Multi-satellite cooperative transmission (MSCT) is a promising paradigm to enhance spectral efficiency in Low Earth Orbit constellations. However, heterogeneous Doppler shifts and coupled multi-satellite interference hinder the capacity improvement of conventional precoding in multiple-input multiple-output (MIMO). Although orthogonal time-frequency space (OTFS) modulation exhibits robustness against doubly-selective channels, its direct application in massive MIMO faces prohibitive computational complexity. To address these challenges, this paper proposes a joint precoding and link scheduling (JPL) design for OTFS-based MSCT systems. Specifically, we formulate a sum-rate maximization problem that couples continuous precoding matrices with discrete link indicators, and decompose it into two tractable subproblems. For precoder design, based on regularized zero-forcing (RZF)-criterion, we present single-satellite precoding (SSP) and multi-satellite precoding (MSP) in the delay-Doppler domain for different MSCT modes, and optimize per-satellite regularization coefficients to maximize the sum-rate. Leveraging the quasi-banded MIMO-OTFS structure, a low-complexity RZF algorithm is developed to reduce the cubic complexity to quadratic order without performance loss. Based on random matrix theory, we conduct asymptotic analysis and derive the deterministic equivalents of sum-rate for SSP and MSP. For link scheduling, a two-stage heuristic algorithm is designed based on tabu search to iteratively optimize link indicators for maximizing the sum-rate. By alternately optimizing precoding matrices with each scheduling iteration, the JPL algorithm is proposed to achieve near-optimal sum-rate performance with polynomial computational complexity. Numerical results demonstrate the proposed schemes significantly improve the sum-rate performance compared to existing works.

Low earth orbit (LEO) satellite constellations are the key enablers to achieve the ubiquitous communication in the 6G era, but the performance is hindered by the Doppler effects and the underutilization of spatial diversity. By leveraging the quasi-static properties of the delay-Doppler (DD) domain, orthogonal time-frequency space (OTFS) demonstrates robustness against the Doppler shifts. Moreover, spatial modulation offers the potential to improve spectrum efficiency and energy efficiency. This paper proposes an OTFS-based spatial modulation (OTFS-SM) paradigm for multi-satellite cooperative transmissions, aiming to enhance system reliability. In order to further exploit the multi-satellite diversity, a satellite selection (SS) strategy based on channel capacity maximization criterion is proposed. Additionally, the average bit error rate (ABER) upper bound of the proposed OTFS-SM system in multi-satellite scenarios is derived using minimum mean square error (MMSE) detection. Numerical results verify the theoretical analysis of the ABER and demonstrate that the proposed strategy significantly outperforms both the single-satellite scenario and the OFDM-SM system in terms of ABER performance.

The increasing demand for spectrum resources has necessitated spectrum-sharing between Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellite systems, introducing the risk of inter-system interference. This interference degrades communication performance, making effective mitigation strategies critical. Unlike existing works that utilize fixed-position antennas, this paper proposes a novel interference mitigation scheme for LEO-GEO spectrum-sharing system, aided by movable antennas (MAs). Specifically, the LEO satellite is equipped with MAs which fully leverage spatial degrees of freedom to enhance LEO system service and mitigate interference. To achieve these objectives, we formulate a max-min signal-to-interference-plus-noise ratio (SINR) optimization problem, subject to LEO-GEO interference mitigation constraints. Due to the non-convex nature of the proposed problem, we design an alternating optimization algorithm, which can optimize signal-to-leakage-noise ratio precoding, power allocation, and MA positions alternatively and jointly. Simulation results demonstrate that the proposed scheme significantly improves the worst LEO user's SINR while ensuring that interference to GEO terminals remains below a specified threshold.

