Optimal Base Station Density for Power Efficiency in Cellular Networks


 Randell Gilmore
 1 years ago
 Views:
Transcription
1 Optimal Base Station Density for Power Efficiency in Cellular Networks Sanglap Sarkar, Radha Krishna Ganti Dept. of Electrical Engineering Indian Institute of Technology Chennai, 636, India {ee11s53, Martin Haenggi Dept. of Electrical Engineering University of Notre Dame Notre Dame, IN 46556, USA Astract In cellular networks, cell size reduction is an important technique for improving the spectral reuse and achieving higher data rates. In addition, it results in power savings as it leads to a decrease in transmit power. However, it is not clear if the transmit power can e indefinitely decreased with the cell sizes. In this paper, we analyze the impact of transmit power reduction (cell size reduction) on the performance of the network. More precisely, we otain a lower ound on the transmit power such that a minimum coverage and a minimum data rate can e guaranteed. We then analyze the area power consumption metric, which denotes the total power consumed per unit area. Under the constraints of target coverage and target data rate, the area power consumption is minimized and the optimal ase station density is otained. For a path loss exponent α > 4, we oserve the existence of a minimum cell size elow which shrinking the cell would result in an overall increase of power. However, for α 4, there exists no such optimal cellsize, as the area power consumption increases with ase station density. Index Terms Small cells, green communication, cell size, quality of service, power consumption, power efficiency, optimal ase station density. I. INTRODUCTION Cell size reduction provides increased spectral reuse and increased data rates to moile users. As the cell size decreases, the numer of users per ase station (BS) decreases leading to a greater andwidth (or time share) per user. Cell size reduction can e achieved y increasing the density of the ase stations, either y increasing the numer of macro ase stations or adding tiers of low powered ase stations. There are two advantages of cell size reduction. Firstly, increased andwidth per user. Secondly, lower transmit power since the moile user is much closer to a ase station. In this paper, we investigate if the downlink transmit power can e decreased aritrarily y increasing the density of ase stations for a given target rate and coverage. It turns out that after a certain power threshold, noise plays a significant role on oth coverage and rate. For α > 4, we otain an expression for the optimum ase station density which minimizes area power consumption and maximizes power efficiency 1 under target rate and coverage constraints. If the cell density exceeds an optimal threshold 1 Power efficiency is defined as inverse of the area power consumption. We call the network to e power efficient if the area power consumption decreases with increase of ase station density. the total power consumption increases. The optimum density gives us a limit up to which cell size can e shrunk and power savings can e achieved. It also indicates when operator should stop further deployment of small cells in this regime. For α 4, we oserve that increasing the ase station density leads to an overall increase of power. II. SYSTEM MODEL AND PROBLEM FORMULATION A. System Model In this paper, we assume a single tier of BSs and focus on the downlink. The locations of the ase stations are modeled y a spatial Poisson point process (PPP) [1] Φ of density. We assume that users forms a stationary point process with density λ u that is independent of the ase station process. Since the users are uniformly distriuted, the average numer of users per cell is λ u /. We assume a path loss function l(x) = K x α, α > 2, where K is a constant chosen 2 as Without loss of generality, we focus on a moile user at the origin for computing coverage and rate. We assume that each moile user is served y its closest BS. We also assume that all BSs transmit with the same power P. The andwidth (BW) per user is a random variale that depends on the cell size and the numer of users in the cell. However, for ease of analysis we set it to e equal to the total availale andwidth (B) divided y the average numer of users, i.e., B u ( ) = B λ u. Hence the average rate per user is R = B u ( )E[ln(1 + SINR)]. Since the user BW depends on the densities, the noise power is F kt B u ( ), where F is the receiver noise figure, k is the Boltzmann constant, and T is the amient temperature. The small scale fading is denoted y h x which is assumed to follow an exponential distriution (square of Rayleigh fading) with unit mean. The signaltointerferenceplusnoise ratio for 2 A typical femtocell covers a range of 4 m with a transmit power of.2 W [2]. The received power at the cell oundary should e at least equal to the receiver sensitivity, which is 8 dbm. So r = 4, α = 3, transmit power P t =.2, received power P r = = K P t r α. Hence, K =
2 the moile user at the origin is h x x α SINR = σ 2 P + y Φ \{x} h y y, α where σ 2 = F kt B/(λ u K), and x Φ is the BS closest to the origin. The coverage proaility is defined as (P ) = P(SINR > θ), and the spectral efficiency is defined as τ(p ) = E[ln(1 + SINR)]. B. Prolem Formulation With BSs transmitting at power P, we define the area transmit power consumption as P. In addition to the transmission power, each BS requires a fixed amount of operational power which we denote y P. The power P includes the fixed operational power of the hardware and also the transmit power of the pilot tones. Hence the area power consumption is (P + P ). When the transmit power is infinity, it is shown in [3] that the SINR distriution and hence the coverage proaility and spectral efficiency does not depend on the BS density. The rate demanded y each user is denoted y ; it is independent of the ase station density. The interferencelimited spectral efficiency, corresponding to P =, is τ( ). It is independent of the ase station density and depends only on path loss exponent α. So, irrespective of the transmit power, the maximum achievale rate for a particular density is given y τ( )B u ( ). The user demand rate can e satisfied only if it is less than the maximum achievale rate. But if the user demand exceeds the maximum achievale rate, then the est the operator can do is to support a data rate of τ( )B u ( ). So, the operator must satisfy the target rate or maximum achievale rate, whichever is minimum, with a maximum rate outage δ. The limiting coverage proaility that can e achieved for a given SINR threshold θ and path loss exponent α is given y ( ). So, the operator must provide ( ) coverage with a maximum outage of ɛ. Our goal is to otain the optimal parameters λ and transmit power P so that the area power consumption (P + P ) is minimal while maintaining the QoS constraints of rate and coverage. Formally, λ = arg min { (P + P )} s.t. (P ) (1 ɛ) ( ), (1) R(P ) (1 δ) min{, τ( )B u ( )}. (2) We consider oth the rate and coverage