Unmanned aerial vehicle (UAV)-assisted systems have attracted a lot of attention due to its high probability of line-of-sight (LoS) connections and flexible deployment. In this paper, we aim to minimize the upload time required for the UAV to collect information from the sensor nodes in disaster scenario, while optimizing the deployment position of UAV. In order to get the deployment solution quickly, a data-driven approach is proposed in which an optimization strategy acts as the expert. Considering that images could capture the spatial configurations well, we use a convolutional neural network (CNN) to learn how to place the UAV. In the end, the simulation results demonstrate the effectiveness and generalization of the proposed method. After training, our CNN can generate UAV configuration faster than the general optimization-based algorithm.

In this paper, we investigate optimal design of millimeter-wave (mmWave) multiuser line-of-sight multiple-input-multiple-output (LOS MIMO) systems using hybrid arrays of subarrays based on hybrid block diagonalization (BD) precoding and combining scheme. By introducing a general 3D geometric channel model, the optimal subarray separation products of the transmitter and receiver for maximizing sum-rate is designed in terms of two regular configurations of adjacent subarrays and interleaved subarrays for different users, respectively. We analyze the sensitivity of the optimal design parameters on performance in terms of a deviation factor, and derive expressions for the eigenvalues of the multiuser equivalent LOS MIMO channel matrix, which are also valid for non-optimal design. Simulation results show that the interleaved subarrays can support longer distance communication than the adjacent subarrays given the appropriate fixed subarray deployment.

This paper introduces heuristic approaches and a deep reinforcement learning approach to solve a joint virtual network function deployment and scheduling problem in a dynamic scenario. We formulate the problem as an optimization problem. Based on the mathematical description of the optimization problem, we introduce three heuristic approaches and a deep reinforcement learning approach to solve the problem. We define an objective to maximize the ratio of delay-satisfied requests while minimizing the average resource cost for a dynamic scenario. Our introduced two greedy approaches are named finish time greedy and computational resource greedy, respectively. In the finish time greedy approach, we make each request be finished as soon as possible despite its resource cost; in the computational resource greedy approach, we make each request occupy as few resources as possible despite its finish time. Our introduced simulated annealing approach generates feasible solutions randomly and converges to an approximate solution. In our learning-based approach, neural networks are trained to make decisions. We use a simulated environment to evaluate the performances of our introduced approaches. Numerical results show that the introduced deep reinforcement learning approach has the best performance in terms of benefit in our examined cases.

5G service has been launched in various countries, and research for the beyond 5G is already underway actively around the world. In beyond 5G, it is expected to expand the various capabilities of communication technologies to cover further wide use cases from 5G. As a candidate elemental technology, cell free massive MIMO has been widely researched and shown its potential to enhance the capabilities from various aspects. However, for deploying this technology in reality, there are still many technical issues such as a cost of distributing antenna and installing fronthaul, and also the scalability aspects. This paper surveys research trends of cell free massive MIMO, especially focusing on the deployment challenges with an introduction to our specific related research activities including some numerical examples.

Network Slicing (NS) is recognized as a key technology for the 5G network in providing tailored network services towards various types of verticals over a shared physical infrastructure. It offers the flexibility of on-demand provisioning of diverse services based on tenants' requirements in a dynamic environment. In this work, we focus on two important issues related to 5G Core slices: the deployment and the reconfiguration of 5G Core NSs. Firstly, for slice deployment, balancing the workloads of the underlying network is beneficial in mitigating resource fragmentation for accommodating the future unknown network slice requests. In this vein, we formulate a load-balancing oriented 5G Core NS deployment problem through an Integer Linear Program (ILP) formulation. Further, for slice reconfiguration, we propose a reactive strategy to accommodate a rejected NS request by reorganizing the already-deployed NSs. Typically, the NS deployment algorithm is reutilized with slacked physical resources to find out the congested part of the network, due to which the NS is rejected. Then, these congested physical nodes and links are reconfigured by migrating virtual network functions and virtual links, to re-balance the utilization of the whole physical network. To evaluate the performance of deployment and reconfiguration algorithms we proposed, extensive simulations have been conducted. The results show that our deployment algorithm performs better in resource balancing, hence achieves higher acceptance ratio by comparing to existing works. Moreover, our reconfiguration algorithm improves resource utilization by accommodating more NSs in a dynamic environment.