Satellite communication (SatCom) is regarded as a key enabler for bridging connectivity and capacity gaps in sixth-generation (6G) networks. However, the proliferation of Low Earth Orbit (LEO) satellites raises significant intersystem interference risks with Geostationary Earth Orbit (GEO) systems. This paper introduces a cooperative multi-satellite multi-reconfigurable intelligent surface (RIS) transmission framework to mitigate such interference while enhancing LEO SatCom performance. Specifically, cooperative beamforming is designed under a non-coherent cell-free paradigm, considering both adaptive and max ratio (MR) precoding, as well as statistical and two-timescale channel state information (CSI), aiming to synthesize the advantages of cell-free and RIS into SatCom in a practical way. Firstly, an alternating optimization (AO)-based design leveraging statistical CSI with adaptive precoding is proposed. Then, we propose a power allocation algorithm under MR precoding with given RIS phase shifts obtained from the former, along with a direct two-stage design bypassing prior results. Additionally, we extend derived closed-form expressions and proposed algorithms to exploit two-timescale CSI. Numerical results demonstrate the impact of intersystem interference mitigation constraints, compare the performance of proposed algorithms, draw insights into the effects of transmit power, interference threshold, and Rician factors, validate SatCom performance enhancements achieved by RISs, and discuss the advantages of multi-satellite cooperation.

Low Earth orbit (LEO) communication satellites are becoming an essential component of the integrated spaceterrestrial network for sixth-generation (6G) networks nowadays. However, due to the time-varying communication environment and long round-trip time (RTT), the downlink channel state information (CSI), which is always obtained by satellites from user equipments feedback, is severely outdated. Furthermore, as the satellite moves, changes in elevation angle result in received CSI with varying correlation characteristics and levels of outdatedness. Motivated by these issues, this paper proposes a deep learning (DL)-based prediction scheme named ATNet, which is designed to make channel prediction in frequency division duplexing (FDD) satellite Communication systems. Specifically, the predictor integrate convolutional neural network (CNN) and attention mechanism with the foundation of long short-term with memory (LSTM) network. This scheme considers the relationship between elevation angle and channel correlation, as well as the degree of CSI outdatedness. Simulation results demonstrate that our ATNet scheme has achieved a $7 \%-17 \%$ improvement in prediction accuracy compared to the baseline schemes.

The rapid expansion of low-Earth orbit (LEO) constellations has greatly intensified the difficulty of mitigating interference among them. Beam hopping (BH) technology is widely considered as one of the key technologies to achieve on-demand services while avoiding interference among these constellation systems. However, the majority of existing BH schemes neglect to address the interference among separate constellations. This paper proposes a novel BH scheme to tackle interference challenges in multi-LEO constellations coexistence scenarios. In contrast to existing BH schemes that predominantly target intra-system interference mitigation, our proposed BH scheme has the capability to dynamically adjust the beam bandwidth to avoid both intra-system interference and inter-system interference. We formulate an optimization problem that maximizes throughput, guarantees delay fairness, minimizes transmission power for LEO constellations, and avoids inter-system interference. To tackle this formidable non-convex and non-linear problem, the optimization problem is interpreted as a sequential decision-making problem, modeled by a Markov Decision Process (MDP). By utilizing the Advantage Actor-Critic (A2C) algorithm, the BH pattern is dynamically designed to avoid interference between beams from multiple LEO constellations in a time-varying network environment. Simulation results demonstrate that the proposed scheme outperforms other schemes in terms of long-term throughput and delay fairness.

Transit signal priority (TSP) is an effective strategy to enhance the operational efficiency of buses and increase bus ridership. Nonetheless, existing TSP strategies seldom utilize connected information to control buses collaboratively and take safety into consideration. In this paper, we propose a method that combines signal timing optimization with longitudinal control of Connected and Autonomous Vehicles (CAVs) to improve efficiency and comfort for passengers while ensuring safety. This method modifies signal timing at isolated intersections to prioritize buses as well as controls the delayed CAV bus to catch the green light smoothly and cooperatively change lanes based on Vehicle-to-Everything (V2X) communication. Moreover, the Control Barrier Functions are used as safety-critical constraints in Model Predictive Control to avoid collisions. The effectiveness of the proposed method is evaluated in comparative analyses against controllers without TSP or longitudinal control in CARLA. The simulation results reveal that the proposed method reduces waiting time at intersections and velocity fluctuations while maintaining a safe following distance.

