Osman Aydin

Generalized resource sharing for multiple operators in cellular wireless networks

Sharing wireless channel resources among cellular network operators saves costs by efficiently utilizing hardware and spectrum. Following the approach of resource sharing, we introduce a general formulation that covers current fixed and dynamic approaches as special cases. Unlike previous approaches, our formulation maximizes spectral efficiency by occasionally penalizing an operator within defined boundaries. This interesting tradeoff between sharing guarantees and system performance is studied by simulation. Our results clarify the costs of fair sharing and how much spectral efficiency can be gained by relaxing fairness in a controlled manner. This study, thus, provides important insight into the efficient design of shared base stations in future cellular networks.

Extending Generalized Processor Sharing for multi-operator scheduling in cellular networks

Sharing wireless channel resources among cellular network operators saves costs by efficiently utilizing hardware and spectrum. To allocate time slots among the users of multiple operators, we introduce a new scheduling algorithm that is based on General Processor Sharing and a Bin Packing heuristic. Our flexible scheduler design allows each operator to execute its own multi-user scheduling policy while providing the agreed resource share. From a practical point of view, our multi-operator scheduler reaches very high spectral efficiency and fairness at an only insignificant increase in queuing delay.

Wireless resource sharing for multiple operators: Generalization, fairness, and the value of prediction

Abstract Sharing wireless channel resources among cellular network operators saves cost by efficiently utilizing hardware, sites, and spectrum. To achieve high spectral efficiency, resource sharing has to adapt to the instantaneous load and channel state. However, before operators accept such dynamic scheduling policies, a solid understanding of their reliability and performance is required. In this paper, we present a consistent theoretical framework for Multi-Operator Scheduling (MOS). This formulation allows to analyze sharing guarantees and spectral efficiency for a large number of parameters and covers various fixed and dynamic resource sharing policies as special cases. Extending our study to the complete knowledge of the future channel state allows us to study the complete performance region of MOS. The proofs, analysis and simulation results in this work provide a profound understanding of the fundamental tradeoff between sharing guarantees and performance in multi-operator sharing. This insight will enable reliable yet efficient infrastructure sharing in 5G Radio Access Networks.

User-centric architectures: Enabling CoMP via hardware virtualization

Motivated by the difficulty in mapping theoretic Coordinated Multi-Point (CoMP) studies into real-world solutions, herein we propose a novel architectural solution for CoMP-based systems. The basic idea is to dynamically allocate virtual processing resources in a base band unit pool, to sets of transmission points, these processing resources being used for jointly transmitting to a given set of users. This proposal corresponds in dropping the cell as the elementary processing unit, and tailoring the system architecture to optimize the service for each user. Such an architecture allows a much easier implementation of advanced CoMP solutions.

Radio resource sharing among operators through MIMO based spatial multiplexing in 5G systems

Network sharing is one of the solutions for mobile operators to improve spectral efficiency and enhance coverage with smaller operational and investment costs. In this work, we propose a novel solution based on the multi-user MIMO approach for the network sharing among multiple operators. We use multiple antennas for spatial multiplexing of multiple operators on the smallest possible radio resource element. We consider three different service level agreements between the shared operators and the service provider. These agreements differ with respect to the pre-defined system utility optimization. The utilities we consider are the achievement of fairness among operators and the optimization of system spectral efficiency. We also propose new algorithms for the achievement of these utilities. The simulation results show that our algorithms perfectly achieve the service level agreements which is one of the key objectives in a shared network scenario. For the spectral efficiency we show that the resource fairness algorithm outperforms the performance results of the rate fairness algorithm.

An analysis of generalized resource sharing for multiple operators in cellular networks

Sharing wireless channel resources among cellular network operators saves costs by efficiently utilizing hardware and spectrum. Following the approach of Generalized Resource Sharing, this paper provides a mathematical rigorous formulation of multi-operator scheduling. The presented scheduling policy allows to trade off sharing guarantees versus spectral efficiency, covering current fixed and dynamic approaches as special cases. Analyzing this general scheduling policy leads to a profound understanding of its most important parameters and of their effect on rate-dependent utilities. The presented proofs are confirmed by simulation results that hold for insightful scenarios and point to promising future work.