To solve the problem of the self-deployment of heterogeneous directional wireless sensor networks in 3D space, this paper proposes a weighted Voronoi diagram-based self-deployment algorithm (3DV-HDDA) in 3D space. To improve the network coverage ratio of the monitoring area, the 3DV-HDDA algorithm uses the weighted Voronoi diagram to move the sensor nodes and introduces virtual boundary torque to rotate the sensor nodes, so that the sensor nodes can reach the optimal position. This work also includes an improvement algorithm (3DV-HDDA-I) based on the positions of the centralized sensor nodes. The difference between the 3DV-HDDA and the 3DV-HDDA-I algorithms is that in the latter the movement of the node is determined by both the weighted Voronoi graph and virtual force. Simulations show that compared to the virtual force algorithm and the unweighted Voronoi graph-based algorithm, the 3DV-HDDA and 3DV-HDDA-I algorithms effectively improve the network coverage ratio of the monitoring area. Compared to the virtual force algorithm, the 3DV-HDDA algorithm increases the coverage from 75.93% to 91.46% while the 3DV-HDDA-I algorithm increases coverage from 76.27% to 91.31%. When compared to the unweighted Voronoi graph-based algorithm, the 3DV-HDDA algorithm improves the coverage from 80.19% to 91.46% while the 3DV-HDDA-I algorithm improves the coverage from 72.25% to 91.31%. Further, the energy consumption of the proposed algorithms after 60 iterations is smaller than the energy consumption using a virtual force algorithm. Experimental results demonstrate the accuracy and effectiveness of the 3DV-HDDA and the 3DV-HDDA-I algorithms.

Full-duplex access points (APs) deployment can significantly affect network performance of a wireless local area network (WLAN). Unlike in traditional half-duplex networks, location of a full-duplex AP will affect network coverage quality as well as full-duplex transmission opportunities. However, the effect of full-duplex AP deployment on network performance and the differences between half- and full-duplex AP deployment have not been well investigated yet. In this paper, we first theoretically analyze the effect of full-duplex AP deployment on WLAN throughput. Exact full-duplex transmission probability is derived in presence of Rayleigh fading with different AP locations. Our analysis reveal that a good AP deployment profile can exploit more full-duplex transmission opportunities and greatly improve network performance. The full-duplex AP deployment problem is then formulated as an integer linear programming (ILP) problem in which our objective is to obtain optimized network throughput. Then we develop a heuristic algorithm to solve the formulated problem and optimal deployment profile can be produced. Simulation results validate that the WLAN throughput as well as full-duplex transmission opportunities can be significantly improved by our generated full-duplex AP deployment profile.

Network function virtualization improves the flexibility of infrastructure resource allocation but the application of commodity facilities arouses new challenges for systematic reliability. To meet the carrier-class reliability demanded from the 5G mobile core, several studies have tackled backup schemes for the virtual network function deployment. However, the existing backup schemes usually sacrifice the efficiency of resource allocation and prevent the sharing of infrastructure resources. To solve the dilemma of balancing the high level demands of reliability and resource allocation in mobile networks, this paper proposes an approach for the problem of pooling deployment of virtualized network functions in virtual EPC network. First, taking pooling of VNFs into account, we design a virtual network topology for virtual EPC. Second, a node-splitting algorithm is proposed to make best use of substrate network resources. Finally, we realize the dynamic adjustment of pooling across different domains. Compared to the conventional virtual topology design and mapping method (JTDM), this approach can achieve fine-grained management and overall scheduling of node resources; guarantee systematic reliability and optimize global view of network. It is proven by a network topology instance provided by SNDlib that the approach can reduce total resource cost of the virtual network and increase the ratio of request acceptance while satisfy the high-demand reliability of the system.