The growing interest in deploying satellite communication (SatCom) systems results in a proliferation of both Geostationary Earth Orbit (GEO) and Low Earth Orbit (LEO) satellites, leading to the risk of inter-system interference between GEO and LEO systems, which can result in degraded communication performance or even complete system failure. Under this context, this paper investigates Reconfigurable Intelligent Surface (RIS)-aided LEO SatCom with LEO-GEO inter-system interference mitigation. Specifically, multiple satellites and multiple RISs are operated in a cooperative manner with properly designed joint beamforming. We formulate a minimum signal-to-interference-plus-noise (SINR) maximization problem exploiting statistical channel state information (CSI), subject to LEO-GEO interference mitigation constraint. We propose an alternating optimization (AO)-based algorithm, combined with quadratic transform and manifold optimization techniques, to design the cooperative multi-satellite multi-RIS beamforming iteratively. Numerical results show that the proposed scheme ensures the mitigation of LEO-GEO interference while effectively improving the performance of LEO SatCom.

Using satellite communications as backhaul links facilitates extending network coverage to unconnected areas. However, providing high-quality video streaming service via satellite backhaul is not economical. This paper presents SatSR, a mobile edge computing (MEC)-based super-resolution (SR)-enhanced adaptive on-demand video streaming system for mobile networks with satellite backhauls. Particularly, SR-based video quality enhancement is integrated into the video streaming process, so that low-quality videos with small sizes can be transmitted by satellite links and then enhanced to be high-quality. Meanwhile, SatSR offloads computation-intensive SR processing from user equipment (UE) to the MEC server to relieve UEs’ computation burden and speed up the SR processing. Specifically, the framework and the operation process of SatSR are designed first. Then, to mitigate the impact of SR processing delay, a pipelined mechanism is proposed, which can coordinate the video transmission and SR-based enhancement efficiently. Furthermore, an SR scale factor adaptation algorithm based on deep reinforcement learning is proposed to cope with the fluctuation of communication links. Finally, a system prototype and a chunk-level simulator of SatSR are built, respectively. The experiments results validate that SatSR outperforms baselines significantly, including both the UE-based SR-enhancement video streaming scheme and the traditional bitrate adaptation based video streaming scheme.

Full frequency reuse combined with precoding is a promising solution for multibeam satellite systems (MSSs) to meet the evergrowing capacity demand. However, line-of-sight-dominant satellite-ground channels will cause severe channel correlation among the geographically clustered hotspot users (HUs), which restricts multiuser capacity over HUs. In this paper, we propose the reconfigurable intelligent surface (RIS)-aided hotspot capacity enhancement scheme for MSSs. We formulate a hotspot sum rate maximization problem with SINR constraints added on a different user set and present an alternating optimization (AO)-based algorithm for its solution. To reduce computational complexity, we propose a two-stage algorithm that sequentially optimizes RIS phase shift with manifold optimization and satellite precoding, no longer resorting to AO. The RIS phase shift design utilizes semi-orthogonal subspace maximization and pairwise channel decorrelation. This design effectively formulates the interplay between RIS phase shifts and transmit beamforming related to the SINR constraint. To circumvent high channel estimation overhead, we extend the algorithms to low-cost designs exploiting statistical channel state information. Simulation results demonstrate that our proposed RIS-aided MSS designs substantially enhance HUs’ sum rate, attributed to the RIS-enabled channel refinement mechanism. Moreover, the two-stage algorithm achieves a comparable performance to the AO-based algorithm.

