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22
Offloading in heterogeneous networks: Modeling, analysis and design insights
 IEEE TRANS. WIRELESS COMMUN
, 2013
"... Pushing data traffic from cellular to WiFi is an example of inter radio access technology (RAT) offloading. While this clearly alleviates congestion on the overloaded cellular network, the ultimate potential of such offloading and its effect on overall system performance is not well understood. To ..."
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Cited by 67 (17 self)
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Pushing data traffic from cellular to WiFi is an example of inter radio access technology (RAT) offloading. While this clearly alleviates congestion on the overloaded cellular network, the ultimate potential of such offloading and its effect on overall system performance is not well understood. To address this, we develop a general and tractable model that consists of M different RATs, each deploying up to K different tiers of access points (APs), where each tier differs in transmit power, path loss exponent, deployment density and bandwidth. Each class of APs is modeled as an independent Poisson point process (PPP), with mobile user locations modeled as another independent PPP, all channels further consisting of i.i.d. Rayleigh fading. The distribution of rate over the entire network is then derived for a weighted association strategy, where such weights can be tuned to optimize a particular objective. We show that the optimum fraction of traffic offloaded to maximize SINR coverage is not in general the same as the one that maximizes rate coverage, defined as the fraction of users achieving a given rate.
Average rate of downlink heterogeneous cellular networks over generalized fading channels – A stochastic geometry approach
 IEEE Trans. Commun
, 2013
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Energyspectral efficiency tradeoff in virtual MIMO cellular systems
 IEEE J. Sel. Areas Commun
, 2013
"... technology promises significant performance enhancements to cellular systems in terms of spectral efficiency (SE) and energy efficiency (EE). How these two conflicting metrics scale up in large cellular VMIMO networks is unclear. This paper studies the EESE tradeoff of the uplink of a multiuser ..."
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Cited by 5 (4 self)
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technology promises significant performance enhancements to cellular systems in terms of spectral efficiency (SE) and energy efficiency (EE). How these two conflicting metrics scale up in large cellular VMIMO networks is unclear. This paper studies the EESE tradeoff of the uplink of a multiuser cellular VMIMO system with decodeandforward type protocols. We first express the tradeoff in an implicit function and further derive closedform formulas of the tradeoff in low and high SE regimes. Unlike conventional MIMO systems, the EESE tradeoff of the VMIMO system is shown to be susceptible to many factors including protocol design (e.g., resource allocation) and scenario characteristics (e.g., user density). Focusing on the medium and high SE regimes, we propose a heuristic resource allocation algorithm to optimize the EESE tradeoff. The fundamental performance limits of the optimized VMIMO system are subsequently investigated and compared with conventional MIMO systems in different scenarios. Numerical results reveal a surprisingly chaotic behavior of VMIMO systems when the user density scales up. Our analysis indicates that low frequency reuse factor, adaptive resource allocation, and user density control are critical to harness the full benefits of cellular VMIMO systems. Index Terms—Energy efficiency, spectral efficiency, virtual MIMO, adaptive resource allocation. I.
Joint Spectrum Partition and User Association in MultiTier Heterogeneous Networks
"... Abstract—The joint spectrum partition and user association problem for multitier heterogeneous networks is studied in this paper, where disjoint spectrums are allocated among tiers and users are associated with each tier with a biased received power. The random placement of basestations (BSs) of d ..."
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Abstract—The joint spectrum partition and user association problem for multitier heterogeneous networks is studied in this paper, where disjoint spectrums are allocated among tiers and users are associated with each tier with a biased received power. The random placement of basestations (BSs) of different tiers are modeled using stochastic geometry, which accounts for their practical deployment and also makes analysis tractable. We derive an upper bound of the average user proportional fair utility based on the user coverage rate, from which we formulate a network utility maximization problem. The optimization of the proposed utility bound shows that the optimal spectrum allocation for each BS tier matches the average proportion of users associated with that tier. The solution to the optimization problem also provides closedform expressions for the optimal user associated bias factors. Compared to systemlevel optimization solutions based on specific network topology and channel realization, our offline analytical approach offers deployment insights. Simulation results demonstrates the effectiveness of the proposed approach. I.
D2D enhanced heterogeneous cellular networks with dynamic TDD,” in arXiv:cs.NI/1406.2752
, 2014
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Radio Resource Allocation in Heterogeneous Wireless Networks: A SpatialTemporal Perspective
"... Abstract—We study optimal radio resource allocation across multiple tiers of a heterogeneous wireless network in order to maximize the downlink sum throughput. Different from prior works, we consider both the randomness of base stations in space and dynamic user traffic session arrivals in time, acc ..."
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Abstract—We study optimal radio resource allocation across multiple tiers of a heterogeneous wireless network in order to maximize the downlink sum throughput. Different from prior works, we consider both the randomness of base stations in space and dynamic user traffic session arrivals in time, accounting for both elastic and inelastic user traffic. A new stochastic analysis framework, which accommodates both spatial and temporal dimensions, is proposed to quantify the throughput objective. The derived throughput function is not in closed form and is nonconcave in terms of the radio resource allocation factors to be optimized, hindering the search for an efficient optimization solution. Therefore, we further develop closedform concave bounds that envelop the throughput function, to form convex approximations of the original optimization problem that can be solved efficiently. We characterize the performance gap when these bounds are used instead of the original objective. Both analytical bounding and simulation experiments demonstrate that the proposed solution is nearly optimal. I.
Nearoptimal spectrum allocation in multitier cellular networks with random inelastic traffic
 in Proc. of IEEE ICASSP
, 2014
"... We present a new method for spectrum allocation in a heterogeneous cellular network with multiple tiers of randomly placed base stations and random user session arrivals. Different from previous works, inelastic network traffic is considered, so as to accommodate application sessions with fixed dat ..."
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We present a new method for spectrum allocation in a heterogeneous cellular network with multiple tiers of randomly placed base stations and random user session arrivals. Different from previous works, inelastic network traffic is considered, so as to accommodate application sessions with fixed data rate requirements. We first quantify the average downlink sum throughput of the network in terms of a given spectrum allocation vector. We then derive concave upper and lower bounds to the throughput to allow efficient approximate solutions to optimize spectrum allocation. We show that the proposed approach has a worst case optimization performance gap of 12:6 % and further demonstrate via simulation that its actual performance is often near optimal. 1.
Costeffective broadcast in cellular networks,” availalbe at: http://arxiv.org/pdf/1304.4285.pdf
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Asymptotic behavior of ultradense cellular networks and its economic impact
, 2014
"... Abstract—This paper investigates the relationship between base station (BS) density and average rate in downlink cellular networks. This relationship has been well known for sparse deployment, i.e. when the number of base stations is low compared to the number of users, showing average rate is inde ..."
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Abstract—This paper investigates the relationship between base station (BS) density and average rate in downlink cellular networks. This relationship has been well known for sparse deployment, i.e. when the number of base stations is low compared to the number of users, showing average rate is independent of BS density. As BS density grows, on the other hand, several preceding works proved BS increases average rate, but no tractable form for the ratedensity relationship has yet been derived. In this paper we derive such a closedform result that reveals the rate is asymptotically a logarithmic function of BS density as the density grows. Further, we study the impact of this result on the network operator’s profit when user demand varies, and derive the profit maximizing BS density and the optimal amount of spectrum to be utilized in closed forms. Consequently, we provide deployment planning guidelines that will aid the operator in his decision if he should invest more in BS densification or in acquiring more spectrum for his ultradense cellular network. Index Terms—Ultradense cellular network, base station density, average rate, spectrum amount, profit maximization. I.