Although camouflaged object segmentation has advanced rapidly in recent years, existing methods are still confined to visual mask prediction under fixed task assumptions. They cannot interactively respond to user requests, nor can they proactively understand and reason about the user’s intent. Our work tackles this issue by proposing a novel task, Language-Guided Reasoning Camouflaged Object Segmentation (LRCOS). Given a camouflaged image and an implicit query text instruction that requires reasoning, LRCOS aims to output intent-consistent segmentation mask. To establish a benchmark for this task, we build CamoQuery, comprising 12,437 image–mask samples and 25971 implicit query text instructions. To better reflect real-world camouflaged scenarios, we additionally collect MCD, a multi-instance camouflage dataset where multiple camouflaged targets co-exist within the same scene, increasing the need for reasoning. Building on CamoQuery, we further propose COSA, a vision–language segmentation assistant that segments the intended camouflaged object from implicit queries and produces a reasoning explanation. Experiments on CamoQuery demonstrate that COSA has strong reasoning segmentation capability in camouflaged scenes and exhibits zero-shot capability.

Multi-access edge computing (MEC) enhances users’ computing capabilities. However, it still faces the queue instability challenge due to the conflict between limited communication-computation resources and the massive access of users. In this paper, we invoke a multiple one-to-one queuing system in a non-orthogonal multiple access (NOMA)-MEC network, where NOMA enables massive access, and study its network stability and queuing delay control mechanisms. Meanwhile, we jointly consider the many-to-one queuing system. We formulate two network power minimization stochastic problems, each subject to the stability of the multiple one-to-one/many-to-one queuing system. We propose Lyapunov-based algorithms to solve these problems analytically and achieve a computational complexity of approximatelyO(N)withNusers. We further investigate the queuing-delay control mechanism in the multiple one-to-one queuing system-based NOMA-MEC network. Numerical results show that the proposed algorithms converge efficiently, effectively stabilize queuing systems, satisfy the queuing-delay requirement, and further demonstrate that: 1) the proposed multiple one-to-one queuing system-based NOMA-MEC exhibits a better balanced load distribution between users and the MEC server compared to orthogonal multiple access (OMA)-MEC; 2) we obtain a more efficient tradeoff between network power consumption and queue backlog compared to the many-to-one queuing system; and 3) we obtain superior queue stabilization capacity under bursty data and network power suppression conditions. This also validates NOMA’s high spectral efficiency and fine-grained remote execution control at the MEC server.

Federated edge learning (FEEL) greatly facilitates the development of ubiquitous intelligence by combining federated learning and edge computing. However, traditional FEEL implementations assume fixed-sized local datasets, neglecting the potential of edge devices to acquire sensory information actively. Such a simplistic scenario leads to overestimating data availability and underestimating resource utilization in networks with varying resource capacity. Moreover, the existing FEEL-oriented systems with integrated sensing, communication, and computation (ISCC) have separate-based designs, leading to an inefficient use of wireless resources. To alleviate these issues, we propose a novel FEEL-oriented ISCC framework in edge collaborative networks, by leveraging the integrated sensing and communication (ISAC) technique to achieve the dual purpose of data sensing and parameter transmission. Then, over the designed framework, we present FEEL convergence analysis under non-independent and identically distributed (non-iid) and iid data. Correspondingly, we formulate a joint beamforming and flexible time duration optimization problem to maximize the convergence speed of FEEL, subject to limited resources on the devices and requirements for data sensing and communication. To address the problem efficiently, we propose an alternative optimization framework, in which the successive convex approximation (SCA) method is adopted to solve the nonconvex beamforming design subproblem, and a low-complexity method is derived for optimal time allocation. Extensive results reveal that the proposed framework can achieve excellent performance in model training accuracy by efficiently utilizing limited resources in edge collaborative networks, under iid and non-iid data.

Using ambient magnetic for indoor localization has been a research focus in recent years. However, intricate magnetic feature patterns in complicated indoor ambiance, with particular emphasis on the multi-scale dynamics arising from diverse user motion states, further hinder localization accuracy and universality. To address these challenges, this paper originally proposes a novel mixed-frequency magnetic data representation learning-based framework (MixLoc) for accurate and universal localization. Our core idea is to systematically model the multi-scale dynamics-affected pedestrian indoor localization as a location-semantic learning problem based on mixed-frequency magnetic data. First, we propose a data augmentation method to automatically construct a mixed-frequency magnetic dataset. Then, we propose a novel encoder to learn temporal and spatial representations from these data and extract subtle differences among multi-scale sequences. Finally, a novel localization model is proposed to accurately infer locations by capturing significant global and local features from both temporal and spatial representations. The evaluation results based on comprehensive experiments show that the accuracy of MixLoc increases about 42% compared to other state-of-the-art approaches.

