matrix factorization. Above all, matrix factorization is still the most widely used technology in the recommendation system. However, its dot product does not follow triangle inequality, which restricts the improvement of its effect. We take inspiration from the distance factor of metric learning, and convert the determinants of user-item relevance from the size of the dot product to the distance of the metric factorization. Furthermore, the number of positive examples is much smaller than the negatives in most datasets. Such an unbalanced scenario will affect the accuracy of recommendations. Inspired by the positive semidefinite matrix of the popular Mahalanobis distance in the field of metric learning, we have fully considered the interaction information between users and items and propose the concept of all-weighted matrix. Finally, the combination of the two improved techniques proposed the All-Weighted Metric Factorization (AWMF) method, which is applied to the personalized ranking task. We have done scientific and adequate experiments on three common datasets, and the results outperform several baselines on different evaluation indicators.

Due to their high mobility, flexible deployment and stable maneuverability, unmanned aerial vehicles (UAVs) have been deemed as a promising and indispensable role for various emerging applications (e.g., dangerous area detection, dynamic target tracking, and map remote sensing). Compared to the static monitoring equipments, UAV-mounted high-definition camera and signal transceiver can be used cost-effectively as an on-demand aerial platform to detect the unknown region and send the real-time data back at the same time. However, these highlighted limitations of battery capacity and communication resource extremely affect the UAV’s performance such as flight endurance and data transmission. Motivated by the above conflicts, this paper aims to minimize the total energy consumed by the UAV during the region detection mission through jointly optimizing the collected data size, transmission time, and flying trajectory. Toward this end, we derive the optimal data collection and transmission time in closed forms via convex optimization, and propose a model-free reinforcement learning-based algorithm for training the UAV to plan its trajectory without knowing the environment information in advance. Simulation results validate the performance of our designs in terms of convergence, energy consumption, and energy efficiency.

Mobile edge computing (MEC) supporting localized context awareness creates a new technological frontier for 5G and beyond. Due to very asymmetric traffic related to MEC and the time division duplexing (TDD) system, we efficiently exploit the networking and computing functionalities for TDD orthogonal frequency division multiple access (TDD-OFDMA) technology supporting multiple services. The primary technical challenge of TDD-OFDMA systems lies in dynamic configuring based on the unknown characteristics of future traffic, i.e., the information lag. Therefore, a model-free online TDD configuration scheme is proposed based on context analysis and multi-armed bandit (MAB) optimization. The characteristics of future traffic are predicted by the context-aware MEC computing, so that TDD configuration is novelly modeled as a contextual MAB problem. Solving MAB by the contextual upper-confidence-bound, TDD configuration can be dynamically adjusted according to network traffic. To simultaneously reduce the energy consumption and makespan of mobile devices (MDs), a greedy resource allocation (GRA) embedded in the TDD configuration is further developed to select MDs and allocate resources. GRA algorithm decomposes the complex multi-factor coupling non-convex problem into a series of convex sub-problems, thereby asymptotically obtaining the selection and allocation with polynomial time complexity. Simulations justify significant performance gain in mobile networking and MEC.

With tremendous computing power in the radio access network, mobile edge computing (MEC) that can support localized context awareness creates a new technological frontier for 5G and beyond. To efficiently exploit the networking and computing functionalities, Time Division Duplex Orthogonal Frequency Division Multiple Access (TDD-OFDMA) type has been considered in this paper. To take advantage of dynamic features in TDD, a model-free online TDD configuration scheme is proposed based on context analysis and Multi-Armed Bandit (MAB) optimization. The TDD configuration problem is therefore novelly modeled as a contextual MAB problem, and is solved by the contextual Upper-Confidence-Bound (C-UCB), which dynamically adjusts TDD configuration to network traffic since that the system cost can be reduced. To further reduce the energy consumption and makespan of mobile devices (MDs), a greedy resource allocation (GRA) embedded in the TDD configuration is developed to select MDs and allocate resources. The simulations demonstrate that proper TDD configuration successfully reduces the system cost, and C-UCB technique approaches the ideal TDD configuration, with significant performance gain when the GRA effectively select and allocate, to strike simultaneous efficiency for mobile networking and MEC.