Precoding techniques combined with aggressive full frequency reuse (FFR) are a promising solution to meet the evergrowing capacity demand for multibeam satellite systems (MSSs). However, LOS-dominant satellite-ground propagation environments will cause strong channel correlation among the geographically clustered hotspot users (HUs), and the consequent degradation of spatial multiplexing gain severely restricts multiuser multiple-input multiple-output (MU-MIMO) capacity. In this paper, we propose a reconfigurable intelligent surface (RIS)-aided scheme for MSSs to enhance the hotspot capacity of HUs via RIS-improved spatial-multiplexed transmission. Specifically, we propose to employ a RIS in the MSS's hotspot area formed by HUs for channel decorrelation and then jointly optimize the RIS passive beamforming and satellite precoding to reap the benefits of the spatial multiplexing. In this context, we formulate a novel HUs' sum rate maximization problem, subject to the transmit power constraint, the quality-of-service constraints for non-hotspot users, and the unit-modulus constraint for the RIS. The formulated problem is non-convex, and we propose an efficient iterative algorithm based on quadratic transform, semi-definite relaxation, and alternating optimization methods to solve it. Simulation results show that in our proposed RIS-aided scheme with optimized precoding and passive beamforming, significant sum rate enhancement is achieved for HUs in the MSS, owing to channel reconfigurable capability brought by the RIS.

Low Earth Orbit (LEO) satellite communication (SatCom) is considered a promising solution to supplement terrestrial networks. However, line-of-sight (LoS) links between the satellite and terrestrial terminals are possibly blocked by objects on Earth (e.g., mountains, tall buildings, etc.), leading to communication interruptions. This paper introduces Reconfigurable Intelligent Surface (RIS) into the LEO SatCom system to extend satellite coverage in blockage scenarios. Specifically, in contrast to the signal transmission model under the far-field assumption in usual studies, we present a model combining the near-field and far-field of the RIS, which can characterize the operational differences of each reflecting element more accurately. Next, a user-received power maximization problem is formulated and we solve it by jointly optimizing the RIS phase shifts and orientation. Simulation results demonstrate the effectiveness of the proposed optimization algorithm. Remarkably, it is shown that the scheme based on the proposed algorithm can assist the satellite in extending its coverage and providing higher power than that before blockage occurs compared to the baseline schemes.

This paper investigates the slice-based service function chain embedding (SBSFCE) problem, which is to embed the service function chains (SFCs) of flows from different slices on a physical network for end-to-end network slice deployment. Compared with regarding slice deployment as complete virtual network embedding (VNE), deploying slices from the perspective of SBSFCE is beneficial for achieving more delicate resource allocation and jointly optimizing virtual network function (VNF) mapping and link mapping without the need for particular virtual topology designs. However, performing effective SBSFCE also faces several key challenges like diversified and differentiated requirements of flows, inter-slice and intra-slice VNF sharing, priority-aware admission control, and VNF placement restrictions, and few existing SBSFCE works have comprehensively considered or solved these challenges. In view of this, we address the SBSFCE problem by jointly considering the above key challenges in this paper. Specifically, we formulate the SBSFCE problem as an integer linear programming (ILP) that aims to maximize flow acceptance ratios and minimize network resource costs. Then, we propose two novel heuristic algorithms, weight-oriented embedding (WOE) and weight-oriented ratio embedding (WORE), to solve the problem. Simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve near-optimal performance.

Mobile crowdsensing (MCS) has appeared as a viable solution for data gathering in Internet of Vehicle (IoV). As it utilizes plenty of mobile users to perform sensing tasks, the cost of sensor deployment can be reduced and the data quality can be improved. However, there exist two main challenges for the IoV-based MCS, which are the privacy issues and the malicious vehicles issues. Therefore, how to protect privacy, resist malicious vehicles, and recruit trustworthy contributors are crucial to be investigated. In order to solve the challenges simultaneously, we propose a privacy-protecting and reputation-based participant recruitment scheme, including a privacy-protecting mechanism, reputation value calculation method, and reputation-based recruitment algorithm. In particular, the privacy-protecting mechanism can be executed quickly since 1) its signaling overhead is low and 2) the computation-intensive procedures are processed by the mobile edge server. The reputation value calculation method considers the vehicle’s past behaviors and Quality of Information (QoI) to guarantee the accuracy. In addition, the introduction of time fading makes the current behavior have a larger weight. The reputation value will drop rapidly once a malicious behavior is detected, so that the method can detect malicious vehicles more quickly. Moreover, we propose a reputation-based recruitment algorithm to recruit appropriate vehicles to perform sensing tasks based on the above method. Simulation results demonstrate that our method can assess the reputation value accurately and detect malicious vehicles quickly while protecting the privacy. It is shown that our recruitment algorithm can realize load balancing, improve sensing quality, and reduce the cost.

