Narrowband Internet of Things (NB-IoT) offers three coverage enhancement (CE) levels to serve massive machines in a large area. For each CE level, the base station configures control periods to determine the number of allocatable radio resources for signal and data transmissions. Both uplink (UL) and downlink (DL) communications use the same control periods, but UL and DL machines need different period lengths. In general, a control period suitable for the UL is longer than that for the DL. Then, the base station assigns UL and DL resources in control periods to each machine. To this end, we study how to choose a suitable length of control periods and allot radio resources to UL and DL machines to minimize resource consumption, thereby improving NB-IoT performance. Two efficient algorithms are thus proposed. Based on the CE level, the control period adaptation algorithm flexibly adjusts control periods using a scale factor. The joint UL and DL resource allocation algorithm distributes radio resources in each control period among machines to increase utilization. Simulation results demonstrate that our algorithms can efficiently decrease the consumption of UL and DL subframes, especially for machines with bad channel qualities.

Narrowband Internet of Things (NB-IoT) is a critical technology for fifth-generation (5G) and next sixth-generation (6G) networks to meet the high density and latency requirements for massive devices. Before data transmission or reception in NB-IoT networks, a device needs to monitor a search space in a narrowband physical downlink control channel (NPDCCH). When the NPDCCH resource is exhausted, neither data transmission in the narrowband uplink shared channel (NPUSCH) nor data reception in the narrowed downlink shared channel (NPDSCH) is possible, even if the two channels still have available radio resources. To solve the problem, the 3rd generation partnership project (3GPP) puts forth an NPDCCH offset mechanism for NB-IoT, which can set different offset values for different devices. Devices using different NPDCCH offset values can postpone different time intervals for monitoring their NPDCCH search spaces so that the devices do not need to contend for the same NPDCCH search space. However, this article finds that improper use of the NPDCCH offset mechanism will hurt the system’s performance. Therefore, this article investigates how to use the NPDCCH offset mechanism in uplink and downlink resource allocation. The objective is to minimize the consumption of uplink and downlink radio resources while each device can transmit and/or receive its data. We propose an offset-aware resource allocation algorithm to solve the problem. Compared with two resource allocation algorithms with and without using the NPDCCH offset, the simulation results provide several insights and show that the proposed algorithm effectively reduces radio resource consumption.

The third-generation partnership project (3GPP) has defined a new radio access network protocol, called the narrowband Internet of Things (NB-IoT). To accommodate devices with diverse signal qualities, the base station can flexibly adjust the lengths of the two important radio resources, the narrowband physical downlink control channel (NPDCCH) and narrowband physical downlink-shared channel (NPDSCH), to serve the devices under one coverage enhancement level. The interval between two consecutive NPDCCH is called an NPDCCH period. Given the NPDCCH period, the base station should allocate the two radio resources for the devices to receive downlink data. The NPDCCH period length and resource allocation significantly affect the radio resource utilization of NB-IoT networks. In this article, we investigate the NPDCCH period adaptation and the NB-IoT scheduling problem over NB-IoT networks. The objective is to minimize the consumed radio resource. We prove that the scheduling problem is NP-hard and cannot be approximated with a ratio better than 3/2. Then, we propose two algorithms based on our observations to solve the target problems and show that the proposed scheduling algorithm is a 2-approximation algorithm. The simulation results evaluate the efficacy of the proposed algorithms and provide useful insights into the NPDCCH period adaptation and scheduling design for NB-IoT networks.

Introducing mobile small cells to next generation cellular networks is nowadays a pervasive and cost-effective way to fulfill the ever-increasing mobile broadband traffic. Being agile and resilient, unmanned aerial vehicles (UAVs) mounting small cells are deemed emerging platforms for the provision of wireless services. As the residual battery capacity available to UAVs determines the lifetime of an airborne network, it is essential to account for the energy expenditure on various flying actions in a flight plan. The focus of this paper is therefore on studying the 3D deployment problem for a swarm of UAVs, with the goal of maximizing the total amount of data transmitted by UAVs. In particular, we address an interesting trade-off among flight altitude, energy expense and travel time. We formulate the problem as a non-convex non-linear optimization problem and propose an energy-aware 3D deployment algorithm to resolve it with the aid of Lagrangian dual relaxation, interior-point and subgradient projection methods. Afterwards, we prove the optimality of a special case derived from the convexification transformation. We then conduct a series of simulations to evaluate the performance of our proposed algorithm. Simulation results manifest that our proposed algorithm can benefit from the proper treatment of the trade-off.