Unmanned aerial vehicle (UAV) assisted non-orthogonal multiple access (NOMA) communications show great potential in providing simultaneous service to various users. This paper proposes a novel reconfigurable intelligent surface (RIS) aided UAV-swarm NOMA system, where the Coordinated Multi-Point (CoMP) technique is employed to manage inter-cell interference for all users. The primary objective is to maximize the overall security rate by jointly optimizing the UAV swarm trajectories, power distribution among the UAVs, and the reflection coefficients of the RIS. To address the non-convex nature and interdependence of this optimization problem while accommodating long-term dynamic requirements, we introduce an optimization framework that combines alternating optimization (AO) with the weighted-QMIX algorithm. Specifically, successive convex optimization (SCA) is utilized within the AO method to determine the optimal power allocation and RIS coefficients. Meanwhile, the weighted-QMIX algorithm conducts online trajectory optimization for all UAVs through a multi-agent deep reinforcement learning process. Numerical results demonstrate that: 1) the proposed method achieves a higher communication security rate compared to systems lacking RIS, NOMA, or stationary UAVs; and 2) the proposed algorithm outperforms a pure deep reinforcement learning (DRL) algorithm in terms of both computational complexity and security performance.

A reconfigurable intelligent surface (RIS) assisted semi-grant-free (SGF) non-orthogonal multiple access (NOMA) system is investigated. Unlike existing works that only focus on short-term resource allocation, we study a long-term power-saving optimization problem under queue stability constraints and utilize Lyapunov stability theory to deal with delay-aware resource allocation. We first transform the long-term problem into a series of per-time-slot problems by exploiting the Lyapunov theory. Then, the objective function is minimized by alternatingly optimizing the power allocation, channel assignment, and RIS reflection coefficients. In particular, the channel assignment subproblem is solved by invoking a many-to-one matching algorithm. The power allocation sub-problem is addressed by the developed fractional programming algorithm. The reflection coefficients design sub-problem is solved by a penalty-based method, which tackles the rank one constraint and optimizes reflection coefficients. The numerical results validate the effectiveness and show that it can achieve queue stability by setting the Lyapunov parameters. It also shows that the proposed RIS-assisted SGF NOMA system outperforms without RIS and random RIS phase-shift baselines.

Mobile edge computing (MEC) pushes cloud computing capabilities to the network edge, which provides real-time processing and caching flexibility for service-based applications. Conventionally, the individual node solution is insufficient to tackle the increasing computation workload and provide diverse services, especially for unpredictable spatiotemporal service request patterns. To address this, we first propose a hierarchical collaborative computing (HCC) framework to serve users’ demands by reaping sufficient computing capability in Cloud, ubiquitous service area in edge layer, and idle resources in device layer. To better unleash the benefits of HCC and pursue long-term performance, we investigate heterogeneity-aware resource management by collaborative service placement, task scheduling, and resource allocation both in-node and cross-node. We then propose an online optimization framework that first decouples the decisions across different slots. For each instant mixed integer non-linear programming problem, we introduce the surrogate Lagrangian relaxation method to reduce complexity and design hybrid numerical techniques to solve the subproblems. Theoretical analysis and extensive simulation results demonstrate the efficiency of the HCC framework in decreasing system cost on devices, and our proposed algorithms can effectively utilize the resources in the collaborative space to achieve the trade-off between system cost minimization and service placement cost stability.