Heterogeneous network (HetNet) and Non-Orthogonal Multiple Access (NOMA) have been seen as promising ways to improve the network capacity. However, the dense deployment of base stations (BSs) in HetNet causes unbalanced loads and large energy consumption. In this paper, we investigate the joint user association and power control problem with the objective of improving the sum rate and the energy efficiency in the Orthogonal Multiple Access (OMA)-enabled HetNet and NOMA-enabled HetNet, respectively. Particularly, the traditional joint user association and power control algorithms can only solve the problem for one network scenario, which are not applicable to the other networks. Specifically, this paper presents a novel deep reinforcement learning (DRL)-based general optimization framework, which is a unified solution for the user association and power control problem and can adapt to OMA-enabled and NOMA-enabled HetNet scenarios with relatively minor modifications. In the framework, a Deep Deterministic Policy Gradient Algorithm (DDPG)-based joint user association and power control algorithm is proposed that can learn to achieve load balance and improve energy efficiency by interacting with the environment. Specifically, different from the DDPG, the proposed algorithm can solve the discrete and continuous optimization problems together. Finally, simulation results demonstrate the proposed algorithm achieves a higher sum rate and energy efficiency than the traditional algorithms in both OMA-enabled HetNet and NOMA-enabled HetNet.

Mobile edge caching has been proven effective in improving the network utility and users’ quality of experience (QoE). Due to the exponential growth of mobile video traffic and the limited cache capacity of the base station (BS), how to pick appropriate video contents to cache is an important challenge. Different from existing users’ cache hit ratio maximization, this paper aims at improving both the users’ QoE and network utility for the cache-enabled network. To achieve this goal, we investigate an optimization problem of QoE-aware joint segment-based video caching and user association (JSVCUA). Then we propose an iterative framework to decouple the original non-convex problem into the cache placement problem and user association problem. For the cache placement problem, we propose utilizing the neural collaborative filtering (NCF) scheme to accurately predict the higher-level and non-linear preferences between users and video contents. Besides, the segment-based caching strategy is presented to further improve the efficiency of caching. Then the caching decision is made by maximizing the QoE weighted content transmission rate. For the user association problem, convex optimization is applied, and the performance gain of caching is squeezed. Simulation results verify that the proposed algorithm can significantly improve both the network utility and QoE on a real dataset. Specifically, the cache hit ratio increase by 10% on average.

Mobile crowdsensing (MCS) has appeared as a viable solution for data gathering in Internet of Vehicle (IoV). As it utilizes plenty of mobile users to perform sensing tasks, the cost on sensor deployment can be reduced and the data quality can be improved. However, there exist two main challenges for the IoV-based MCS, which are the privacy issues and the existence of malicious vehicles. In order to solve these two challenges simultaneously, we propose a privacy-protecting reputation management scheme in IoV-based MCS. In particular, our privacy-protecting scheme can execute quickly since its complexity is extremely low. The reputation management scheme considers vehicle's past behaviors and quality of information. In addition, we introduced time fading into the scheme, so that our scheme can detect the malicious vehicles accurately and quickly. Moreover, latency in the IoV must be exceedingly low. With the help of the mobile edge computing (MEC) which is deployed on the base station side and has powerful computing capability, the latency can be greatly reduced to meet the requirements of the IoV. Simulation results demonstrate effectiveness of our reputation management scheme in resisting malicious vehicles, which can assess the reputation value accurately and detect the malicious vehicles quickly while protecting the privacy.