Densely deploying small cells will be a solution to provide explosive data requirements in fifth-generation networks. Because users often cluster together in popular locations of an urban area, congested small cells in the popular sites are also gathered. Traditional load balancing schemes generally only consider neighboring cells when offloading users are not suitable for clusters of overloaded cells. This paper considers groups of overloaded cells in the load balancing problem. The objective is to maximize the quality-of-service satisfaction ratio. To solve the problem, we propose a multidepth offloading algorithm with the consideration of the radio resource allocation. The proposed multidepth offloading algorithm can be applied no matter that congested small cells are gathered or not. Compared with a previous offloading algorithm and a baseline, the simulation results show that our proposed algorithm can increase 16% QoS satisfaction ratio in a real user distribution and 13% QoS satisfaction ratio in a clustered user placement.

One viable and cost-effective method to fulfill the ever-increasing mobile broadband traffic and to achieve coverage and capacity improvement is the employment of mobile small cells in next generation cellular networks. Being agile and resilient, aerial small cells (ASCs), which are small cells mounted on unmanned aerial vehicles (UAVs), are deemed promising platforms for the provision of wireless services. Since the lifetime of an airborne network highly depends on the residual battery capacity available to each ASC, it is essential to account for the energy expenditure on various flying actions in a flight plan. Therefore, the focus of this paper is to study the 3D deployment problem for a swarm of ASCs, in which a trade-off among flight altitudes, energy expenses and available lifetimes is observed. The objective is to maximize the total throughput of all users. We formulate the problem as a non-convex non-linear optimization problem and propose an energy-aware 3D deployment algorithm to resolve it with the aid of Lagrangian dual relaxation, interior-point and subgradient projection methods. We then conduct a series of simulations to evaluate the performance of our proposed algorithm. Simulation results manifest that our proposed algorithm can bring tremendous increase in the total throughput for all users by properly coping with the trade-off, compared to the two user-aware approaches with random and minimum altitude assignments.

Recently, the third generation partnership project (3GPP) has specified a new radio access network protocol, called narrowband IoT (NB-IoT) which is a low-power wide-area IoT network. In NB-IoT systems, the number of connected IoT devices will be explosively increased and how to efficiently use radio resources of NB-IoT systems to achieve massive connections is an important issue. However, since the downlink radio access strategy designed for NB-IoT systems needs to be performed in one dimension (i.e., time domain), we observe that inappropriate scheduling decisions will result in radio resource waste. In this paper, we introduce the NB-IoT radio access strategy in detail and study the NB-IoT scheduling problem. The objective is to minimize the used radio resource while each device''s data requirement can be satisfied. Then, we formulate the NB-IoT scheduling problem and propose an efficient algorithm. The simulation results evaluate the efficacy of the proposed algorithm and provide useful insights into the scheduling design for NB-IoT systems.

Software-defined networking (SDN) paradigm and high-speed wireless technologies have been widely adopted by industries to improve the performance of data center networks. In SDN, a switch commonly uses ternary content addressable memory (TCAM) to store rules for fast flow table lookup. However, due to the limited TCAM entries, rule replacement will occur frequently when traffic load in data centers is heavy. This issue will be more serious when flow characteristics in data centers are diverse. Although wireless transmissions can benefit for reducing TCAM usage, we observe that routing and forwarding designs without considering the flow characteristics will result in rule replacement storm in switches and significantly harm the SDN scalability for wireless data centers. As a result, switches will issue massive flow setup requests which cannot be processed by an SDN controller in real time. This paper studies the SDN scalability problem with the objective of minimizing the number of flow setup requests under the hybrid architecture with wired and wireless links. We propose a routing and forwarding algorithm with the consideration of flow characteristics to tackle the problem. A series of simulation results agrees our observations and shows that our algorithm outperforms two well-known solutions for wired data centers and three approaches for wireless data centers. The results also provide useful insights and guidelines for designing SDN-based wireless data centers.