The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can provide a fullcoverage agile radio environment. A unique STAR-RIS-assisted coordinated multi-point (CoMP) framework is investigated in this paper, where cell-center and cell-edge users are embellished by the reflection and transmission features of the STAR-RIS. Unlike previous works controlling the transmission and reflection phase-shift independently, we consider a more practical coupled phase-shift model. We formulate an online active and passive beamforming problem to maximize long-term energy efficiency (EE) with time-varying locations and channels. Moreover, we propose a hybrid learning framework combining model-free and model-based optimization techniques. For the model-free method, we invoke a risk-sensitive multi-agent deep reinforcement learning algorithm to accelerate the online optimization of the passive beamforming of all STAR-RISs. For the model-based method, we invoke fractional programming (FP) to optimize the coordinated zero-forcing beamformer among all base stations and to realize an exact reward evaluation for each action in the DRL algorithm. Compared to DRL algorithms that optimize passive and active beamforming together, we dramatically shrink the action and state spaces. Comprehensive numerical results explain that the STAR-RIS enhanced CoMP system accomplishes a more excellent EE than the benchmark STAR-RIS cases and that without STAR-RIS.

This paper investigates the design of jointly supporting physical layer security (PLS) and covert communications (CCs) in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (a-STAR-RIS) assisted integrated sensing and communication (ISAC) system. Due to the unified waveform design of ISAC signals, we consider a challenging scenario with two targets being suspicious attackers, where one warden target potentially detects the confidential transmission behavior of covert users and another eavesdropper target attempts to intercept the broadcasted confidential information of security users. We investigate the joint beamforming design at the base station (BS) and the a-STAR-RIS to achieve a high-quality sensing beampattern while meeting covertness and security communication requirements. (1) For the ideal scenario with perfect channel state information (CSI) and precise target locations, we propose an alternative optimization (AO) method to address the optimization problem involving highly coupled variables. Specifically, the optimal beamforming design at the BS is handled using the semi-definite relaxation (SDR) technique, while the beamforming design at the a-STAR-RIS is addressed through a penalty-based iterative algorithm. (2) A more practical case with uncertain target locations and imperfect CSI is considered to achieve a robust beamforming design, where the non-deterministic outage probability constraints are effectively transformed by employing the Bernstein-type inequality. Numerical results demonstrate the superiority of the a-STAR-RIS over the baseline cases and certify that the proposed algorithms can effectively balance the tradeoff among the sensing quality, covert and secure communication requirements. Besides, results also show that the proposed robust beamforming scheme can construct adequate sensing beampattern, even with imperfect CSI and uncertain target locations.

Unmanned aerial vehicle (UAV) assisted communications have been regarded as an effective solution to provide instant services. This paper proposes a novel reconfigurable intelligent surface (RIS) assisted multi-UAV system, where ground users are formed as multicast groups and served by multiple UAVs with coordinated multi-point technique. The goal is to maximize the sum of the minimum rates for all groups by jointly optimizing the trajectories, the cooperative beamforming of the clustered UAVs, and the passive beamforming of the RIS. A hybrid learning scheme is proposed, integrating a multi-agent deep reinforcement learning algorithm, RES-QMIX, and a majorization-minimization (MM)-based alternating optimization. First, the RES-QMIX algorithm is proposed to optimize the trajectories of all UAVs. Then, the alternating optimization is invoked to decouple the joint beamforming into two sub-problems, and each is transformed into a convex quadratic cone programming problem with the MM algorithm. Moreover, the alternating optimization is employed to estimate the reward of the action in RES-QMIX algorithm, thus reducing the action space and achieving the joint optimization. Numerical results show that: 1) The proposed hybrid learning framework achieves fast convergence and outperforms heuristic algorithms; 2) The proposed system obtains a more significant communication rate than CoMP and RIS-only systems.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), as a revolutionary technique, can boost transmission security by controlling unfavorable environments for signal eavesdropping and reducing interference. Integrating unmanned aerial vehicles (UAVs) with STAR-RISs has generated considerable interest due to its enhanced deployment flexibility. However, developing secure communication capabilities using flying STAR-RIS remains an open issue. Therefore, this work investigates the secrecy energy efficiency (SEE) maximization problem for the uplink non-orthogonal multiple access (NOMA) systems, where the UAV-mounted STAR-RIS is employed against the eavesdroppers. Specifically, we consider the joint optimization of the power control, the transmission/reflection coefficients, and the UAV/STAR-RIS’s placement for static and mobile scenarios. The problems are also subject to the minimum data rate requirements and the safety flight region. To tackle the intractable problems, we first adopt the iterative-based method to solve the problem under the static scenario. After that, we invoke the fractional programming and successive convex approximation methods to get the power control scheme, the semidefinite relaxation method to get the transmission/reflection (T/R) coefficients design, and the search-based method to obtain the UAV/STAR-RIS position. Extending to the mobile scenario, we adopt the double deep Q-network (DDQN) algorithm to learn the online UAV trajectory design policy from a long-term perspective. Numerical results unveil that: 1) the proposed iterative-based joint optimization algorithm for static scenarios achieves a near-optimal solution; 2) the NOMA communications aided by the UAV-mounted STAR-RIS achieve significant SEE gain over the conventional reflection-only RIS and the fixed STAR-RIS cases; 3) the DDQN-based algorithm for mobile scenario achieves a near-optimal solution and obtains a valuable performance gain over the short-sighted greedy algorithm.