Narrowband Internet of Things (NB-IoT) is a new communication technology designed for machine type communications (MTC), which needs to support much more devices compared to Long Term Evolution (LTE). But the random access (RA) process of it is also based on the slotted-ALOHA mechanism like LTE, which is not that suitable for machine to machine (M2M) communications. When a device's access attempt failed, the time of backoff and access class barring (ACB) is random. This would lead to a lot of meaningless failed attempts when the access load is heavy. And the access successful probability and energy efficiency will be very low. In this paper, we propose a novel access protocol based on resource grouping and distributed queuing (RGDQ) mechanism to effectively solve the massive access issue in NB-IoT. Firstly, we apply DQ mechanism into the access process of NB-IoT. Afterwards, we newly propose an arrival-based access resource grouping mechanism (RG) to reduce the access delay caused by the queuing process of DQ. In addition, we develop an analytical model to accurately estimate the access performance of the proposed protocol. Finally, computer simulations are also performed to validate the accuracy of the analytical model and verify the proposed protocol in comparison with the NB-IoT standard and conventional DQ access schemes.

To meet the dramatically increasing traffic demand, licensed-assisted access (LAA) has been proposed for long-term evolution (LTE) systems to operate on the unlicensed spectrum. However, it is a huge challenge to achieve fair and efficient coexistence between LAA and Wi-Fi on the same band. In this paper, we devise a novel channel access approach for LAA eNBs to improve system throughput and achieve fair coexistence with Wi-Fi nodes. Specifically, Carrier Sense Multiple Access with Enhanced Collision Avoidance (CSMA/ECA), which is able to reach and maintain collision-free operation by deterministic backoff (DB) after successful transmissions, is introduced to the listen-before-talk (LBT) procedure of LAA eNBs to improve the coexistence system throughput. However, note that the fixed DB value in traditional CSMA/ECA cannot ensure fair coexistence all the time corresponding to different network sizes. Therefore, an adjustable DB value scheme is devised to replace the fixed one, in order to achieve fair coexistence whatever the number of Wi-Fi nodes and LAA eNBs is. Aiming to obtain the maximum system throughput improvement, the behavior of a LAA eNB is modeled as a Markov chain, and the throughput of the coexistence network is derived. Then, we formulate a throughput optimization problem and develop an algorithm of DB value adjustment, which can not only achieve the maximum throughput improvement but also maintain the fair coexistence whatever the number of nodes in the coexistence network is. At last, numerical results are presented which validate the accuracy of our analysis model and demonstrate the effectiveness of the proposed algorithm.

Energy harvesting from ambient radio frequency (RF) radiation is an efficient approach to provide wireless energy replenishment for mobile devices. At the same time, introducing active/sleep modes in base stations (BSs) is an effective method to save the energy consumption of cellular networks. However, the performance of the two techniques would be dependent and interact with each other if both of them were employed in cellular networks. In this paper, we analyze the performance and investigate the sleeping strategy design problem in the cellular network, in which the mobile devices have the capability of energy harvesting from the ambient RF signals. Specifically, using tools from stochastic geometry theory, we derive analytical expressions of the coverage probability and the harvested energy and show that they are both affected by the sleeping strategy. Then, we formulate a BSs power consumption minimization problem under the coverage probability and harvesting performance constraints. Finally, the optimal operating regime of the sleeping strategy is derived. Numerical results confirm the correctness of the analysis and show the effect of switching off BSs on both performance metrics.

In this paper, the power allocation problem, which aims to optimize the energy efficiency (EE) of downlink OFDMA heterogeneous networks, is researched. Specifically, the terminals are sensor nodes in the Internet of Things (IoT) with RF energy harvesting (EH) technology. This technology enables the sensor nodes to capture energy from the wireless signals in air, constitutes a permanent energy source, and provides self-stainability to them. Traditional interference analysis and EE modeling are unsuitable for our EH introduced problem, so we model the energy-efficient power allocation problem as an EH based non-cooperative (EHNC) game. Then we prove the existence and reveal the uniqueness conditions of the EHNC game equilibrium. We propose a distributed energy-efficient power allocation algorithm to solve the problem, and reveal that the game can converge to Nash equilibrium after certain times of iterations. Simulation results show that the proposed algorithm is effective in increasing energy-efficient performance, harvesting energy and guaranteeing QoS of the IoT sensor nodes simultaneously.