constraints for the following two reasons: Even though B u ( ) ln(1 + SINR) might e high, SINR might e low. This might e ecause of a large BW B. Because of the inclusion of, the lower ound on transmit power otained from the two constraints, may scale differently with the BS density as can e seen in Lemma 1 and Lemma 2 and Fig. 3 and Fig. 6. C. Prior Work Energy efficiency in HetNets has received increasing attention recently. Mostly only interferencelimited networks have een considered [4], [5]. Different energy saving techniques in communication networks, have een presented in [6]. Energy efficiency in cellular networks has also een analyzed in [7],[8]. However, the authors have focused on a hexagonal grid model with fixed downlink transmit power. In [4], an optimal asestation density for oth homogeneous and heterogeneous cellular networks has een otained under the constraint of target rate. However, noise has een ignored. Coverage constraints have not een considered, either.energy efficiency in HetNets has een studied in [5], which provides an optimal macropico density ratio that maximizes the overall energy efficiency under fixed noise assumption (i.e., noise power does not vary with the allocated andwidth). In this paper, we consider noise and oth target coverage constraints and target rate constraints. We also show how the transmit power has to e scaled down with increase of ase station density. III. OPTIMAL POWER FOARGET COVERAGE AND RATE A. Minimum transmit power for coverage As the BS density increases, the transmit power of the ase stations may e decreased ecause of the decreasing cell size. However, reducing the transmit power, decreases the coverage proaility ecause of the noise. See Fig. 1. In the next lemma we evaluate the transmit power required to achieve (1 ɛ) ( ) coverage. Lemma 1. The minimum downlink transmit power for which a minimum SINR of θ can e guaranteed for a fraction 1 ɛ of the users is given y where k 1 = P c k 1 θσ2 Γ(α/2+1) ɛπ α/2 [1+ρ(θ,α)] α/2. λ α/2 1, (3) Proof: The coverage proaility without noise (or infinite power) is [3] ( ) = (1 + ρ(θ, α)) 1, (4) where ρ(θ, α) = θ 2/α 1 dx. The coverage proaility 1+x α/2 with noise is [3] (P ) = π e π(1+ρ(θ,α))v θσ 2 v α/2 P 1 dv. (5) Now, using the sustitution θσ 2 P 1 s in (5) and the approximation e svα/2 (1 sv α/2 ) for small s (or, equivalently, small σ 2 ), we get ( θσ 2 ) Γ(α/2 + 1) (P ) ( ) 1, (6) P λ α/2 1 [π(ρ(θ, α) + 1)] α/2 where Γ(x) = t x 1 e t dt is the standard gamma function. This approximation (linearization of the exponential term) is
3 1 Coverage proaility vs Transmit power Min transmit power for target coverage vs ase station density 7 Coverage proaility: (P) =1/1 2 m 2, θ= 15dB =1/5 2 m 2, θ= 15dB =1/4 2 m 2, θ= 15dB ( )(θ= 15dB, α=4) =1/1 2 m 2, θ= db =1/5 2 m 2, θ=db =1/4 2 m 2, θ=db ( )(θ=db, α=4) Transmit power: P (dbm) Fig. 1: Effect of transmit power on coverage proaility: User density λ u =.1 m 2, α = 4 and σ 2 = W. min power for target coverage: P c * (dbm) θ=5db, α=3 θ=2db, α=3 θ=5db, α=4 θ=2db, α= Base station density: λ (in m 2 ) 1 2 Fig. 3: Minimum transmit power for target coverage: User density λ u =.1 m 2, Outage proaility ɛ =.25 and σ 2 = W. Tightness of approximation used to evaluate coverage proaility Rate vs transmit power Coverage Proaility: (P) With Approximation for =1 6 m 2 Without Approximation for =1 6 m 2 With Approximation for =1 2 m 2 Without Approximation for =1 2 m Transmit Power P (dbm) Fig. 2: Tightness of numerical approximation e (θσ2 P 1 )v α/2 (1 θσ 2 P 1 v α/2 ) used to evaluate coverage proaility: SINR threshold θ = 3dB, User density λ u =.1 m 2, α = 3 and σ 2 = W. Rate: R (in ps) = 1/1 2 m 2 = 1/5 2 m 2 = 1/2 2 m 2 = 1/7 2 m 2 R 1 T = 4Kps Transmit power: P (dbm) Fig. 4: Effect of noise on rate: User density λ u =.1 m 2, α = 4.5 and σ 2 = W. very tight, as can e seen from Fig. 2. The lower ound Pc on transmit power P can e otained y comining (4) and (6) with the coverage constraint (1). With increasing ase station density, the operator can scale down the transmit power at a rate proportional to λ 1 α/2 (as shown in Fig. 3), while guaranteeing a certain minimum coverage. B. Minimum transmit power for target data rate The effect of noise on rate is shown in Fig. 4. The transmit power can decrease with increasing BS density. However, a minimum transmit power is required to comat the noise and provide average rate to satisfy the user demand or the maximum achievale rate, whichever is minimum. This constraint imposes a limit on the downlink transmit power, which is provided in the next lemma. Lemma 2. The minimum downlink transmit power that achieves a fraction 1 δ of the minimum of the target rate and maximum supported rate τ( )B u ( ) is given y Pr min{h(), k 2 }, (7) λ α/2 1
4 Spectral efficiency: τ (in ps/hz) Tightness of approximation used to evaluate spectral efficiency without approx, =1 3 m 2, α=4 with approx, =1 3 m 2, α=4 without approx, =1 4 m 2, α=4 with approx, =1 4 m 2, α=4 without approx, =1 4 m 2, α=3 with approx, =1 4 m 2, α= Transmit power: P (in dbm) Fig. 5: Tightness of numerical approximation e σ2 P rα (e t 1) [1 σ2 P rα (e t 1)] used to evaluate spectral efficiency: User density λ u =.1m 2, noise power σ 2 = W. Min power for target rate: P r * ( in dbm ) Min transmit power target rate vs ase station density =1Kps, α=3 =1Mps, α=3 =1Gps, α=3 =1Kps, α=4 =1Mps, α=4 =1Gps, α= Base station density λ (m 2 ) Fig. 6: Minimum transmit power for target rate: User density λ u =.1 m 2, total andwidth B = 2 MHz, Outage proaility δ =.25 and noise power σ 2 = W. where k 2 = σ2 g(α) δτ( ), h() = g(α) = π α/2 t> z> e z z α/2 σ 2 g(α) [τ( ) (1 δ){ λu B }], and (e t 1)e z{(et 1) 2/α (e t 1) 2/α 1 1+x α/2 dx} dtdz. Proof: The spectral efficiency τ(p ) has een derived in [3] as τ(p ) E[ln(1 + SINR)] = e πr 2 P r> t> e σ 2 λ rα (e t 1) L Ir (r α (e t 1))dt2π rdr, where L Ir (r α (e t 1)) is the Laplace transform of the interference I r evaluated at r α (e t 1), which is given y ( ) exp π r 2 (e t 1) 2/α 1 dx. (e t 1) 1 + x 2/α α/2 Since σ 2 is very small, we use the approximation e σ2 P rα (e t 1) (1 σ2 P rα (e t 1)) in (8). This approximation is tight as shown in Fig. 5. Sustituting π r 2 z in (8), we otain τ(p ) = τ( ) σ 2 g(α)p 1 λ α/2+1 + o(σ 2 ), σ 2, (9) where τ( ) is the spectral efficiency at zero noise or high power, which is independent of the BS density [3]. A fraction 1 δ of the users must achieve a data rate at least equal to the target rate or maximum achievale rate, whichever is (8) Rate ( in Mps ) Target rate and maximum acheivale rate Vs ase station density Maximum achievale rate Target rate or user demand Base station density: ( in m 2 ) Fig. 7: Maximum achievale rate and target rate (user demand): User density λ u =.1 m 2, α = 4, total andwidth B = 2 MHz and target rate (user demand) = 5 kps. minimum. This, in turn, imposes a constraint on the spectral efficiency τ(p ): { } RT τ(p ) (1 δ) min B u ( ), τ( ). (1) Comining (1) and (9) we get a lower ound P r on the transmit power P. Depending on the target data rate, the transmit power of the ase stations can e scaled down (under target data rate constraint) with the increase of small cell density, at a rate proportional to λ 1 α 2 or faster than that as shown in Fig. 6. IV. OPTIMUM BASE STATION DENSITY To ensure QoS (oth coverage and user demand rate), the minimum downlink transmit power should e P m=max(p c, P r ). So, the total power consumption y a ase station is P m + P. The area power consumption follows as Λ 1 Λ 2