An optical transport network is composed of optical transport systems deployed in thousands of office-buildings. As a common infrastructure to accommodate diversified communication services with drastic traffic growth, it is necessary not only to continuously convey the growing traffic but also to achieve high end-to-end communication quality and availability and provide flexible controllability in cooperation with service layer networks. To achieve high-speed and large-capacity transport systems cost-effectively, system configuration, applied devices, and the manufacturing process have recently begun to change, and the cause of failure or performance degradation has become more complex and diversified. The drastic traffic growth and pattern change of service networks increase the frequency and scale of transport-capacity increase and transport-network reconfiguration in cooperation with service networks. Therefore, drastic traffic growth affects both optical-transport-system configuration and its operational cycles. In this paper, we give an overview of the operational problems emerging in current nationwide optical transport networks, and based on trends analysis for system configuration and network-control schemes, we propose a vision of the future nationwide optical-transport-network architecture expressed using five target features.

A joint deployment of base stations (BSs) and RGB-depth (RGB-D) cameras for camera-assisted millimeter-wave (mmWave) access networks is discussed in this paper. For the deployment of a wide variety of devices in heterogeneous networks, it is crucial to consider the synergistic effects among the different types of nodes. A synergy between mmWave networks and cameras reduces the power consumption of mmWave BSs through sleep control. A purpose of this work is to optimize the number of nodes of each type, to maximize the average achievable rate within the constraint of a predefined total power budget. A stochastic deployment problem is formulated as a submodular optimization problem, by assuming that the deployment of BSs and cameras forms two independent Poisson point processes. An approximate algorithm is presented to solve the deployment problem, and it is proved that a (1-e-1)/2-approximate solution can be obtained for submodular optimization, using a modified greedy algorithm. The numerical results reveal the deployment conditions under which the average achievable rate of the camera-assisted mmWave system is higher than that of a conventional system that does not employ RGB-D cameras.

In order to tackle rapidly increasing traffic, dramatic performance enhancements in radio access technologies (RATs) are required for fifth-generation (5G) mobile communication system. In 5G, small/semi-macro cells using Massive MIMO (M-MIMO) with much wider bandwidth in higher frequency bands are overlaid on macro cell with existing frequency band. Moreover, high density deployment of small/semi-macro cell is expected to improve areal capacity. However, in low SHF band (below 6GHz), antenna array size of M-MIMO is large so that it cannot be installed on some environments. Therefore, to improve system throughput on various use cases in 5G, we have proposed distributed Massive MIMO (DM-MIMO). DM-MIMO coordinates lots of distributed transmission points (TPs) that are located in ultra-high density (UHD). Furthermore, DM-MIMO uses various numbers of antenna elements for each TP. In addition, DM-MIMO with UHD-TPs can create user-centric virtual cells corresponding to user mobility, and design of flexible antenna deployment for DM-MIMO is applicable to various use cases. Then, some key parameters such as the number of the distributed TPs, the number of antenna elements for each TP, and proper distance between TPs, should be determined. This paper presents such parameters for 5G DM-MIMO with flexible antenna deployment under fixed total transmission power and constant total number of antenna elements. Computer simulations show that DM-MIMO can achieve more than 1.9 times higher system throughput than an M-MIMO system using 128 antenna elements.

Named Data Networking (NDN) has emerged as an alternative to traditional IP-based networking for the achievement of Information-Centric Networking (ICN). Currently, most NDN is deployed over IP networks, but such an overlay deployment increases the transport network overhead due to the use of dual network control planes (NDN routing and IP routing). Software-Defined Networking (SDN) can be used to mitigate the network overhead by forwarding NDN packets without the use of IP routing. However, to deploy NDN over SDN, a variable NDN content name needs to be mapped to a fixed-size match field in an OpenFlow switch flow table. For efficient support of such a mapping task, we propose a new architecture that uses dual name for content: content name and Name Tag. The Name Tag is derived from the corresponding content name and is a legitimate IPv6 address. By using the proposed Name Tag, the SDN with an NDN control application can transport an IPv6 packet that encapsulates an NDN packet for an NDN name-based routing. We emulate the proposed architecture using Mininet and verify that it is feasible.