Integrated sensing and communication (ISAC) has been regarded as an emerging technique to satisfy the sensing requirements for future 6G networks. However, the confidential communication information embedded in the probing waveform could be eavesdropped by the radar targets, which leads to insecurity issues for ISAC systems. To this end, we propose a coordinated multi-point transmission (CoMP) empowered secure ISAC system. Unlike existing work focusing on a single base station (BS), multiple BSs are coordinated to improve sensing performance and communication security. Specifically, non-orthogonal multiple access (NOMA) is employed to improve spectrum efficiency and facilitate spectrum sharing between sensing and communication functions. By importing the artificial noise (AN) to disrupt eavesdropper reception, a joint radar association and beamforming design optimization problem is formulated to maximize the minimum beampattern gain, subject to the maximum power constraint and secure communication requirements. The mixed-integer non-convex optimization problem is first transformed into more tractable forms. Then, a near-optimal solution is obtained by applying an accelerated stochastic coordinate descent algorithm for radar association and the penalty-based iterative algorithm for beamforming design. Moreover, the optimization problem is further extended to more practical cases with uncertain target directions. Our numerical results show: i) the proposed AN-aided COMP-NOMA empowered ISAC system can support much higher high-quality radar sensing, while simultaneously guaranteeing secure communication; ii) the proposed scheme significantly outperforms the relevant benchmark schemes in terms of the beampattern gain; iii) the proposed joint optimization algorithm can achieve high beampattern gain, even with uncertain target directions.

In recent years, the number of network applications is constantly increasing, and network congestion often occurs. To ensure the network Quality of Service (QoS), different types of traffic are classified according to their requirements, and similar traffic is transmitted to the same queue for scheduling. The switch generally uses fair queuing and its extension schemes to schedule traffic. These schemes achieve different bandwidth allocation by configuring different queue weights, so as to obtain a lower packet loss rate. However, the switch can provide us with very few statistical parameters, so using a large number of statistical parameters for adaptive weight adjustment is challenging in implementation. At the same time, the weight range supported by the switch is large, but the action space supported by reinforcement learning is limited, which cannot represent the entire queue weight space. Although deep reinforcement learning can solve the problem with large space, the existing switches can not well support the calculation of neural network model. In this paper, we propose a scheduling optimization mechanism combining Q-learning and genetic algorithm, called QGSO, which is used to schedule traffic in real switches. Firstly, we model the scheduling optimization problem as a Markov decision process (MDP) and use Q-learning to solve it in order to select the optimal queue weights according to the state of the environment. Secondly, we use genetic algorithm to filter out a group of optimal queue weights from the weight space, achieving compression of the solution space. Finally, we use a hardware testbed to test and verify the effectiveness of the algorithm. The experimental results show that our algorithm can effectively schedule traffic and achieve a lower packet loss rate.

A novel simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) assisted collabo-rative multi-cell network is proposed. Cell-center and cell-edge users are enhanced by the STAR-RIS's reflection and transmission functions, respectively. We propose an online joint active and passive beamforming framework to maximize this system's long-term energy efficiency (EE) under time-varying channels and users' requirements. We first invoke fractional programming (FP) to optimize the coordinated zero-forcing beamforming among all base stations and to construct an interference-free transmission during each time slot. Then, a parallel deep reinforcement learning (DRL) algorithm is proposed to facilitate the online optimization of the passive beamforming of all STAR-RISs. Finally, the beamforming calculated by the FP algorithm is utilized as a part of the reward function of the DRL. As a result, the size of the action and state space is reduced, and the dynamic joint optimization is realized. Extensive numerical results reveal that: 1) the proposed algorithm induces a low computation complexity compared with conventional DRL algorithms, and 2) the STAR-RIS enhanced system can achieve higher EE than systems without RIS or with conventional reflection/transmission-only RISs.

