Resource Slicing in Virtual Wireless Networks: A Survey Richart, Matias; Baliosian, Javier; Serrat, Joan ...
IEEE eTransactions on network and service management,
09/2016, Volume:
13, Issue:
3
Journal Article, Publication
Peer reviewed
Open access
New architectural and design approaches for radio access networks have appeared with the introduction of network virtualization in the wireless domain. One of these approaches splits the wireless ...network infrastructure into isolated virtual slices under their own management, requirements, and characteristics. Despite the advances in wireless virtualization, there are still many open issues regarding the resource allocation and isolation of wireless slices. Because of the dynamics and shared nature of the wireless medium, guaranteeing that the traffic on one slice will not affect the traffic on the others has proven to be difficult. In this paper, we focus on the detailed definition of the problem, discussing its challenges. We also provide a review of existing works that deal with the problem, analyzing how new trends such as software defined networking and network function virtualization can assist in the slicing. We will finally describe some research challenges on this topic.
Wireless network slicing (i.e., network virtualization) is one of the potential technologies for addressing the issue of rapidly growing demand in mobile data services related to 5G cellular ...networks. It logically decouples the current cellular networks into two entities: infrastructure providers (InPs) and mobile virtual network operators (MVNOs). The resources of base stations (e.g., resource blocks, transmission power, and antennas), which are owned by the InP, are shared with multiple MVNOs who need resources for their mobile users. Specifically, the physical resources of an InP are abstracted into multiple isolated network slices, which are then allocated to MVNO's mobile users. In this paper, two-level allocation problem in network slicing is examined while enabling efficient resource utilization, inter-slice isolation (i.e., no interference among slices), and intra-slice isolation (i.e., no interference between users in the same slice). A generalized Kelly mechanism (GKM) is also designed, based on which the upper level of the resource allocation issue (i.e., between the InP and MVNOs) is addressed. The benefit of using such a resource bidding and allocation framework is that the seller (InP) does not need to know the true valuation of the bidders (MVNOs). For solving the lower level of resource allocation issue (i.e., between MVNOs and their mobile users), the optimal resource allocation is derived from each MVNO to its mobile users by using Karush-Kuhn-Tucker (KKT) conditions. Then, bandwidth resources are allocated to the users of MVNOs. Finally, the results of the simulation are presented to verify the theoretical analysis of our proposed two-level resource allocation problem in wireless network slicing.
In Mesh Wireless Networks (MWNs), the network coverage is extended by connecting Access Points (APs) in a mesh topology, where transmitting frames by multi-hop routing has to sustain the ...performances, such as end-to-end (E2E) delay and channel efficiency. Several recent studies have focused on minimizing E2E delay, but these methods are unable to adapt to the dynamic nature of MWNs. Meanwhile, reinforcement-learning-based methods offer better adaptability to dynamics but suffer from the problem of high-dimensional action spaces, leading to slower convergence. In this paper, we propose a multi-agent actor-critic reinforcement learning (MACRL) algorithm to optimize multiple objectives, specifically the minimization of E2E delay and the enhancement of channel efficiency. First, to reduce the action space and speed up the convergence in the dynamical optimization process, a centralized-critic-distributed-actor scheme is proposed. Then, a multi-objective reward balancing method is designed to dynamically balance the MWNs' performances between the E2E delay and the channel efficiency. Finally, the trained MACRL algorithm is deployed in the QaulNet simulator to verify its effectiveness.
Since wireless network virtualization enables abstraction and sharing of infrastructure and radio spectrum resources, the overall expenses of wireless network deployment and operation can be reduced ...significantly. Moreover, wireless network virtualization can provide easier migration to newer products or technologies by isolating part of the network. Despite the potential vision of wireless network virtualization, several significant research challenges remain to be addressed before widespread deployment of wireless network virtualization, including isolation, control signaling, resource discovery and allocation, mobility management, network management and operation, and security as well as non-technical issues such as governance regulations, etc. In this paper, we provide a brief survey on some of the works that have already been done to achieve wireless network virtualization, and discuss some research issues and challenges. We identify several important aspects of wireless network virtualization: overview, motivations, framework, performance metrics, enabling technologies, and challenges. Finally, we explore some broader perspectives in realizing wireless network virtualization.
Heterogeneous wireless networks (HetNets) provide a powerful approach to meeting the dramatic mobile traffic growth, but also impose a significant challenge on backhaul. Caching and multicasting at ...macro and pico base stations (BSs) are two promising methods to support massive content delivery and reduce backhaul load in HetNets. In this paper, we jointly consider caching and multicasting in a large-scale cache-enabled HetNet with backhaul constraints. We propose a hybrid caching design consisting of identical caching in the macro-tier and random caching in the pico-tier, and a corresponding multicasting design. By carefully handling different types of interferers and adopting appropriate approximations, we derive tractable expressions for the successful transmission probability in the general signal-to-noise ratio (SNR) and user density region as well as the high SNR and user density region, utilizing tools from stochastic geometry. Then, we consider the successful transmission probability maximization by optimizing design parameters, which is a very challenging mixed discrete-continuous optimization problem. By exploring structural properties, we obtain a near optimal solution with superior performance and manageable complexity. This solution achieves better performance in the general region than any asymptotically optimal solution, under a mild condition. The analysis and optimization results provide valuable design insights for practical cache-enabled HetNets.