Wireless self-backhaul is an efficient solution to release the backhaul bottleneck of 5G Heterogeneous Networks (HetNets). This paper aims at jointly optimizing uplink and downlink scheduling to improve system throughput of time division duplex (TDD) HetNets. The problem that considers the uplink-downlink requirements and access-backhaul link coordination constraints is formulated, which has coupled variables and non-polynomial calculation complexity. Based on the structure property of the constructed problem, a joint uplink and downlink scheduling (JUDS) scheme is proposed to manage the complex interference and fully obtain the flexibility of wireless self-backhaul. In JUDS scheme, the original problem is decomposed into a transmission time slot (TTI) orchestration sub-problem and a sub-band scheduling sub-problem, both of which owns high efficient solutions. Simulation results address the effectiveness of JUDS scheme. On the one hand, the total system downlink throughput is significantly improved by JUDS scheme comparing with other existing scheduling schemes. On the other hand, The data rates of access links and backhaul links are well matched.

Content popularity evolution impacts optimization of cache placement and therefore influences design of caching policies. In this paper, we propose a cache updating scheme with popularity prediction and energy cost consideration. Firstly, we use linear regression method to predict future content popularity. Furthermore, we update the preceding cache placement according to predicted popularity aiming at minimizing energy cost. The problem is formulated as a convex optimization problem. Then we transform cache updating cost function, such that the optimal cache updating solution can be gained by Karush-Kuhn-Tucker (KKT) conditions. At last, the simulation results show that our scheme gets better performance in terms of energy consumption compared to the scheme without cache updating energy cost consideration.

Ultra reliable and low latency communication (URLLC) is envisioned as a new paradigm for the fifth generation (5G) mobile communications. To meet its stringent requirement, multi-connectivity attracts attention from both the academy and industrial, especially in Ultra-Dense Networks (UDN) where the user is under the coverage of several base stations (BSs). The dense deployment of BSs leads to a frequent unnecessary handover of moving user equipment (UE), which results in higher handover latency and more signal overhead. To overcome this problem, a received signal strength (RSS) prediction based multi-connectivity handover (RPMCH) scheme is proposed. In the scheme, a new handover triggering mechanism is adopted and the target serving BSs are selected based on RSS prediction. The simulation shows that the PRMCH scheme reduces the number of handovers by more than 50% compared with state-of-the-art handover scheme. In addition, our scheme contributes to a higher average throughput for medium and high mobility cases.

Ultra reliable and low latency communication (URLLC) is one of the three typical service scenarios in the fifth generation mobile communications (5G) system, which supports mission-critical machine-type communication. Grant-free non-orthogonal multiple access (NOMA) system is a promising candidate technology for uplink URLLC scenario but it causes the problem of multi-user detection (MUD). In this paper, we propose a dynamic adaptive compressive sensing (DACS)-based MUD algorithm to realize MUD in URLLC scenario by exploiting user activity sparsity. Different from most of the state-of-the-art compressive sensing (CS)-based MUD algorithms, this algorithm needs no input of user activity sparsity level which may be unknown in practical system. Particularly, this algorithm adopts a stage-wise approach to increase estimated number of active users stage by stage for adaptively acquiring the true user activity sparsity level, introduces a backtracking idea to refine the estimated active user set for more accurate detection, and exploits the temporal correlation between active user sets in adjacent time slots for reducing computational complexity. Simulation results demonstrate that, although the proposed DACS-based MUD algorithm lacks the information of user activity sparsity level, it achieves better bit error rate (BER) performance than the conventional CS-based MUD algorithm.

Due to the severe propagation loss nature of millimeter wave (mmWave), multi-hop relay transmission is a promising mmWave backhaul solution in ultra dense network. In this paper, we propose a multi-path routing scheme taking all small cells' backhaul into account to increase the system throughput. Under multi-hop constraint, we aim to optimize the downlink resource allocation of mmWave band. A two-objective link scheduling problem is formulated, where one objective is to ensure fairness among user equipments (UEs), and the other is to increase the system throughput. Since the two objectives are contradictory, we cannot obtain their optimal solutions simultaneously. To solve this problem, we propose a concurrent transmission based heuristic algorithm, which is called QoS (i.e., quality of service) -aware fairness scheduling (QFS). The proposed QFS scheme aims at transmitting as much data as possible in each time slot and scheduling appropriate links to guarantee the fairness. Simulation results show that QFS scheme can provide better fairness than conventional schemes. The percentage of UEs capable of achieving the minimum data rate requirements by the proposed QFS scheme is about 92.4%, which is greatly higher than those of the conventional schemes. In addition, the throughput of QFS scheme is about 3.18 times higher than Time Division Multiple Access (TDMA) scheme.