5 P A ( ) = (P m + P ) = (max(p c, P r ) + P ). Using (3) and (7), we get P A ( ) = max{k 1, min{h( ), k 2 }} λ α/2 2 + P. (11) The optimal λ is then otained y setting dp A d =. Theorem 1. When α > 4, the optimum ase station density λ that minimizes the area power consumption under coverage and rate constraints is given y where and [ (α 4) max{k1, k 2 } ] 2 λ = 2P [ (α 4)k1 ] 2 2P R 2, R 1, [ (α 4)(Bτ( )) α/2 1 ] 2 k 1 R 1 =, 2P λ α/2 1 u [ (α 4)(Bτ( )) α/2 1 ] 2 max{k 1, k 2 } R 2 =. 2P λ α/2 1 u Proof: We egin the proof y considering 3 different ranges of. We define Λ 1 = λ u Bτ( ) and Λ k 2 = 1c 2 k 1τ( ) c 1. λ We also rewrite h( ) as h( ) = c 1 τ( ) c 2. The demand rate is at least equal to the maximum achievale rate B u ( )τ( ) only when (, Λ 1 ], as can e seen in Fig. 7. Rearranging the expressions of h( ), Λ 1, Λ 2 and k 2 we get k 2 (, Λ 1 ], min{h( ), k 2 } = h( ) (Λ 1, Λ 2 ), h( ) [Λ 2, ). Rearranging the expressions of Λ 2, k 1 and h( ), it can e shown that, k 1 h( ) only for [Λ 2, ). Therefore, the area power consumption given in (11) can e rewritten as max{k 1, k 2 } + P λ α/2 2 (, Λ 1 ], h( ) P A = + P (Λ 1, Λ 2 ), (12) λ α/2 2 k 1 + P λ α/2 2 [Λ 2, ). Area power consumption: P A (in W/m 2 ) Area power consumption: analytical and simulation results α=6 α=5 α=4 α= Base station density: λ (in m 2 ) 1 Fig. 8: Area power consumption: Target rate = 5Mps, SINR threshold θ = 12 db, User density λ u =.1 m 2, Outage proaility ɛ =.25, Rate outage δ =.25, P = 1.5 W and noise power σ 2 = W. The dashed lines correspond to our analysis while the old lines are otained y Monte Carlo simulation. Rearranging the expressions of R 1, R 2, Λ 1, Λ 2 and using the When R 1 < < R 2, the optimal ase station density λ is derivative of (12), for α > 4, we otain the solution to the following equation dp A (Λ 1 ) λ α/2 2 (2P c 2 2) λ α/2 1 (4P τ( )c 2 ) + 2λ α/2 P (τ( )) 2 R 2, d dp A (Λ 1 ) λ <, dp A(Λ 2 ) > R ((α 4)c 1 τ( )) + ((α 6)c 1 c 2 ) =, 1 < < R 2, d d dp where c 1 = σ 2 g(α), c 2 = (1 δ) λ u A (Λ 2 ) R B. There exists no T R 1. d optimum density for α 4. Therefore for α > 4, (, Λ 1 ] R 2, λ (Λ 1, Λ 2 ) R 1 < < R 2, (13) [Λ 2, ) R 1. Now, for α > 4, optimal ase station density λ can e otained y setting the derivative of (12) to zero. When α 4, the total area transmit power consumption Pm as well as the total area fixed power consumption P increase with ase station density, for (, Λ 1 ] and [Λ 2, ), see (12). Hence, in this case, there exists no optimal density. The area power consumption always increases with ase station density for α = 3 and α = 4 as can e seen in Fig. 8. The simulation curves are otained using the Monte Carlo method. This result indicates that cell size shrinking or dense small cell deployment is not powerefficient for these values of the path loss exponent. Fig. 8 also shows the area power consumption and the optimal ase station densities for α = 5 and α = 6. This result indicates that small cell deployment can e power efficient in uran areas (where it is possile that α > 4).
