CEAR: A cooperation based energy aware reward scheme for next generation green cognitive radio networks

Cognitive Radio (CR) networks are envisioned as a key empowering technology of the fifth-generation (5G) wireless communication networks, which solves the major issues of 5G, like high-speed data transmission, seamless connectivity, and increased demand for mobile data. Another significant characteristic of the 5G network is green communications, as energy consumption from the communication field is predicted to rise remarkably by the year 2030. In this work, we are concerned about energy-related issues and propose a cooperation-based energy-aware reward scheme (CEAR) for next-generation green CR networks. The proposed CEAR scheme is based on the antenna and temporal diversity of the primary users (PUs). For providing the service to the PUs, the users of another network called cognitive users (CUs) work as a cooperative relay node, and, in return, they get more spectrum access opportunities as a reward from the primary network. The CUs with delay-tolerant data packets take a cooperative decision by recognizing the availability and traffic load of PUs, channel state information, and data transmission requirements. We utilize the optimal stopping protocol for solving the decision-making problem and use the backward induction method to obtain the optimal cooperative solution. The simulation results reveal notable enhancements in energy efficiency (EE) of CUs compared with other cooperative schemes. The proposed CEAR scheme is more energy-efficient for ultra-dense network deployment because results show that the CU’s EE, spectral efficiency (SE), and throughput improved with the increase of PUs.     

Deep learning-based energy-efficient relay precoder design in MIMO-CRNs

This paper investigates the problem of energy efficient relay precoder design in multiple-input multiple-output cognitive relay networks (MIMO-CRNs). This is a non-convex fractional programming problem, which is traditionally solved using computationally expensive optimization methods. In this paper, we propose a deep learning (DL) based approach to compute an approximate solution. Specifically, a deep neural network (DNN) is employed and trained using offline computed optimal solution. The proposed scheme consists of an offline data generation phase, an offline training phase, and an online deployment phase. The numerical results show that the proposed DNN provides comparable performance at significantly lower computational complexity as compared to the conventional optimization-based algorithm that makes the proposed approach suitable for real-time implementation.     

Stochastic geometry analysis of coverage probability in energy efficient dense heterogeneous network with sleep control mechanism

The upsurge of data traffic in the macrocell networks has led to the massive deployment of small cells (SCs) for load sharing. Though small cell power consumption is low individually, significant aggregate power consumption, together with the MBS transmission power that is exponentially increasing over its served user load, deteriorates the network energy efficiency (EE). Consequently, EE is seen as a critical requirement for wireless network design in future. The challenge of EE maximization in dense heterogeneous networks is investigated in this research through a strategic small cell sleeping technique. A heuristic method based on distance and load awareness strategy in which the small cells with fewer users near the macrocell will be put into sleep mode. User equipments (UEs) under the service of the sleep small cells will be offloaded to the MBS. A dense heterogeneous network is considered with overlapped small cell coverage regions. All the edge area SCs will be kept ON to avoid QoS degradation. Simulation analysis on the stochastic geometry model indicates that the proposed sleep strategy significantly improves the network EE than the prevailing sleep control strategies while assuring seamless, efficient coverage in the sleep cell areas.     

Pruned optical banyan networks on vertical stacking scheme for faster connection establishment

In this paper, we address the issue of faster connection establishment in a large vertically stacked optical Banyan (VSOB) network. The best known global routing algorithm, which turns an N × N crosstalk-free VSOB network into a rearrangeably non-blocking one, has time complexity O (NlogN). This is quite large compared to O (NlogN) time complexity of a single plane banyan network, which is a self-routing network with very high blocking probability. For a large size of switching network this O (NlogN) time complexity may result unacceptably long delay. Therefore, an optical network with very low blocking probability and O (NlogN) time complexity will be useful. Previously proposed Plane Fixed Routing (PFR) algorithm has O (logN) time complexity but results in higher than 2% blocking probability with zero-crosstalk constraint for a network as large as 4096 × 4096 at full load. In this paper, first we propose the pruning of VSOB networks that reduces the hardware cost by almost 30%. The networks can still use the PFR algorithm and results in the same blocking probability. However, we show that the blocking probability can be reduced dramatically while keeping the optimum time complexity O (logN) by allowing only a small amount of crosstalk. Then, we propose a new kind of switching networks in which extra regular banyan planes have been added with the pruned VBOS (P-VSOB) networks. Necessary routing algorithms, namely, PFR_RS and PFR_LS show that this new switching network can reduce the blocking probability to very low value even with zero-crosstalk constraint while keeping the hardware cost 3almost the same as for P-VSOB networks. Both these algorithms also have time complexity O (NlogN).     