This paper investigates proportional-fair energy-efficient radio resource allocation problem for the uplink transmission of OFDMA smallcell networks. Instead of the fairness measured by users' achievable data rates, this paper concentrates on the fairness in terms of energy efficiency (EE) and aims to provide EE-based proportional fairness guarantee among all users in smallcell networks. Specifically, EE-based global proportional fairness utility optimization problem is formulated, taking into account both the minimum data rates requirements and the cross-tier interference constraints. In order to make the problem more tractable, it is transformed into a weighted sum maximization problem of each user's instantaneous EE utility. Then, a two-step scheme is adopted, which solves subchannel allocation and power allocation separately, and the corresponding algorithms are devised. The proposed subchannel allocation algorithm is heuristic and low-complexity. The power allocation scheme is optimal, and is devised based on a novel method which can solve the sum of ratios problems efficiently. Numerical results verify the effectiveness of the proposed algorithms, especially the good capability of ensuring high level EE fairness among all users in the smallcell network.

WiFi offloading is a cost-effective and practical solution to alleviate the problem of highly congested cellular networks. Recent theoretical and experimental studies show that delayed WiFi offloading where traffic can be delayed to increase the chance of meeting WiFi can significantly improve offloading efficiency. Nevertheless, there is no exact analytic model to analyze the offloading benefits with different types of traffic. In this paper, we propose a preemptive priority queuing analytic model for delayed offloading with multi-level priority traffic and derive expressions for the average delay and offloading efficiency of traffic with different priorities as a function of the WiFi availability, deadlines, and other key parameters. At last, we validate the accuracy of our queuing model by simulation in different scenarios and clarify how to choose a suitable deadline for traffic of different priorities.

The explosive growth of mobile multimedia services has caused tremendous network traffic in wireless networks and a great part of the multimedia services are delay-sensitive. Therefore, it is important to design efficient radio resource allocation algorithms to increase network capacity and guarantee the delay QoS. In this paper, we study the power control problem in the downlink of two-tier femtocell networks with the consideration of the delay QoS provisioning. Specifically, we introduce the effective capacity (EC) as the network performance measure instead of the Shannon capacity to provide the statistical delay QoS provisioning. Then, the optimization problem is modeled as a non- cooperative game and the existence of Nash Equilibriums (NE) is investigated. However, in order to enhance the selforganization capacity of femtocells, based on non-cooperative game, we employ a Q-learning framework in which all of the femtocell base stations (FBSs) are considered as agents to achieve power allocation. Then a distributed Q- learning-based power control algorithm is proposed to make femtocell users (FUs) gain maximum EC. Numerical results show that the proposed algorithm can not only maintain the delay requirements of the delay-sensitive services, but also has a good convergence performance.

This paper addresses the energy-efficient power allocation problem of downlink transmission with delay quality of service (QoS) constraint in the femtocell networks. Particularly, in order to provide statistical delay guarantee, the effective capacity (EC) is employed as the network performance measure instead of the conventional Shannon capacity. As a result, the energy efficiency (EE) metric of the femtocell is defined to be the total-EC-to-the-overall-power-consumption ratio of the femtocell base station (FBS). The optimization problem is firstly modeled as a supermodular game. Then the existence and characteristics of the Nash Equilibrium (NE) are investigated. A distributed energy-efficient power allocation algorithm is also designed to implement the game. Simulation results demonstrate that, our proposed algorithm delivers substantial energy efficiency improvement while satisfying a wide range of delay requirements.

Femtocells have attracted growing attentions in academia, industry, and standardization forums in recent years. However, most of existing works on femtocell networks are focused on spectrum efficiency and interference mitigation, energy efficiency aspect is neglected. In this paper, we investigate the maximization of energy efficiency of downlink OFDMA dense femtocell networks by efficient resource allocation. To decrease the complexity, joint subchannel allocation and power control are decomposed into two steps. Power control has been modeled as a non-cooperative game, a closed-form best response of transmit power is obtained. Considering fairness and low complexity, a fair time-averaged subchannel allocation metric have been derived out. Based on that, we propose a distributed suboptimal subchannel allocation and optimal power control algorithm. Simulation results show that the proposed algorithm has a low complexity with slight loss of energy efficiency compared with Round-Robin Scheduling and a noncooperative energy-efficient power optimization algorithm.