Intelligent reflecting surface (IRS) is a new and promising paradigm to substantially improve the spectral and energy efficiency of wireless networks, by constructing favorable communication channels ...via tuning massive low-cost passive reflecting elements. Despite recent advances in the link-level performance optimization for various IRS-aided wireless systems, it still remains an open problem whether the large-scale deployment of IRSs in wireless networks can be a cost-effective solution to achieve their sustainable capacity growth in the future. To address this problem, we study in this paper a new hybrid wireless network comprising both active base stations (BSs) and passive IRSs, and characterize its achievable spatial throughput in the downlink as well as other pertinent key performance metrics averaged over both channel fading and random locations of the deployed BSs/IRSs therein based on stochastic geometry . Compared to prior works on characterizing the performance of wireless networks with active BSs only, our analysis needs to derive the power distributions of both the signal and interference reflected by distributed IRSs in the network under spatially correlated channels, which exhibit channel hardening effects when the number of IRS elements becomes large. Extensive numerical results are presented to validate our analysis and demonstrate the effectiveness of deploying distributed IRSs in enhancing the hybrid network throughput against the conventional network without IRS, which significantly boosts the signal power but results in only marginally increased interference in the network. Moreover, it is unveiled that there exists an optimal IRS/BS density ratio that maximizes the hybrid network throughput subject to a total deployment cost given their individual costs, while the conventional network without IRS (i.e., zero IRS/BS density ratio) is generally suboptimal in terms of throughput per unit cost.
In the problem of routing in multi-hop wireless networks, to achieve high end-to-end throughput, it is crucial to find the "best" path from the source node to the destination node. Although a large ...number of routing protocols have been proposed to find the path with minimum total transmission count/time for delivering a single packet, such transmission count/time minimizing protocols cannot be guaranteed to achieve maximum end-to-end throughput. In this paper, we argue that by carefully considering spatial reusability of the wireless communication media, we can tremendously improve the end-to-end throughput in multi-hop wireless networks. To support our argument, we propose spatial reusability-aware single-path routing (SASR) and anypath routing (SAAR) protocols, and compare them with existing single-path routing and anypath routing protocols, respectively. Our evaluation results show that our protocols significantly improve the end-to-end throughput compared with existing protocols. Specifically, for single-path routing, the median throughput gain is up to 60 percent, and for each source-destination pair, the throughput gain is as high as 5.3x; for anypath routing, the maximum per-flow throughput gain is 71.6 percent, while the median gain is up to 13.2 percent.
One of the security issues in a wireless network is jamming attacks, where the jammer causes congestion and significant decrement in the network throughput by obstructing channels and disrupting user ...signals. Recent works have proposed using a deep-reinforcement learning (DRL) model to confront the jamming attacker due to its capability in predicting the jammer decisions and pattern recognition. Training a DRL model from scratch may take a long time. We first propose a recurrent neural network architecture to minimize the number of parameters for training a DRL model. We further propose a transfer learning (TL) approach to enable the DRL agent to learn fast in dynamic wireless networks to confront jamming attacks effectively. To make our proposed TL method adaptive to different network environments, we propose a novel method to quantitatively measure the difference between the source and target domains, using an integrated feature extractor. Afterward, based on the measured difference, we can choose an optimal setting for the TL model. We also show that the proposed TL method can effectively reduce the training time for the DRL model and outperforms other existing TL methods.
This paper introduces the concept of smart radio environments, currently intensely studied for wireless communication in metasurface‐programmable meter‐scaled environments (e.g., inside rooms), on ...the chip scale. Wireless networks‐on‐chips (WNoCs) are a candidate technology to improve inter‐core communication on chips but current proposals are plagued by a dilemma: either the received signal is weak, or it is significantly reverberated such that the on–off‐keying modulation speed must be throttled. Here, this vexing problem is overcome by endowing the wireless on‐chip environment with in situ programmability which enables the shaping of the channel impulse response (CIR); thereby, a pulse‐like CIR shape can be imposed despite strong multipath propagation and without entailing a reduced received signal strength. First, a programmable metasurface suitable for integration in the on‐chip environment (“on‐chip reconfigurable intelligent surface”) is designed and characterized. Second, its configuration is optimized to equalize selected wireless on‐chip channels “over the air.” Third, by conducting a rigorous communication analysis, the feasibility of significantly higher modulation speeds with shaped CIRs is evidenced. The results introduce a programmability paradigm to WNoCs which boosts their competitiveness as complementary on‐chip interconnect solution.
A programmable metasurface is included inside a chip package, and suitable metasurface configurations are identified that equalize wireless channels on the chip over‐the‐air to mitigate inter‐symbol interference. The largely improved data transfer rates boost the competitiveness of wireless networks‐on‐chips (WNoCs) as complementary interconnect technology. WNoCs aim to avert the risk of communication‐limited performance of multicore chips.
Localized channel modeling is crucial for offline performance optimization of wireless networks, but existing channel models are not well suited for wireless network optimization. In this paper, we ...propose a physics-based and data-driven localized statistical channel model for wireless network optimization. The proposed channel modeling solely relies on the reference signal receiving power (RSRP). The key is to build the statistical relationship between the RSRP and the angular power spectrum (APS). Based on it, we formulate the task of channel modeling as a sparse recovery problem where the non-zero entries of the APS indicate the channel paths' powers and angles of departure. Although such problem typically can be handled by orthogonal matching pursuit (OMP)-type algorithms, our problem is more challenging due to the non-uniform and closely parallel columns of the coefficient matrix. To address these issues, we propose the weighted non-negative OMP (WNOMP) and the second-order-statistics-based WNOMP (SWOMP) algorithms. The WNOMP algorithm can alleviate the effect of non-uniform columns, while the SWOMP algorithm can further identify the closely parallel columns correctly. Finally, comprehensive experiments based on synthetic and real-world RSRP are presented to demonstrate that the proposed methods outperform classic methods in terms of accuracy and mean absolute error (MAE).
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