基于休眠机理的三维小基站蜂窝网络能效优化

小基站通常部署在写字楼、商贸区等城市密集区域以弥补传统宏基站在覆盖和传输方面的不足.小基站的分布一般是根据高峰时的网络负荷设计的,这必然导致网络负荷较低时的资源浪费.讨论了在平均接入率和信道容量双重约束下基于休眠机理的三维小基站蜂窝网络的能效优化问题.借助泊松点过程理论推导了三维小基站网络下行信道容量和平均接入率的数学表达式.通过分析下行信道容量和平均接入率的单调性得出同时满足传输信道容量和接入率要求的最佳休眠概率.分析了小基站最大用户连接数的最佳值,通过对该参数的合理配置,可以在满足通信指标的前提下最大程度地降低网络能耗.仿真结果表明,设计的基站休眠机理可以使小基站网络的能耗下降约21%.     

Reducing Power Consumption in Core Wavelength Division Multiplexing Optical Networks

Abstract

This work proposes some heuristic criteria aiming at saving energy by efficiently switching off optical fibers in generalized multi-protocol label switching-controlled optical networks during low traffic load periods and by taking into account network equipment power consumption. Simulations show that up to 80% of energy can be saved when the traffic is reduced by 20% with respect to its maximum. The impact of network design parameters is also investigated.     

Low complexity optimization for user centric cellular networks via large dimensional analysis

Users near cell edges suffer from severe interference in traditional cellular networks. In this paper, we consider the scenario that multiple nearby base stations (BSs) cooperatively serve a group of users which is referred to as the cell free networks. A low complexity optimization method based on the large dimensional analysis is proposed. The advantage of the cell free networks is that the interference caused in the cell edge users can be converted into intended signal. It is not easy to obtain the optimal solution to the network due to coupled relations among the users’ rates. To obtain a suboptimal solution, a precoder that balances signal and interference is adopted to maximize the network capacity. In traditional optimization, it requires instantaneous channel state information. We try to optimize the network sum rate based on the large dimensional analysis. In this way, the optimization can be transformed into another problem that merely depend on the large scale channel statistics. Large dimensional analysis is leveraged to derive the asymptotic signal to interference plus noise ratio that only depends on large scale channel statistics. Based on this result, the power allocation problem does not need to adapt as frequently as the instantaneous channel state information. By this means, signal exchange overhead can be greatly reduced. Numerical results are provided to validate the efficacy of the proposed optimization method.     

Emergent criticality through adaptive information processing in boolean networks

We study information processing in populations of boolean networks with evolving connectivity and systematically explore the interplay between the learning capability, robustness, the network topology, and the task complexity. We solve a long-standing open question and find computationally that, for large system sizes N, adaptive information processing drives the networks to a critical connectivity K(c)=2. For finite size networks, the connectivity approaches the critical value with a power law of the system size N. We show that network learning and generalization are optimized near criticality, given that the task complexity and the amount of information provided surpass threshold values. Both random and evolved networks exhibit maximal topological diversity near K(c). We hypothesize that this diversity supports efficient exploration and robustness of solutions. Also reflected in our observation is that the variance of the fitness values is maximal in critical network populations. Finally, we discuss implications of our results for determining the optimal topology of adaptive dynamical networks that solve computational tasks.     