Energy-Spectral Efficiency Tradeoffs in 5G Multi-Operator Networks With Heterogeneous Constraints

Along with spectral efficiency (SE), energy efficiency (EE) is a key performance metric for the design of 5G and beyond 5G (B5G) wireless networks. At the same time, infrastructure sharing among multiple operators has also emerged as a new trend in wireless communication networks. This paper presents an optimization framework for EE and SE maximization in a network, where radio resources are shared among multiple operators. We define a heterogeneous service level agreement (SLA) framework for a shared network, in which the constraints of different operators are handled by two different multi-objective optimization approaches namely the utility profile and scalarization methods. Pareto-optimal solutions are obtained by merging these approaches with the theory of generalized fractional programming. The approach applies to both noise-limited and interference-limited systems, with single-carrier or multi-carrier transmission. Extensive numerical results illustrate the effect of the operator specific SLA requirements on the global spectral and EE. Three network scenarios are considered in the numerical results, each one corresponding to a different SLA, with different operator-specific EE and SE constraints.

A Two-Step Scheduler for Dynamically Sharing Wireless Channel Resources among Operators

Sharing wireless channel resources among cellular network operators saves costs by efficiently utilizing hardware and spectrum. We introduce a practical scheduler that (i) allocates time slots among operators in a round robin manner and (ii) allocates resources to the users of each operator. This two-step approach guarantees the agreed resource shares while allowing each operator to execute its own multi-user scheduling policy. Our simulation results demonstrate an insignificant increase in delay at high spectral efficiency and fairness.

5G Multi-RAT Integration Evaluations Using a Common PDCP Layer

5G is expected to operate in a wide frequency range to support new challenging use-cases. Multi- RATs (Radio Access Technologies): NR (New Radio) and evolved LTE (Long Term Evolution) will together constitute 5G. Utilizing NR at high frequencies will have a significant impact on radio propagation conditions with e.g. unfavorable higher path loss and increased outdoor-to-indoor penetration losses. In order to provide a reliable communication from the outset of 5G deployment and to minimize the standardization and implementation complexity, 5G UP (User Plane) instances of 5G AIs (Air Interface) related to evolved LTE and NR need to be aggregated on a certain layer of the protocol stack. This paper sheds light on how to integrate 5G AIs into a single 5G AI framework and explores which protocol stack layer could be used as aggregation layer. Inter-RAT hard handover is the state of the art technique to integrate multiple RATs in order to support mobility and reliability across different RATs. However, the hard handover incurs a transmission interruption which stands as an obstacle along the way of accomplishing 5G design. According to simulation results, a common PDCP (Packet Data Convergence Protocol) layer improves the hard handover functionality and stands out as a basis for tight interworking between evolved LTE and NR. By means of simulation, it is shown that the multi-RAT UP aggregation can achieve three times higher user throughput, when NR is using 28 GHz and LTE 2 GHz, compared to stand-alone NR.

Rate Fairness Based QoS Provisioning for Operators in 5G Shared Networks

In this work, we focus on two problems related to the rate fairness based SLAs (Service Level Agreements) in an environment where multiple operators are sharing the spectrum. At first we show analytically that achieving rate fairness among arbitrary number of operators on the smallest time-frequency radio resource (e.g. one OFDM resource element) is feasible. Then we transform the problem of rate fairness to power allocation problem and provide an analytical proof that a unique solution is possible for multiple operators. In addition to the analytical method, we also provide an algorithm which is capable of guaranteeing rate fairness for any arbitrary number of operators. In our algorithm, we use Newton-Raphson based numerical approximation which is well known for its fast convergence and simplicity. We also present simulation results for the assessment of our proposed approach and algorithm. The results show that rate fairness based SLAs can also achieve system spectral efficiencies as high as the resource fairness based SLAs. This provides an extra degree of freedom to the 5G service providers in terms of rate based QoS provisioning.