Sensor deployment to achieve better system performance is one of the critical issues in wireless sensor networks (WSN). This letter proposes an effective sensor deployment scheme for large area sensor networks, where the event occurrence rate varies over the sensor-deployed region. Based on local event occurrence rate, the proposed scheme determines the number of sensors that should be deployed in each local region to maximize the overall detection probability. Simulation results show that the sensor deployment by the proposed scheme improves detection capability by 21% in comparison to the Incidence algorithm.

In disaster sites of 2011 Tohoku Earthquake, digital communication was virtually unavailable due to the serious damage to the existing Internet and ICT resources. Thus there were urgent demands for recovering the Internet connectivity and first aid communication tools. This paper describes the design and deployment of networking systems that provide Internet connectivity using 3G mobile links or VSAT satellite links. In this paper we examine two approaches for post-disaster networking: quickly deployable package and on-demand networking. Based on a comparison of their characteristics and deployment experiences, this paper tries to extract lessons that contribute to improving the preparedness to another disaster. This paper also shares our significant operational experience acquired through supporting a maximum of 54 sites in Tohoku area including evacuation shelters, temporary hospitals and local government offices.

For the deployment of sensor networks, the sensor localization, which finds the position of sensor nodes, is very important. Most previous localization schemes generally use the GPS signal for the sensor localization. However, the GPS signal is unavailable when there is an obstacle between the sensor nodes and satellites. Therefore, in this paper, we propose a new localization scheme which does not use the GPS signal. The proposed scheme localizes the sensors by using three mobile anchors. Because the three mobile anchors collaboratively move by themselves, it is self-localizable and can be adopted even when the sensors are randomly and sparsely deployed in the target field.

In practical settings of wireless sensor networks, it is often feasible to consider heterogeneous deployments of devices with different capabilities. Under prescribed cost constraints, we analyze such heterogenous deployments and present how they impact the coverage of a sensor network including spatial correlation effect. We derive expressions for the heterogeneous mixture of devices that maximizes the lifetime coverage in both single-hop direct and multi-hop communication models. Our results show that using an optimal mixture of many inexpensive low-capability devices and some expensive high-capability devices can significantly extend the duration of a network's sensing performance, especially in a network with low spatial correlation.

Random scattering of sensors may cause some location not to be covered. In such a case, it is useful to make use of mobile sensors that can move to eliminate the coverage holes. Wang et al [1]. proposed self-deployment schemes of mobile sensors by using Voronoi polygon. However, some coverage holes still remain after the execution of the schemes. We propose a new self-deployment scheme using the centroid (geometric center) of each sensor's Voronoi polygon as the moving target position. The performance evaluation shows that the proposed scheme achieves better results than the existing schemes in terms of fast coverage expansion.

Cognitive radio has emerged as an efficient approach to reusing the licensed spectrums. How to appropriately set parameters of secondary user (SU) plays a rather important role in constructing cognitive radio networks. In this letter, we have analyzed the theoretical value of SUs' density, which provides a standard for controlling the number of SUs around one primary receiver, in order to guarantee that primary communication links do not experience excessive interference. The simulation result of secondary density well matches with the theoretical result derived from our analysis. Additionally, the achievable rate of secondary user under density control is also analyzed and simulated.

Motivated by the needs of modern agriculture, in this paper we present CropNET, a wireless multimedia sensor network system for agriculture monitoring. Both hardware and software designs of CropNET are tailored for sensing in wide farmland without human supervision. We have carried out multiple rounds of deployments. The evaluation results show that CropNET performs well and facilitates modern agriculture.

This letter proposes a novel mobile sensor deployment scheme for maximizing coverage. The basic idea is to force mobile sensors to move to predetermined target points that are the optimal layout in a distributed manner using Voronoi diagram data structure. A simulation shows that the result of the proposed scheme is quite close to the optimal result and outperforms previous works.