Offdiagonal complexity: A computationally quick complexity measure for graphs and networks

A vast variety of biological, social, and economical networks shows topologies drastically differing from random graphs; yet the quantitative characterization remains unsatisfactory from a conceptual point of view. Motivated from the discussion of small scale-free networks, a biased link distribution entropy is defined, which takes an extremum for a power-law distribution. This approach is extended to the node–node link cross-distribution, whose nondiagonal elements characterize the graph structure beyond link distribution, cluster coefficient and average path length. From here a simple (and computationally cheap) complexity measure can be defined. This offdiagonal complexity (OdC) is proposed as a novel measure to characterize the complexity of an undirected graph, or network. While both for regular lattices and fully connected networks OdC is zero, it takes a moderately low value for a random graph and shows high values for apparently complex structures as scale-free networks and hierarchical trees. The OdC approach is applied to the Helicobacter pylori protein interaction network and randomly rewired surrogates.     

Combining circuit and packet switching using a large port-count optical cross-connect for data center networks

The exponential increase in the data center network traffic has posed new challenges for achieving high throughput, low latency and energy consumption. To address these challenges, this paper presents a novel scheme that combines optical circuit and packet switching technologies together in a single optical cross-connect for data center networks. By utilizing fast tunable linecards and parallel array waveguide grating routers, the optical cross-connect can offer low latency, large scalability and high throughput in datacenter networks. A scheduling technique is designed to simultaneously accommodate circuit switching and packet switching in the optical cross-connect. The physical performance of this optical cross-connect in circuit and packet switching modes is investigated by experiments. In addition, the network performance of the optical cross-connect is evaluated through simulations under a mixed circuit/packet-switched traffic pattern. Results show that the network performance of the optical cross-connect does not degrade significantly with an increase in the number of racks, but it is affected by the circuit-switched traffic proportion.     

Power management in heterogeneous networks with energy harvesting base stations

In this paper, heterogeneous cellular networks (HCNs) with base stations (BSs) powered from both renewable energy sources and the grid power are considered. Based on a techno-economic analysis, we demonstrate that by controlling both transmit power and stored energy usage of BSs, energy costs can be effectively reduced. Specifically, we propose a two-stage BS operation scheme where an optimization and control subproblem is solved at each stage, respectively. For the first subproblem, transmit power of BSs is adjusted while quality of service (QoS) experienced by users is preserved. In the second subproblem, we consider the strategic scheduling of renewable energy used to power the BSs. That is, harvested energy may be reserved in the battery for future use to minimize the cost of on-grid power that varies in real-time. We propose: (1) an optimization approach built on a lattice model with a method to process outage rate constraint, and (2) a control algorithm based on nonlinear model predictive control (NMPC) theory to solve the two subproblems, respectively. Simulation results include a collection of case studies that demonstrate as to how operators may manage energy harvesting BSs to reduce their electricity costs.     

Multiple carrier frequency offsets and multipath channels estimation for coordinated multi-point FBMC systems

Filter bank multicarrier (FBMC) systems are active candidates for the physical layer of the fifth generation of wireless communication networks (5G). The combination of FBMC system with the coordinated multipoint (CoMP) has been proposed to improve the performance of mobile terminals (MT) located at the cell edge. Accurate estimation of carrier frequency offset (CFO) and channel has great importance in communication systems. In this paper, a data-aided joint maximum likelihood (ML) CFO and channel estimation is proposed for the downlink of CoMP. Notably choosing the proper training sequences from Hadamard matrix for different base stations (BSs) reduces the complexity of estimator. Additionally joint CFO correction and channel equalization has been studied at the MT. Finally, we evaluate the efficacy of the presented algorithms using simulation.     

5G/NGPON Evolution and Convergence: Developing on Spatial Multiplexing of Optical Fiber Links for 5G Infrastructures

ABSTRACT

The offering of demanding telecommunication services as promised by the 5G specifications raise the necessity for high capacity, flexible, adaptive, and power conserving fronthaul. Toward this goal, the role of the passive optical network which is responsible for interconnecting the central office (CO) with the cell-sites is crucial. Among the latest related technologies that need to be integrated in the context of the next generation passive optical networks (NGPONs), the most promising for increasing the provided bandwidth, is the optical spatial multiplexing. In this paper, we present the key 5G technologies, focusing on spatial division multiplexing, which constitutes the main innovation of the blueSPACE 5G Infrastructure Public Private Partnership (5G PPP) project. Exploiting the recent developments on multicore fibers (MCFs), optical beamforming networks (OBFNs), analog radio over fiber (ARoF), and spatial-spectral resources granularity in the context of Spectrally Spatially Flexible Optical Networks (SS-FONs), we describe a complete approach for the 5G fronthaul, emphasizing on the efficient allocation of optical resources while aiming at minimizing energy consumption. The modeled optimization problem is thoroughly presented, and the introduced scheme is evaluated through a real-world based simulation scenario, exhibiting quite promising results.     