With the advance of wireless technologies, the rapid mobile data traffic growth will lead to severe network resource consumption and exceptionally long latency to access services, especially for cloud-based applications. To tackle these issues, Fog Radio Access Network (F-RAN) is recently emerged for next generation cellular networks. F-RAN is considered as an extension of the cloud computing paradigm to the edge of the network and a highly virtualized platform that provides computing, storage, and network services for mobile devices. However, how to appropriately place virtual machines (VMs) into fog nodes in F-RAN systems is very challenging, and will significantly affect the bandwidth consumption of backhaul links. Thus this paper studies the replication-based VM placement problem, and aims at minimizing the total backhaul traffic generated by VM migrations and data transmissions. We observe that the VM placement operation should not be frequently executed and practically considers the problem in a long term aspect. Then we propose a heuristic algorithm to solve the problem. The simulation results agree our observation and show that compared with a greedy approach and an optimal algorithm, the proposed algorithm demonstrates with favorable results for the overall backhaul network usage.

The software-defined networking (SDN) architecture has been widely deployed in data centers to support diverse cloud services. In order to provide fast access for flow table lookup, SDN switches usually use ternary content addressable memory (TCAM) to store their routing rules given by a controller. However, the number of TCAM entries is relatively small due to its high cost and high energy consumption. As traffic in data centers significantly increases, rule replacement will occur very frequently in SDN switches. As a result, massive flow setup requests are generated and cannot be processed by the controller in real time. To address the scalability issue, we propose a novel rule-reduction-based policy to minimize TCAM usage, thus alleviating the burden on the SDN controller. Specifically, we integrate the 60 GHz wireless technology into data centers and formulate a routing rule minimization problem under the hybrid architecture with wired and wireless links. The objective is to minimize the number of installed rules in switches. We then propose an efficient algorithm, with the consideration of TCAM limitations and flow sizes, to solve the problem. Results of a series of simulations show that our algorithm outperforms a well-known solution for wired data centers and two approaches for wireless data centers. The results also provide useful insights and guidelines for designing scalable SDN-based data centers.

Joint transmission (JT), an attractive coordinated multipoint (CoMP) downlink transmission technique, is used to increase cell-edge user throughput, but has to distribute the same user data to multiple base stations termed a JT cluster, thus leading to a huge burden on backhaul bandwidth. Therefore, recent studies considered caching user data at the base stations to reduce the backhaul bandwidth consumption. However, we observe that even with the caches at the base stations, JT clustering decisions, without considering the data caching, cannot efficiently use the data in the caches and will result in unnecessary backhaul data traffic. In this paper, we study the joint transmission clustering problem with the consideration of data caches at the base stations. The objective is to minimize the backhaul data traffic while each user's data rate requirement can be satisfied under the limited radio resource blocks. Then, considering the cache effect, we formulate the target problem and propose an algorithm to solve this problem. The simulation results agree our observation and show that compared with a JT clustering algorithm, our proposed algorithm can significantly reduce more backhaul data traffic.

With the increasing demand for ultra-low latency services in 5G cellular networks, fog with edge computing is one of promising solutions which migrate the computing from the cloud to the edge of the network. Rather than relying on the distant cloud or additional servers, we propose the Fog-Radio Access Network (F-RAN), which leverages the current infrastructures in the radio access network, such as small cells and macro base stations, to pursue the ultra-low latency by joint powerful computing of multiple F-RAN nodes and near-range communications at the edge. The optimization problem is firstly formulated to tackle the tradeoff between communication and computing resources into time domain within distributed computing scenario, and then we propose a cooperative task computing operation algorithm to simultaneously decide how many F-RAN nodes should be selected with proper communication resource allocation and computing task assignment. The numerical results show that the ultra low-latency services can be achieved by F-RAN via cooperative task computing.

Mobile devices have increasingly been used to run multimedia applications which are extremely downlink-intensive. The conventional rate adaptive and/or margin adaptive approach for radio resource allocation may result in unnecessary energy consumption on mobile devices, which will not be energy efficient for mobile multimedia applications. In this paper, we develop an energy adaptive approach and design an energy-efficient downlink resource allocation scheme to support multimedia applications. The objective is to minimize the total energy consumption of mobile devices for data reception while meeting the data rate requirements at mobile devices and the transmit power constraint at the base station. We show that the optimization problem is NP-hard and then propose an efficient algorithm that has a provable performance guarantee under a certain condition. We have conducted extensive simulations to evaluate the efficacy of the proposed algorithm and our results provide useful insights into the design of energy-efficient resource allocation for wireless systems.