6 V. CONCLUSION In this paper, we have derived a lower ound on downlink transmit power in a homogeneous Poisson networks under target coverage and target data rate constraints. For a path loss exponent α > 4, we have proven the existence of an optimal deployment density after which further reduction of the cell size is not efficient from an energy viewpoint. Our results indicate that, for α 4, counterintuitively, smaller BS densities lead to improved area power consumption. In other words, increasing the BS density is not green for α 4. REFERENCES [1] M. Haenggi, Stochastic geometry for wireless networks. Camridge University Press, 212. [2] V. Chandrasekhar, J. Andrews, and A. Gatherer, Femtocell networks: a survey, Communications Magazine, IEEE, vol. 46, no. 9, pp , 28. [3] J. G. Andrews, F. Baccelli, and R. K. Ganti, A tractale approach to coverage and rate in cellular networks, IEEE Trans. on Communications, vol. 59, no. 11, pp , Nov [4] D. Cao, S. Zhou, and Z. Niu, Optimal ase station density for energyefficient heterogeneous cellular networks, International Conference on Communications (ICC), IEEE, pp , 212. [5] T. Q. Quek, W. C. Cheung, and M. Kountouris, Energy efficiency analysis of twotier heterogeneous networks, in 11th European Wireless Conference 211Sustainale Wireless Technologies (European Wireless). VDE, 211, pp [6] X. Wang, A. V. Vasilakos, M. Chen, Y. Liu, and T. T. Kwon, A survey of green moile networks: Opportunities and challenges, Moile Networks and Applications, vol. 17, no. 1, pp. 4 2, 212. [7] F. Richter and G. Fettweis, Cellular moile network densification utilizing micro ase stations, in International Conference on Communications (ICC). IEEE, 21, pp [8] F. Richter, A. J. Fehske, P. Marsch, and G. P. Fettweis, Traffic demand and energy efficiency in heterogeneous cellular moile radio networks, in 71st Vehicular Technology Conference (VTC). IEEE, 21Spring, pp. 1 6.
How performance metrics depend on the traffic demand in large cellular networks
How performance metrics depend on the traffic demand in large cellular networks B. B laszczyszyn (Inria/ENS) and M. K. Karray (Orange) Based on joint works [1, 2, 3] with M. Jovanovic (Orange) Presented
More informationOptimizing the SINR operating point of spatial networks
Optimizing the SIR operating point of spatial networks ihar Jindal ECE Department University of Minnesota nihar@umn.edu Jeffrey G. Andrews ECE Department University of Texas at Austin jandrews@ece.utexas.edu
More informationALOHA Performs DelayOptimum Power Control
ALOHA Performs DelayOptimum Power Control Xinchen Zhang and Martin Haenggi Department of Electrical Engineering University of Notre Dame Notre Dame, IN 46556, USA {xzhang7,mhaenggi}@nd.edu Abstract As
More informationA Handover Scheme Towards Downlink Traffic Load Balance in Heterogeneous Cellular Networks
A Handover Scheme Towards Downlink Traffic Load Balance in Heterogeneous Cellular Networks Fei Liu, Petri Mähönen, and Marina Petrova Institute for Networked Systems, RWTH Aachen University Kackertstrasse
More informationSystem Design in Wireless Communication. Ali Khawaja
System Design in Wireless Communication Ali Khawaja University of Texas at Dallas December 6, 1999 1 Abstract This paper deals with the micro and macro aspects of a wireless system design. With the growing
More informationPhysical Layer Security in Cellular Networks: A Stochastic Geometry Approach
Physical Layer Security in Cellular Networks: A Stochastic Geometry Approach He Wang, Student Member, IEEE, Xiangyun Zhou, Member, IEEE, Mark C. Reed, Senior Member, IEEE arxiv:33.69v [cs.it] 7 Mar 23
More informationOn the Traffic Capacity of Cellular Data Networks. 1 Introduction. T. Bonald 1,2, A. Proutière 1,2
On the Traffic Capacity of Cellular Data Networks T. Bonald 1,2, A. Proutière 1,2 1 France Telecom Division R&D, 3840 rue du Général Leclerc, 92794 IssylesMoulineaux, France {thomas.bonald, alexandre.proutiere}@francetelecom.com
More informationA! Aalto University Comnet