An efficient scheme for optimizing channel utilization in OBS networks

Optical burst switching (OBS) is an emerging technology that allows variable size data bursts to be transported directly over DWDM links. In order to make OBS a viable solution, the wavelength scheduling algorithms need to be able to utilize the available wavelengths efficiently, while being able to operate fast enough to keep up with the burst incoming rate. Unfortunately, horizon scheduling cannot utilize the voids created by previously scheduled bursts, resulting in low bandwidth utilization. To date, Min-SV is the fastest scheduling algorithm that can schedule wavelengths efficiently. However, its complexity is O (log m) and it requires 10 log (m) memory accesses to schedule a single burst. This means that it can take upto several microseconds for each burst request, which is still too slow to make it a practical solution for OBS deployment. In this paper, an efficient scheme has been proposed for optimizing channel utilization in OBS networks. In the proposed approach, a burst is represented by an interval of time. The process of scheduling a number of bursts, thus, turns to be a process of fitting a set of the corresponding time intervals on a channel time line that represents a channel-time resource. By doing so, the scheduling process can be formulated as a combinatorial optimization problem. Then, graph theory is applied to schedule as many non-overlapping intervals as possible onto the channel time line. The underlying concept of the proposed scheduling scheme is that of briefly delaying the scheduling of a burst so that a much better decision can be made about a number of bursts all-together. This scheme is shown, through simulations, to improve performance in terms of burst loss probability, channel utilization, fairness-control and data throughput over existing schemes. Thus the proposed scheme is well suited for high performance networks in terms of reliability.     

Highly efficient upconversion emission and luminescence switching from Yb3+/Tm3+ co-doped water-free low silica calcium aluminosilicate glass

We present highly efficient 480 and 800 nm upconversion emissions in Tm³⁺/Yb³⁺ co-doped water-free low silica calcium aluminosilicate glass under excitation at 976 nm. As a result of this efficient upconversion process, a luminescent switching with the excitation intensity has been observed. The switching is explained and discussed using rate equations analysis and saturation effects. By means of fitting of the experimental data point, it was possible to obtain the value of the energy transfer parameter related to the transition ²F_5/2, ³H₄→²F_7/2, ¹G₄. The value of this parameter is higher than that of materials like YLF. This switching mechanism could be used in the development of sensors and networks for optical processing and optical communications.     

Game theory-based radio resource allocation in NOMA vehicular communication networks supported by UAV

Vehicular communication networks are emerging as a promising technology to provide high-quality internet service such as entertainment for road users via infrastructure-to-vehicle (I2V) communication, and to guarantee road users’ safety via vehicle-to-vehicle (V2V) communication. Some technical issues that impact the performance of these networks are the lack of or poor communication paths between vehicles, and the limitation of radio resources. Unmanned aerial vehicles (UAVs) as promising solutions for supporting vehicular networks could provide communication coverage in hazardous environments and areas with no capacities for installation or maintenance of ground base stations (BSs). Also, non-orthogonal multiple access (NOMA) methods can improve spectral and energy efficiency and thereby allow more users to be connected to the desired network. In this paper, exploring the NOMA, we develop a scheme for optimum resource allocation in presence of a UAV that supports vehicular communications. Resource allocation for this scenario is formulated as a mixed-integer non-linear programming (MINLP) problem. Due to the high complexity of such problems, we propose two low-complexity near-optimal methods. First, we apply difference-of-concave-functions (DC) approximations to solve the problem in an iterative process. Next, we use Stackelberg game-based method for efficient solving, and then, closed-form expressions of optimal power allocations using KKT-conditions are derived. Simulations illustrate the effectiveness of the proposed scheme along with the Stackelberg game-based method.     

