Sumudu Samarakoon

Backhaul-Aware Interference Management in the Uplink of Wireless Small Cell Networks

The design of distributed mechanisms for interference management is one of the key challenges in emerging wireless small cell networks whose backhaul is capacity limited and heterogeneous (wired, wireless and a mix thereof). In this paper, a novel, backhaul-aware approach to interference management in wireless small cell networks is proposed. The proposed approach enables macrocell user equipments (MUEs) to optimize their uplink performance, by exploiting the presence of neighboring small cell base stations. The problem is formulated as a noncooperative game among the MUEs that seek to optimize their delay-rate tradeoff, given the conditions of both the radio access network and the - possibly heterogeneous - backhaul. To solve this game, a novel, distributed learning algorithm is proposed using which the MUEs autonomously choose their optimal uplink transmission strategies, given a limited amount of available information. The convergence of the proposed algorithm is shown and its properties are studied. Simulation results show that, under various types of backhauls, the proposed approach yields significant performance gains, in terms of both average throughput and delay for the MUEs, when compared to existing benchmark algorithms.

Co-Primary Multi-Operator Resource Sharing for Small Cell Networks

To tackle the challenge of providing higher data rates within limited spectral resources we consider the case of multiple operators sharing a common pool of radio resources. Four algorithms are proposed to address co-primary multi-operator radio resource sharing under heterogeneous traffic in both centralized and distributed scenarios. The performance of these algorithms is assessed through extensive system-level simulations for two indoor small cell layouts. It is assumed that the spectral allocations of the small cells are orthogonal to the macro network layer and thus, only the small cell traffic is modeled. The main performance metrics are user throughput and the relative amount of shared spectral resources. The numerical results demonstrate the importance of coordination among co-primary operators for an optimal resource sharing. Also, maximizing the spectrum sharing percentage generally improves the achievable throughput gains over non-sharing.

Joint Load Balancing and Interference Mitigation in 5G Heterogeneous Networks

We study the problem of joint load balancing and interference mitigation in heterogeneous networks in which massive multiple-input multiple-output macro cell base station (BS) equipped with a large number of antennas, overlaid with wireless self-backhauled small cells (SCs), is assumed. Self-backhauled SC BSs with full-duplex communication employing regular antenna arrays serve both macro users and SC users by using the wireless backhaul from macro BS in the same frequency band. We formulate the joint load balancing and interference mitigation problem as a network utility maximization subject to wireless backhaul constraints. Subsequently, leveraging the framework of stochastic optimization, the problem is decoupled into dynamic scheduling of macro cell users, backhaul provisioning of SCs, and offloading macro cell users to SCs as a function of interference and backhaul links. Via numerical results, we show the performance gains of our proposed framework under the impact of SCs density, number of BS antennas, and transmit power levels at low and high frequency bands. It is shown that our proposed approach achieves a 5.6 times gain in terms of cell-edge performance as compared with the closed-access baseline in ultra-dense networks with 350 SC BSs per

Gibbs Sampling based Spectrum Sharing for Multi-Operator Small Cell Networks

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Outage Probability and Capacity for Two-Tier Femtocell Networks by Approximating Ratio of Rayleigh and Log Normal Random Variables

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Vehicle clustering for improving enhanced LTE-V2X network performance

To tackle the challenge of providing higher data rates within limited spectral resources we consider the case of multiple operators sharing a common pool of radio resources in the downlink. The goal is to maintain a long term fairness of spectrum sharing with a no coordination among small cell base stations. It is assumed that the spectral allocations of the small cells are orthogonal to the macro network layer and thus, only the small cell traffic is modeled. We develop a decentralized control mechanism for base stations using Gibbs sampling based learning techniques. Four algorithms are compared addressing the co-primary multi-operator radio resource sharing under heterogeneous traffic in both centralized and distributed scenarios. The performance of these algorithms is assessed through extensive system-level simulations for two indoor small cell layouts. The main performance metrics are user throughput and fairness between operators. The numerical results demonstrate that the proposed Gibbs sampling based learning algorithm provides considerably high throughput while ensuring fairness between OPs.

System level analysis of multi-operator small cell network at 10 GHz

This paper presents the derivation for per-tier outage probability of a randomly deployed femtocell network over an existing macrocell network. The channel characteristics of macro user and femto user are addressed by considering different propagation modeling for outdoor and indoor links. Location based outage probability analysis and capacity of the system with outage constraints are used to analyze the system performance. To obtain the simplified expressions, approximations of ratios of Rayleigh random variables (RVs), Rayleigh to log normal RVs and their weighted summations, are derived with the verifications using simulations.

Enhanced Co-Primary Spectrum Sharing Method for Multi-Operator Networks

Vehicle-to-Everything (V2X) communication holds the promise for improving road safety and reducing road accidents by enabling reliable and low latency services for vehicles. Vehicles are among the fastest growing type of connected devices. Therefore, there is a need for V2X communication, i.e., passing of information from Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) and vice versa. In this paper, we focus on both V2I and V2V communication in a multi-lane freeway scenario, where coverage is provided by the Long Term Evolution Advanced (LTE-A) road side unit (RSU) network. Here, we propose a mechanism to offload vehicles with low signal-to-interference-plus-noise ratio (SINR) to be served by other vehicles, which have much higher quality link to the RSU. Furthermore, we analyze the improvements in the probabilities of achieving target throughputs and the performance is assessed through extensive system-level simulations. Results show that the proposed solution offloads low quality V2I links to stronger V2V links, and further increases successful transmission probability from 93% to 99.4%.

Ultra Dense Small Cell Networks: Turning Density Into Energy Efficiency

Due to higher cost and spectrum scarcity, it is expected that an efficient use of spectrum in fifth generation (5G) networks will rather rely on sharing than exclusive licenses, especially when higher frequency allocations are considered. In this paper, the performance of a dense indoor multi-operator small cell network at 10 GHz is analyzed. The main goal is to show the benefits obtained at higher carrier frequency due to network densification while mobile network operators are sharing the spectrum. The analysis is assessed through extensive system level simulations. The main performance metrics are user throughput and signal-to-interference-and-noise ratio. Results show that when 10 GHz carrier frequency is used it allows higher network densities while satisfying user throughput requirements. However, when network is sparse lower carrier frequency leads to better performance. When network is dense, on average 2 Mb/s better mean throughput is achieved at 10 GHz when compared to traditional cellular frequency.

Opportunistic sleep mode strategies in wireless small cell networks

We consider a multi-operator small cell network where mobile network operators are sharing a common pool of radio resources. The goal is to ensure long term fairness of spectrum sharing without coordination among small cell base stations. It is assumed that spectral allocation of the small cells is orthogonal to the macro network layer, and thus, only the small cell traffic is modeled. We develop a decentralized control mechanism for base stations using the Gibbs sampling based learning technique, which allocates a suitable amount of spectrum for each base station. Five algorithms are compared addressing co-primary multi-operator resource sharing under heterogeneous traffic requirements and the performance is assessed through extensive system-level simulations. The main performance metrics are user throughput and fairness between operators. The numerical results demonstrate that the proposed Gibbs sampling based learning algorithm provides about tenfold cell edge throughput gains compared to state-of-the-art algorithms, while ensuring fairness between operators.