Coordinated 5G Network Slicing How Constructive Interference Can Boost Network Throughput
ABSTARCT :
Radio access network (RAN) slicing is a virtualization technology that partitions radio resources into multiple autonomous virtual networks. Since RAN slicing can be tailored to provide diverse performance requirements, it will be pivotal to achieve the high-throughput and low-latency communications that next-generation (5G) systems have long yearned for. To this end, effective RAN slicing algorithms must (i) partition radio resources so as to leverage coordination among multiple base stations and thus boost network throughput; and (ii) reduce interference across different slices to guarantee slice isolation and avoid performance degradation. The ultimate goal of this paper is to design RAN slicing algorithms that address the above two requirements. First, we show that the RAN slicing problem can be formulated as a 0-1 Quadratic Programming problem, and we prove its NP-hardness. Second, we propose an optimal solution for small-scale 5G network deployments, and we present three approximation algorithms to make the optimization problem tractable when the network size increases. We first analyze the performance of our algorithms through simulations, and then demonstrate their performance through experiments on a standard-compliant LTE testbed with 2 base stations and 6 smartphones. Our results show that not only do our algorithms efficiently partition RAN resources, but also improve network throughput by 27% and increase by 2x the signal-to-interference-plus-noise ratio.
EXISTING SYSTEM :
? The interference resulting from densification of access points and the coexistence of different numerologies within the same spectrum severely hinders inter-slice isolation.
? We propose a slice allocation policy that enforces inter-slice isolation by minimizing the inter-slice interference suffered by each virtual operator.
? When different slices are multiplexed within the same bandwidth, each slice performance is affected by the number of radio resources that are simultaneously shared with other slices.
? Slice isolation becomes an essential requirement since it ensures that coexisting slices do not have any mutual impact on each other’s performance.
DISADVANTAGE :
? In this paper, We tackle the problem from an IP’s point of view which has no access to mobile users’ location, demanded traffic and channel conditions.
? Once RAN slicing policies have been defined, a key problem is how to allocate the spectrum resource blocks (RBs) as prescribed by the slicing policy.
? This problem, also referred to as the RAN slicing enforcement problem (RSEP), ensures that if an MVNO has been assigned a slice of 15% of the spectrum resources, such MVNO receives approximately 15% of the available RBs.
? Although the problem of RAN slicing has attracted large interest, only few works have tackled the issue of physical-level allocation of spectrum resources to MVNOs.
PROPOSED SYSTEM :
• In light of this, recent works have proposed the utilization of fog/edge computing to provide intelligence to components of the network close to the UE.
• Although coordinated multipoint (CoMP) systems were primarily proposed to improve the cell edge performance in 4G, their collaborative nature can be leveraged to support the diverse requirements and enabling technologies of 5G and beyond networks.
• Its potential as a solution to various conflicting goals of future networks is motivated, which is followed by a description of the proposed GCoMP framework.
• To identify the coordinated TPs per cluster, a set of solutions is proposed in taking into account real operating conditions such as connectivity and network layout.
ADVANTAGE :
? This techniques considerably improve network performance, but require coordination among BSs in proximity.
? The greedy algorithm RSEP-MLF enjoys fast convergence time at the price of sub-optimal performance.
? Moreover, we show that RB aggregation is the technique that produces the best performance improvement in terms of convergence time.
? This performance metric is defined as one minus the ratio between the utility function achieved by any of the aforementioned approximation and heuristic algorithms and that achieved by RSEP-QP.
? A major question, however, is whether or not the enforcement strategies presented in this paper can actually bring performance gains in terms of throughput and interference mitigation when applied to real-world 5G networks.
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