This paper aims at maximizing the sum rate of a time division duplex two-transceiver system via the joint access and backhaul scheduling operation. The original scheduling problem that simultaneously considers the causal relationship constraint, the access-backhaul link matching constraint and the minimum data rate requirement is formulated. By using the access-backhaul constraints, the original problem is equivalently simplified into a zero-one integer linear programming with non-polynomial calculation complexity. Based on the simplified problem, an upper bound of the optimal scheduling is achieved. To obtain a practical scheduling solution with reduced calculation complexity, a two-phase access-backhaul scheduling (TPABS) scheme is proposed to approach the obtained upper scheduling bound. The causal relationship constraint and the minimum required data rate are guaranteed to be satisfied in TPABS. Simulation results indicate that TPABS approaches the proposed upper bound with less than 10% sum rate loss.

Ultra-Dense Network (UDN) is foreseen as one of the promising technologies in 5G to meet the ever-increasing mobile traffic demands. However, with existing cell discovery mechanisms, such massive deployment of access points (APs) would lead to a considerable increase in energy consumption and a significant reduction in offloading opportunity. To solve those problems, a User Location Prediction based Cell Discovery (ULPCD) scheme for User-centric Ultra-Dense Network (UUDN) is proposed. In the scheme, the network deployment information is sent to the user equipment (UE) the first time user enters in the range of cells. Then, with the aid of positioning information, the probability of a user entering into a new cell is calculated. Using the probability, UE can predict whether to send a connection request to activate an idle AP before entering in the AP's cell range. Simulation results demonstrate that our proposed scheme reduces over 60% energy consumption of UE and saves at least 20% working time of APs compared with state-of-the-art schemes. Meanwhile, our scheme provides higher detection probability, shorter access delay, and more offloading opportunity.

Due to the flexible deployment, in-band wireless backhaul solutions are preferred to the small cells in the ultradense networks (UDN). This paper manages the scheduling issues of in-band wireless backhaul in UDN by jointly considering the effect of access-backhaul link constraint, the interference and the minimum user data rate requirement. The scheduling problem that aims at maximizing the system throughput is formulated, whose solution is NP-hard. To reduce the calculation complexity and obtain a practical scheduling solution, A scheme named as coloring based access-backhaul scheduling (CBABS) is proposed. In CBABS, the minimum data rate requirements are satisfied by a QoS guarantee algorithm, the interference is managed by coloring algorithm (CA), the matching is done by ensuring the same data rate on access and backhaul link. Simulation results show that 98% of users' minimum downlink data rate requirements and 99% of users' minimum uplink data rate requirements are satisfied. The RB utility ratio is improved by 30% in CBABS compared to traditional scheduling scheme.

This paper aims at improving the system energy efficiency of orthogonal frequency division multiple access small cell networks under user data rate requirements. Differently, in this paper, we take into account the effect of both forward link and backhaul link when formulating the system energy efficiency optimization problem. The optimal solution to the system energy efficiency problem needs the joint consideration of the scheduling, the transmission power optimization, and the backhaul link data rate control, which is NP-hard and generates huge signaling overhead. To this end, we first compute and obtain the lower bound and upper bound of the optimal system energy efficiency based on the property analysis of the formulated system energy efficiency problem. Then, to reduce the signaling overhead and calculation complexity, we propose a forward and backhaul link energy efficiency optimization scheme (FBEEOS) to approach the achieved system energy efficiency bounds. The system energy efficiency is proved to be convergent in a non-decreasing manner in FBEEOS. The simulation results confirm the properties of FBEEOS. In addition, our results show that the transmission power optimization is necessary to achieve higher system energy efficiency though its contribution to total energy consumption is negligible. This is a result of the effect of transmission power on interference and throughput, and needs to be taken into consideration when we optimize the system energy efficiency.

Orbital angular momentum (OAM) provides a new dimension for multiplexing and consequently can improve the link capacity remarkably. In the scope of radio communication, uniform circular array (UCA) is usually used to generate OAM signals, where beam axis detection and alignment is a key factor that influences the detection of OAM signal and directly determines the capacity of OAM links. This study analyses the beam axis detection and alignment issues under the ideal (noiseless) condition. The authors first clarify the fact that for single‐mode OAM transmission, the intensity of electric field in radial direction is characterised by the Bessel function of the first kind. Inspired by this property, they propose three practical methods for beam axis detection and alignment, that is, double parallelogram array method, double UCA method and single UCA method. Iterative translation and rotation strategy are applied to enhance the precision. Finally, the effectiveness of the proposed methods is justified by simulation.