In the data center network, mixed flows which have contradictory service requirements are transmitted simultaneously. Switches usually aggregate similar flows to the same queue after flow classification and schedule them using fair queuing and its extension schemes capable of flow isolation. These schemes implement diverse bandwidth allocation by assigning different weights to queues. Existing solutions rely on rich statistics such as packet arrival rate and delay to realize dynamic bandwidth allocation. However, many statistics are difficult to accurately measure or even obtain in real switches due to resource limitations. Providing differentiated services for mixed flows under such restrictions is still a challenge. To solve this issue, this paper proposed a reinforcement learning-based scheduling optimization (RLSO) mechanism. First, mixed flows scheduling is modeled as the Markov decision process (MDP) and Q-learning is used to find the approximate optimal solution with a few statistics. Second, the solution space is compressed to reduce the complexity of the algorithm and adapt to the limited performance of switches. Finally, the performance of the proposed mechanism is evaluated on a hardware testbed with workloads that include coarsegrained and fine-grained flows. The results show that RLSO can effectively schedule mixed flows.

Multiple types of flows with contradictory service requirements, namely mixed flows, coexist in the data center network. Similar flows will be aggregated into the same queue after flow classification and are scheduled in switches by using fair queueing and its extension schemes which are capable of flow isolation. These schemes allocate different bandwidths by adjusting weights to realize differentiated services. However, existing solutions only focus on the requirements of some flows, which leads to the failure to satisfy the requirements of other flows. Therefore, it is necessary to make a trade-off between the service requirements of different flows when allocating bandwidth. In this paper, a max-min fairness based scheduling optimization (MMFSO) mechanism is proposed to schedule mixed flows. First, the bandwidth requirements of each queue are calculated by statistics of the switch. To reduce the influence of sampled statistics while forecasting bandwidth requirements, we introduce the exponentially weighted moving average for bandwidth requirements computation. Second, the bandwidth is allocated to each queue according to the max-mix fairness. The queue weight is determined by the allocated bandwidth of the queue. Finally, the performance of the proposed mechanism is evaluated on the hardware testbed in which workloads include coarse-grained flows and fine-grained flows. The results show that MMFSO can effectively schedule mixed flows.

In this paper, the joint task offloading and resource allocation are investigated for the semi-grant-free (SGF) non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system. Moreover, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) are deployed to improve the quality of wireless communications under the mode switching protocol. Each MU can partially or fully offload its task to the base station (BS) based on its differentiated channel conditions and computing capacity in the proposed MEC system. We formulate the joint task offloading, channel assignment, power allocation, and the RIS coefficients design problem to save energy consumption. The formulated problem is modeled from a long-term optimization perspective as a multi-agent Markov game (MG). Then, a multi-agent deep reinforcement learning (MADRL) based joint task offloading and resource allocation (JTORA) algorithm is proposed to solve the problem. The simulation results confirm that the applied SGF-NOMA scheme can significantly reduce energy consumption under a stringent latency constraint. Moreover, the effectiveness of the STAR-RIS and the proposed algorithm are confirmed.

The problem of resource allocation in a high mobility network is always meaningful while challenging. Due to the mobility characteristic, the main difficulty lies in solving different optimization problems in a limited time, so traditional time-consuming optimization solvers are no longer applicable. This paper considers NGMA-enabled heterogeneous networks (HetNets) and uses end-to-end multi-agent reinforcement learning (MARL) to optimize the resource allocation problem. Even though MARL can use many cooperative agents to divide action space, it may lead to a significant increase in agent number, which is harmful to credit assignment. To tackle this problem, we employ a unique design that can fix the number of agents in any case. Agent credit assignment is then considered to guide agents to work cooperatively and ensure each efficient action gets a suitable reward. Also, a novel learning process named Learn to Improve is utilized to make our method more general. Numerical results and comparison experiments show the effectiveness and robustness of our methods.