Information theoretic description of networks

We present a new information theoretic approach for network characterizations. It is developed to describe the general type of networks with n nodes and L directed and weighted links, i.e., it also works for the simpler undirected and unweighted networks. The new information theoretic measures for network characterizations are based on a transmitter-receiver analogy of effluxes and influxes. Based on these measures, we classify networks as either complex or non-complex and as either democracy or dictatorship networks. Directed networks, in particular, are furthermore classified as either information spreading and information collecting networks.The complexity classification is based on the information theoretic network complexity measure medium articulation (MA). It is proven that special networks with a medium number of links (L∼n^1.5) show the theoretical maximum complexity . A network is complex if its MA is larger than the average MA of appropriately randomized networks: MA>MA_r. A network is of the democracy type if its redundancy R<R_r, otherwise it is a dictatorship network. In democracy networks all nodes are, on average, of similar importance, whereas in dictatorship networks some nodes play distinguished roles in network functioning. In other words, democracy networks are characterized by cycling of information (or mass, or energy), while in dictatorship networks there is a straight through-flow from sources to sinks. The classification of directed networks into information spreading and information collecting networks is based on the conditional entropies of the considered networks (H(A/B)=uncertainty of sender node if receiver node is known, H(B/A)=uncertainty of receiver node if sender node is known): if H(A/B)>H(B/A), it is an information collecting network, otherwise an information spreading network.Finally, different real networks (directed and undirected, weighted and unweighted) are classified according to our general scheme.     

Failure Impact,Availability and Cost Analysis of PONs Based on a Network Geometric Model

Passive Optical Networks(PONs)are considered as the preferred solution for broadband fibre-based access networks.This is because PONs present low cost deployment,low energy consumption and also meet high bandwidth demands from end users.In addition,end users expect a high availability for access networks,while operators are more concerned about reducing the failure impact(number of clients affected by failures).Moreover,operators are also interested in reducing the cost of the access network.This paper provides a deep insight into the consequences that the physical topology and design decisions cause on the availability,the failure impact and the cost of a PON.In order to do that,the physical layout of the PON deployment area is approximated by a network geometric model.A PON deployed according to the geometric model is then assessed in terms of failure impact,availability and cost.This way,the effects of different design decisions and the physical layout on these three parameters are evaluated.In addition,the tradeoffs between availability,failure impact and cost caused by planning decisions and the physical topology are identified and pinpointed.     

Self organized energy efficient position aided relays in LTEA

Energy is an expensive commodity in Long term Evolution-Advanced networks (LTEA). In recent years many researchers have proposed different approaches to minimize energy consumption in LTEA. Relays and the Self Organized Network (SON) technologies are an attractive solution to cost reduction, and to minimize energy utilization in LTEA. SON has the potential for self-configuring, self-optimizing, self-healing and minimizing the energy consumption in the network and by deploying Relay Nodes (RNs) it enhances the capacity, the diversity and fairness in the network. In this paper we consider the self-optimizing and self-healing properties of SON and propose a novel energy efficient SON based position aided relay algorithm. The proposed algorithm is capable of reducing the energy consumption by 50% in the best case scenario. A System Level Simulator (SLS) is used to evaluate the performance of the proposed algorithm through numerical results.     

An optimal burst assembly approach employing traffic shaping (OBATS) for OBS

Optical burst switching (OBS) aims at combining the strengths of packet and circuit switching and is considered as a promising technology for implementing the next generation optical Internet, required to cope with the rapid growth of Internet traffic and the increased deployment of new services. In this paper, an optimal burst assembly approach employing traffic shaping (OBATS) for OBS networks has been proposed in order to improve network performance in terms of reduced blocking probability, congestion control and better utilization of bandwidth. Particularly, the proposed scheme aims at reducing the average delay experienced by the packets during the burstification process in optical burst switched (OBS) networks, for a given average size of the bursts produced. Reducing the packet burstification delay, for a given average burst size, is essential for real-time applications; correspondingly, increasing the average burst size for a given packet burstification delay is important for reducing the number of bursts injected into the network and the associated overhead imposed on the core nodes. Simulation results show that the proposed burst assembly approach gives better network performance in terms of burst drop, resource contention and delay as compared to conventional burst assembly approaches.