This paper aims at improving the energy efficiency of small cell networks via cell sleep mechanism. The traditional power consumption minimization problem with quality of service (QoS) constraints is reestablished from the distributed small cell eNodeB (SeNB) sleep algorithm. The proposed algorithm consists of a Rate- based Access and Scheduling (RAS) algorithm and a distributed Diluted Load based Sleep (DLS) algorithm. In RAS algorithm, a many-to-many matching model is adopted to deal with the UE access and resource allocation problem. In DLS algorithm, a metric called Diluted Load is introduced to determine the sleep SeNB and solve the sleep optimization problem in a distributed way. By alternately conducting RAS and DLS algorithms, the energy efficiency of small cells keeps improving. Simulation results show that our algorithm can achieve lower power consumption as well as lower UE blocking ratio compared with existing sleep algorithms.

The key obstacle to exploiting orbital angular momentum in wireless communication is the influence of axis misalignment on detection side. This paper summarizes the effects of axis misalignment quantitatively and presents novel automatic methods to overcome the obstacle under the ideal (noiseless) condition. Numerical results in the far field demonstrate the effectiveness of our approach to make orbital angular momentum in wireless communication feasible and reliable.

In macro and Closed Subscriber Group (CSG) femtocells co-channel deployment, femtocells and Macrocell User Equipments (MUEs) suffer strong interference from neighboring femtocells. The interference seriously degrades MUEs' throughput performance or even causes outage. To solve this issue, we provide a centralized component carrier selection scheme. By globally optimizing femtocells' and MUEs' component carrier selection, the proposed scheme can not only avoid the interference among femtocells and the MUEs' interference from femtocells simultaneously, but also maximize the system spectral efficiency and improve MUEs' throughput performance on this basis. Simulation results demonstrate under high data rate demands the proposed scheme can greatly improve MUEs' performance both in total throughput and outage rate than the classical universal frequency reuse scheme. Furthermore, the proposed scheme can achieve better femtocells' total throughput performance than the hard frequency reuse 1/2 scheme.

This paper studies energy efficient power and subchannel allocation in dense small cell networks. After giving the subchannel allocation algorithm, we transform the fractional energy efficient problem into an equivalent iterative optimization problem which has a subtractive form. With the developed utility function of the equivalent problem, we introduce a non-cooperative game to optimize power allocation. The existence and uniqueness of the Nash equilibrium of the game is proved. Besides, the proposed distributed power update needs minor signalling to get the optimal solution. System-level simulation results show that the proposed scheme can quickly reach the convergence and efficiently improve the energy efficiency in small cell networks.

This paper addresses overhead-throughput tradeoff issues for the centralized cooperative cognitive network. Taking the reporting overhead into consideration, a novel frame structure consisting of M + 1 subframes is proposed to maximize the achievable throughput of the cognitive network. According to the channel condition between primary user (PU) and each secondary user (SU), the overhead-throughput tradeoff problem is investigated in two scenarios. For scenario I, we focus on optimizing the number of reporting secondary users (SUs) to achieve overhead-throughput tradeoff. For scenario II, we not only optimize the number of reporting SUs, but also design reporting SUs' selection methods. Numerical results show that under the proposed frame structure, there exists an optimal number of reporting SUs to achieve overhead-throughput tradeoff, and maximize the achievable throughput of the cognitive network.

In cooperative cognitive radio networks (CCRNs), the reporting time is consumed by all secondary users (SUs) for reporting the sensing results to the central node or fusion center. Intuitively, the performance of CCRNs will be degradation, since the more SUs for cooperation, the more reporting time for reporting sensing results. However, most previous studies have not considered this point. In this paper, a cluster-based cooperative spectrum sensing model is proposed in order to reduce the reporting time. Based on a novel frame structure, a sensing-throughput tradeoff problem considering the reporting time is formulated for two scenarios. The optimal clustering rule is obtained by maximizing the transmission time of all SUs. Then, a low-complexity solution is proposed to solve the tradeoff problem, which shows close-to-optimal performance by simulation.