With the rapid growth of mobile broadband traffic, adopting small cell is a promising trend for operators to improve network capacity with low cost. However, static small cells cannot be flexibly placed to fulfill time/space-varying traffic. The static small cells might stay in idle or under-utilized mode during some time periods, which wastes resources. Therefore, this paper utilizes the mobile small cell concept and studies the deployment problem for mobile small cells. The objective is to maximize the service time provided by mobile small cells for all users. If a finite number of mobile small cells can serve more users for more time, the mobile small cell deployment will have more gains. Specifically, we show an interesting trade-off in the service time maximization. Then, we prove our target problem is NP-hard and propose an efficient mobile small cell deployment algorithm to deal with the trade-off to maximize the total service time. We construct a series of simulations with realistic parameter settings to evaluate the performance of our proposed algorithm. Compared with a static small cell deployment algorithm and a random mobile small cell deployment algorithm, the simulation results show that our proposed scheme can significantly increase the total service time provided for all users.

With the rapid growth of mobile data traffic, operators are expected to densely deploy base stations to meet user demands. Recent researches have indicated that the densely deployed base stations lead to the significant increase of the operational expenses of operators due to the electricity bills to maintain their operation, and thus the profit of operators is greatly decreased. Different from the past works in dynamically switching on/off base stations for energy saving, we propose to consider the benefits of users in service fee discounts as a joint optimization process in cutting down the energy consumption of base stations to maximize the total profit of operators. The optimization problem is formulated and shown being NP-hard. We then propose a profit-aware algorithm to switch off base stations, as needed, with the adjustment of the data rates provided to the users who are willing to receive discounts. The simulation results show that the proposed algorithm can significantly increase the total profit of operators and introduce a win-win situation to both users and operators.

Recently, large-scale data centers are widely built to support various kinds of cloud services, which are mostly delivered by multicast. Even with multicast, cloud services may still generate a large amount of data traffic in some bottleneck links and, even worse, cause network congestion. Thus, how to reduce the redundancy of data transmissions to mitigate network congestion is essential. In addition to wired transmissions, modern data centers adopt wireless links to augment network capacity. Under the coexisting scenario of wired and wireless links, this paper studies multicast data delivery problem. Specifically, a multicast tree problem is defined, and the objective is to minimize the total multicast data traffic. We prove the problem is NP-hard and propose an efficient heuristic algorithm to solve the problem. A series of experiment results shows that our proposed algorithm is very effective, compared with an optimal solution designed for traditional wired data centers.

Given the explosive growth of mobile subscribers, network operators have to densely deploy base stations to serve the exponentially increasing access demands. Nevertheless, recent researches have pointed out that base station operation has been identified as a significant portion of total system energy consumption and 90% of the traffic is carried by only 40% of base stations even under peak traffic demand. Therefore, switching off underutilized base stations for saving power is an important issue with the increasing awareness of environmental responsibility and economical concerns of network operators. This paper targets the problem of dynamic base station operation, with an objective to minimize total power consumption of all base stations. We prove this problem is NP-hard and cannot be approximated in polynomial time with a ratio better than 3 over 2. Then, we propose a distributed algorithm to tackle it. The simulation results show that our proposed algorithm can significantly reduce the network power consumption.

Layer-based video coding, together with adaptive modulation and coding, is a promising technique for providing real-time video multicast services on heterogeneous mobile devices. With the rapid growth of data communications for emerging applications, reducing the energy consumption of mobile devices is a major challenge. This paper addresses the problem of resource allocation for video multicast in fourth-generation wireless systems, with the objective of minimizing the total energy consumption for data reception. First, we consider the problem when scalable video coding is applied. We prove that the problem is {\cal NP}-hard and propose a 2--approximation algorithm to solve it. Then, we investigate the problem under multiple description coding, and show that it is also {\cal NP}--hard and cannot be approximated in polynomial time with a ratio better than 2, unless {\cal P}={\cal NP}. To solve this case, we develop a pseudopolynomial time 2-approximation algorithm. The results of simulations conducted to compare the proposed algorithms with a brute-force optimal algorithm and a conventional approach are very encouraging.

Scalable video coding with adaptive modulation and coding is a promising technique to provide real-time multicast services for heterogeneous mobile devices. Nevertheless, as the rapid growth of data communication for emerging applications, energy consumption is an critical challenge of mobile devices. This paper targets the problem of resource allocation for scalable video multicast with adaptive modulation-coding schemes in next generation cellular wireless networks, with an objective to minimize the total energy consumption of all mobile devices for reception. We show the NP-hardness of the target problem and propose a 2-approximation algorithm. Extensive simulations are conducted to compare the proposed algorithm with a brute-force optimal algorithm and a conventional approach, which provides some useful insights into power-aware scalable video multicast in 4G wireless systems.