In millimeter wave heterogeneous networks with integrated access and backhaul (mABHetNets), a considerable part of spectrum resources are occupied by the backhaul link, which limits the performance of the access link. In order to overcome such backhaul “ spectrum occupancy ”, we introduce cache into mABHetNets. Caching popular files at small base stations (SBSs) can offload the backhaul traffic and transfer spectrum from the backhaul link to the access link. To approach the optimal performance of cache-enabled mABHetNets, we first analyze the signal-to-interference-plus-noise ratio distribution and derive the average potential throughput (APT) expression by stochastic geometric tools. Then, based on our analytical work, we formulate a joint optimization problem of cache decision and spectrum partition to maximize the APT. Inspired by the block coordinate descent (BCD) method, we propose a joint cache decision, spectrum partition and power allocation (JCSPA) algorithm to approach the optimal solution. Numerical results show the effectiveness and enhancement of the proposed algorithm. Besides, we verify the APT under different parameters and find that the introduction of cache facilitates the transfer of backhaul spectrum to access link. Jointly deploying appropriate caching capacity at SBSs and performing specified spectrum partition can bring up about 90% APT gain in mABHetNets.

In tactical networks, link fluctuations and node movements make operation management a difficult problem. Software-defined networking (SDN) has the ability to manage wired networks, but for tactical networks, SDN faces challenges arising from unreliable transmission and inefficient operation of wireless links. Moreover, protocols in wireless tactical networks are heterogeneous to implement different tasks, while the existing Openflow protocol cannot handle such protocol heterogeneity. In this paper, we proposed a reliable dynamic control architecture based on protocol-oblivious forwarding (POF) in the wireless tactical network, which can increase the reliability of the control plane and the communication performance between switches of the data plane. Based on the proposed architecture, we customized a routing protocol, which can make data forwarding more efficient. Finally, we build a test platform based on Raspberry Pi to validate the architecture of the proposed architecture. The results demonstrate the proposed architecture achieves desired performance in dynamic tactical environments.

The reliability of current virtual reality (VR) delivery is low due to the limited resources on VR head-mounted displays (HMDs) and the transmission rate bottleneck of sub-6 GHz networks. In this paper, we propose a dual-connectivity sub-6 GHz and mmWave heterogeneous network architecture empowered by mobile edge capability. The core idea of the proposed architecture is to utilize the complementary advantages of sub-6 GHz links and mmWave links to conduct a collaborative edge resource design, which aims to improve the reliability of VR delivery. From the perspective of stochastic geometry, we analyze the reliability of VR delivery and theoretically demonstrate that sub-6 GHz links can be used to enhance the reliability of VR delivery despite the large mmWave bandwidth. Based on our analytical work, we formulate a joint caching and computing optimization problem with the goal to maximize the reliability of VR delivery. By analyzing the coupling caching and computing strategies at HMDs, sub-6 GHz and mmWave base stations (BSs), we further transform the problem into a multiple-choice multi-dimension knapsack problem. A best-first branch and bound algorithm and a difference of convex programming algorithm are proposed to obtain the optimal and sub-optimal solution, respectively. Numerical simulations demonstrate the performance improvement using the proposed algorithms, and reveal that caching more monocular videos at sub-6 GHz BSs and more stereoscopic videos at mmWave BSs can improve the VR delivery reliability efficiently.

To relieve the stress on channel estimation and decoding complexity in cell-free massive multiple-input multiple-output (MIMO) systems, user grouping problem is investigated in this paper, where access points (APs) based on time-division duplex (TDD) are considered to serve users on different time resources and the same frequency resource. In addition, when quality of service (QoS) requirements are considered, widely-used max-min power control is no longer applicable. We derive the minimum power constraints under diverse QoS requirements considering user grouping. Based on the analysis, we formulate the joint power and user grouping problem under QoS constraints, aiming at minimizing the total transmit power. A generalized benders decomposition (GBD) based algorithm is proposed, where the primal problem and master problem are solved iteratively to approach the optimal solution. Simulation results demonstrate that by user grouping, the number of users served in cell-free MIMO systems can be as much as the number of APs without increasing the complexity of channel estimation and decoding. Furthermore, with the proposed user grouping strategy, the power consumption can be reduced by 2–3 dB compared with the reference user grouping strategy, and by 7 dB compared with the total transmit power without grouping.

In network function virtualization enabled networks with dynamic traffic, virtual network function (VNF) migration has been considered as an effective way to improve quality of service as well as resource utilization. However, due to time-varying network traffic, designing a fast and accurate VNF migration algorithm is still a great challenge. To address this issue, in this paper, we exploit the temporal convolutional network (TCN) to predict traffic flow for VNF migration decision in a fast and accurate manner. Based on the predicted results, we define a metric, i.e., migration index, to represent the load trend of each node in the network. A fast and efficient heuristic VNF migration algorithm is then proposed based on the migration index, with the goal to minimize the total migration cost in a time period. Extensive simulations are carried out to validate the effectiveness of TCN for traffic prediction. The results demonstrate that the proposed VNF migration algorithm can reduce the total migration cost up to 20% compared with existing algorithms.