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission has been viewed as one of the promising technologies to meet massive connectivity requirements of the next-generation networks. A novel intelligent reconfigurable surface (IRS) assisted SGF NOMA transmission system is proposed, where the IRS is employed to satisfy the channel gain requirements for grant-based users (GBUs) and grant-free users (GFUs). The dynamic optimization on the sub-carrier assignment and power allocation for roaming GFUs, and the amplitude control and phase shift design for reflecting elements of the IRS, is formulated. Aiming at maximizing the long-term data rate of all GFUs, the optimization problem is first modeled as a multi-agent Markov decision problem. Then, three multi-agent deep reinforcement learning based frameworks are proposed to solve the problem under three different IRS cases, including the ideal IRS, non-ideal IRS with continuous phase shifts, and non-ideal IRS with discrete phase shifts. Specifically, for each GFU agent, a sub-carrier assignment deep Q-network (DQN) and a power allocation deep deterministic policy gradient (DDPG) are integrated to dynamically assign network resources for each GFU. For the only IRS agent, two DDPGs are integrated to dynamically assign phase shift and amplitude for each reflecting element of ideal IRS. The single DDPG for dynamically assigning continuous phase shifts, and parallel DQNs for dynamically assigning discrete phase shifts for non-ideal IRS with fixed amplitude are also proposed. Simulation results demonstrate that: 1) The network sum rates of all GFUs can achieve a significant improvement with the aid of IRS, comparing with the system without IRS. 2) The network sum rates of the NOMA assisted SGF transmissions are superior to that of OMA assisted GF transmissions.

A novel intelligent reconfigurable surface (IRS) assisted coordinated multi-point (CoMP) system is proposed. Our objective is to maximize the energy efficiency (EE) of this system by jointly optimizing base station (BS) clustering, user association, sub-carrier assignment, power allocation, and optimal design of the IRS, while satisfying the users’ quality of service requirements. Considering the amplitude and phase shift characteristics, both ideal and non-ideal IRS are investigated. The formulated problem is proved to be NP-hard. By analyzing its structure, we decouple it into the power allocation sub-problem, the BS clustering, UE association, and sub-carrier assignment sub-problem, and the reflection coefficients design sub-problem. For the power allocation sub-problem, we invoke the fractional programming to find the optimal solution. For the reflection coefficients design sub-problem of ideal IRS, the optimal solution is derived with the Lagrangian dual method. Whereas quantization-based method is employed to find the discrete phase shifts for non-ideal IRS. We finally propose a genetic algorithm (GA) to represent the potential solutions of sub-carrier assignment, and combine the other two optimization algorithms as fitness estimator in GA. Numerical results validate the feasibility, fast convergence, and the flexibility of the proposed algorithm. It shows that the proposed scheme outperform the system without IRS and that with a random initialized IRS.

Smartphone-based indoor localization has attracted considerable attentions in both research and industrial areas. However, the localization accuracy and robustness are still challenging problems due to low-cost noisy devices, especially in those complicated localization environments. Considering that pedestrian dead-reckoning (PDR) devices are widely equipped in recent smartphones, we propose a novel indoor localization fusing algorithm that integrates both wireless fidelity (WiFi) features and PDR features. By formulating the fusing indoor localization as a recursive function approximation problem, a sliding-window-based displacement scheme is designed to generate a time-series-based feature data set. We further apply the long short-term memory (LSTM) network for data fusion and localization on this data set by taking advantage of its benefits in time-series prediction and characterization. To evaluate the performance of the proposed algorithm, we compare it with state-of-the-art filter-based localization algorithms in three typical movements and three postures of holding smartphones. Extensive experiment results demonstrate the accuracy and robustness of the proposed algorithm in indoor localization, even in some extreme environments.

With the severe spectrum shortage in conventional cellular networks, millimeter-wave (mmWave) technology has been proposed and is expected to be used in small cells to meet the increase spectrum efficiency, especially for those small cells are deployed with high density and forming as a multi-hop backhaul structure different from conventional schemes with priori given routes, a cross-layer optimization model aiming at maximizing the energy efficiency while taking into account both route selection and resource allocation. This paper decomposes the original problem into two sub-problems: resource allocation sub-problem at the link-physical layer and route selection sub-problem at the network layer. To reflect the interplay property between these two problems, we further propose two joint optimization strategies. The first is using genetic algorithm (GA) for resource allocation with given routes and using linear programming for routes selection with given resource allocation. The second is using the linear programming to evaluate the fitness of each individual in the GA. The simulation results show our solution can efficiently scale with the energy and spectrum efficiency and properly route the traffic without overloading any one of the base stations.

Traditional cellular networks are moving towards heterogenous cellular networks (HetNets) to satisfy the stringent demand for data rates and capacity. To enable the new applications in 5G, such as haptic communications, we face new challenges of achieving high availability with low latency in HetNets. In this paper, we introduce coordinated multi-point (CoMP) and carrier aggregation (CA) techniques in HetNets to guarantee the availability of all UEs, where CoMP improves the single-path availability, and CA enhances availability via multi carrier gain combining. To characterize the availability, we first derive an exact closed-form expression for the availability of a random UE in a CoMP&CA-enabled HetNets. To achieve the maximum UE availability, we formulate a max-min optimization problem. To solve it, we then propose a joint two-step optimization algorithm (JTOA), and our results showcase the effective of our proposed JTOA, and the effective of CoMP in availability improvement in HetNets.

Traditional cellular networks are moving toward heterogeneous cellular networks (HetNets) to satisfy the stringent demand for data rates and capacity. To enable the new applications in 5G, such as haptic communications, we face new challenges of achieving high availability with low latency in HetNets. In this paper, we introduce coordinated multi-point (CoMP) and carrier aggregation (CA) techniques in HetNets to guarantee the availability of all user equipment (UE), where the CoMP improves the single-path availability, and the CA enhances availability via multi-carrier gain combining. To characterize the availability, we first derive an exact closed-form expression for the availability of a random UE in a CoMP and CA-enabled HetNets. To achieve the maximum UE availability, we formulate a max-min optimization problem. To solve it, we then propose a two-step optimization algorithm (TSOA) and a joint (JTOA). The TSOA is based on heuristic algorithm for the optimal subcarrier assignment and UE association, and based on the Lagrangian dual method for the power allocation. The JTOA is based on genetic algorithm to achieve the interaction between the first step and the second step. Our results showcase the effective of our proposed JTOA, and the effective of the CoMP in availability improvement in HetNets.

Spectrum aggregation provides a promising approach to improve the network capacity for Cognitive Radio Ad-Hoc Networks (CRAHNs). Resource allocation for spectrum aggregation-based CRAHNs has become one of the main issues. In this paper, we propose a cross-layer optimization for CRAHNs with the spectrum aggregation. The main objective of our paper is to maximize the network throughput under the network resource constraints. In this regard, we investigate the joint optimization for channel allocation, power control and routing under signal-to-interference-and- noise ratio (SINR) model. This cross-layer optimization problem is decomposed into two sub-problems: a resource allocation at the physical (PHY) layer, and a throughput optimization at the network layer. At the PHY layer, the particle swarm optimization algorithm is proposed to find the suboptimal solution, then at the network layer, linear programming is applied to evaluate the particle's fitness value for throughput maximization. The simulation results demonstrate that the joint optimization of channel allocation and power control is an effective way to improve network throughput, especially when the network has sufficient power supply.

The exponential growth in data traffic and dramatic capacity demand in fifth generation (5G) has inspired the move from traditional single-tier cellular networks towards heterogeneous cellular networks (HetNets). To face the coming trend in 5G, the high availability requirement in new applications, needs to be satisfied to achieve low latency service. In this work, we present a tractable multi-tier multi-band availability model to examine the high availability in carrier aggregation (CA)-enabled HetNets. We first derive a closed-form expression for the availability in CA- enabled HetNets based on the signal-to-interference- plus-noise model. By doing so, we formulate the joint subcarrier and power allocation problem, to maximize the availability under the power constraint. The optimization problem is non-convex problem, which is challenging to solve. To cope with it, the genetic algorithm (GA) is proposed to optimize availability through joint subcarrier and power allocation. The average availability in CA-enabled HetNets improves with decreasing the number of UEs, and increasing the power budget ratio interestingly.

In this paper, we propose an energy aware movement-assisted deployment algorithm to achieve complete coverage and load balance for wireless sensor networks. By dividing the target area into several coronas with the same width of sensor communication radius, the optimal deployment density is computed for each corona. To the whole network, the movement of sensors can be treated as two steps. For the first step, the nodes move straightly between different coronas to regulate the number of nodes in each corona. For the second step, each node moves the adaptive distance inside the corona to achieve uniform distribution by exchanging messages only to its one-hop neighbor. Simulation results are provided to verify our analysis for wireless sensor networks.