Limited backhaul bandwidth and blockage effects are two main factors limiting the practical deployment of millimeter wave (mmWave) networks. To tackle these issues, we study the feasibility of relaying as well as caching in mmWave networks. A user association and relaying (UAR) criterion dependent on both caching status and maximum biased received power is proposed by considering the spatial correlation caused by the coexistence of base stations (BSs) and relay nodes (RNs). Using stochastic geometry tools, we decouple the UAR and caching placement issues by analyzing the relationship between UAR probabilities and caching placement probabilities. We then optimize the formulated caching placement problem based on polyblock outer approximation by exploiting the monotonic property in the general case and utilizing convex optimization in the noise-limited case. Accordingly, we propose a BS and RN selection algorithm where caching status at BSs and maximum biased received power are jointly considered. Experimental results demonstrate a significant enhancement of backhaul offloading using the proposed algorithms, and show that deploying more RNs and increasing cache size in mmWave networks is a more cost-effective alternative than increasing BS density to achieve similar backhaul offloading performance.

To alleviate severe inter-user interference in Non-orthogonal multiple access (NOMA) systems, in practice, several users are grouped together for NOMA transmission and orthogonal resources are allocated among different groups. In this paper, we investigate uplink NOMA scenarios with multiple base stations, where user association is jointly considered with user grouping. It is worth noting that a user prefers to be associated with the base station where it can be assigned into a group with less inter-user interference. Furthermore, different users have different quality-of-service (QoS) requirements. Based on these observations, we firstly quantitatively analyze the extra transmit power that a user brings to the others in the same group under QoS constraints, i.e., externalities, and derive an externality function to indicate interference among users in uplink NOMA. Then, we obtain a game model for the joint user association, grouping and power allocation (JAGP) problem in uplink NOMA systems and prove that it is an exact potential game, with the goal to minimize total transmit power. Furthermore, we prove the existence of Nash Equilibrium (NE) as well as the finite improvement property (FIP) of this game. In addition, the NE of this game is proved to be Pareto-optimal. A heuristic algorithm is proposed to find the solution to the JAGP problem by achieving the NE of the proposed game, which convergence is guaranteed by FIP. Simulation results show that the proposed algorithm can considerably reduce the total transmit power compared with existing schemes.

Fiber-Wireless (FiWi) access networks have been widely deployed due to the complementary advantages of high-capacity fiber backhaul and ubiquitous wireless front end. To meet the increasing demands for bandwidth-hungry applications, access points (APs) are densely deployed and new wireless network standards have been published for higher data rates. Hence, fiber backhaul in FiWi access networks is still facing the incoming bandwidth capacity crunch. In this paper, we involve caches in FiWi access networks to cope with fiber backhaul bottleneck and enhance the network throughput. On the other hand, power consumption is an important issue in wireless access networks. As both power budget in wireless access networks and bandwidth of fiber backhaul are constrained, it is challenging to properly leverage power for caching and that for wireless transmission to achieve optimal system performance. To address this challenge, we formulate the downlink wireless access throughput maximization problem by joint consideration of power allocation and caching strategy in FiWi access networks. To solve the problem, firstly, we propose a volume adjustable backhaul-constrained waterfilling method (VABWF) to derive the expression of optimal wireless transmission power allocation. Then, we reformulate the problem as a multiple-choice knapsack problem (MCKP) and propose a dynamic programming algorithm to find the optimal solution of the MCKP problem. Simulation results show that the proposed algorithm significantly outperforms existing algorithms in terms of system throughput under different FiWi access network scenarios.

With the explosive increment in mobile users and mobile devices, nowadays network infrastructures are foreseen to be much more struggle in handling the vast mobile video traffic in the network. To meet this challenge, small-cell base stations (SBSs) are introduced in heterogeneous networks (HetNets) to support high data rate services in the fronthaul. However, HetNets still suffer from backhaul bandwidth shortage. Caching is a promising way to relieve the bandwidth burden on the backhaul. However it may fail in improving user's quality of experience (QoE) in video services. The reason is that, without a corresponding power allocation strategy in the fronthaul, users are unlikely to be allocated sufficient power to receive the higher quality version of videos, which in return leads to the waste of the limited caching space. On the other hand, the distinct layered feature of scalable video encoding (SVC) videos allows us to do layer-wise caching to further promote the cache utilization efficiency. Thereupon, a nature idea is to combine the power allocation strategy and the layer-wise caching strategy to effectively utilize the limited power and cache space of SBSs, so that the limited bandwidth of backhaul can be saved while user's QoE can be improved. In this work, we take the layered feature of SVC videos into account to jointly optimize these two strategies, aiming to maximize user's QoE. A simulated annealing based algorithm is proposed to solve the formulated problem. Simulation results show that, by doing so, the cache-hit ratio is increased while the limited backhaul bandwidth can be saved. Moreover, user's QoE has been significantly improved compared to traditional caching strategies.