NETS2020 Project Task #2.3: Selforganization in Dynamic Networks Olav Tirkkonen, Jyri Hämäläinen 1 Content Subtask #2.3.1: Convergence of Distributed Network Algorithms: The project outcome Subtask #2.3.2:
More informationSubscriber Maximization in CDMA Cellular Networks
CCCT 04: INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATIONS, AND CONTROL TECHNOLOGIES 234 Subscriber Maximization in CDMA Cellular Networks Robert AKL Department of Computer Science and Engineering
More informationA Traffic Load Balancing Framework for SoftwareDefined Radio Access Networks Powered by Hybrid Energy Sources
A Traffic Load Balancing Framework for SoftwareDefined Radio Access Networks Powered by Hybrid Energy Sources 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained
More informationEE4367 Telecom. Switching & Transmission. Prof. Murat Torlak
Path Loss Radio Wave Propagation The wireless radio channel puts fundamental limitations to the performance of wireless communications systems Radio channels are extremely random, and are not easily analyzed
More informationA Traffic Load Balancing Framework for SoftwareDefined Radio Access Networks Powered by Hybrid Energy Sources
A Traffic Load Balancing Framework for SoftwareDefined Radio Access Networks Powered by Hybrid Energy Sources 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained
More informationADHOC RELAY NETWORK PLANNING FOR IMPROVING CELLULAR DATA COVERAGE
ADHOC RELAY NETWORK PLANNING FOR IMPROVING CELLULAR DATA COVERAGE Hungyu Wei, Samrat Ganguly, Rauf Izmailov NEC Labs America, Princeton, USA 08852, {hungyu,samrat,rauf}@neclabs.com Abstract Nonuniform
More informationAn Algorithm for Automatic Base Station Placement in Cellular Network Deployment
An Algorithm for Automatic Base Station Placement in Cellular Network Deployment István Törős and Péter Fazekas High Speed Networks Laboratory Dept. of Telecommunications, Budapest University of Technology
More informationSURVEY OF LTE AND LTE ADVANCED SYSTEM
IMPACT: International Journal of Research in Engineering & Technology (IMPACT: IJRET) ISSN(E): 23218843; ISSN(P): 23474599 Vol. 2, Issue 5, May 2014, 16 Impact Journals SURVEY OF LTE AND LTE ADVANCED
More informationRethinking MIMO for Wireless Networks: Linear Throughput Increases with Multiple Receive Antennas
Rethinking MIMO for Wireless Networks: Linear Throughput Increases with Multiple Receive Antennas Nihar Jindal ECE Department University of Minnesota nihar@umn.edu Jeffrey G. Andrews ECE Department University
More informationSpatial Stochastic Models and Metrics for the Structure of Base Stations in Cellular Networks
Spatial Stochastic Models and Metrics for the Structure of Base Stations in Cellular Networks Anjin Guo, Student Member, IEEE and Martin Haenggi, Senior Member, IEEE Abstract The spatial structure of base
More informationMOBILE CONVERGED NETWORKS: FRAMEWORK, OPTIMIZATION, AND CHALLENGES
GE_LAYOUT_Layout 12/1/14 2:4 PM Page 2 MOBILE C O N V E RGED N E T W O RKS MOBILE CONVERGED NETWORKS: FRAMEWORK, OPTIMIZATION, AND CHALLENGES TAO HAN, YANG YANG, XIAOHU GE, AND GUOQIANG MAO Tao Han and
More informationAn Interference Avoiding Wireless Network Architecture for Coexistence of CDMA 2000 1x EVDO and LTE Systems
ICWMC 211 : The Seventh International Conference on Wireless and Mobile Communications An Interference Avoiding Wireless Network Architecture for Coexistence of CDMA 2 1x EVDO and LTE Systems Xinsheng
More informationDesigning Wireless Broadband Access for Energy Efficiency
Designing Wireless Broadband Access for Energy Efficiency Are Small Cells the Only Answer? Emil Björnson 1, Luca Sanguinetti 2,3, Marios Kountouris 3,4 1 Linköping University, Linköping, Sweden 2 University
More informationPolitecnico di Milano Advanced Network Technologies Laboratory
Politecnico di Milano Advanced Network Technologies Laboratory Energy and Mobility: Scalable Solutions for the Mobile Data Explosion Antonio Capone TIA 2012 GreenTouch Open Forum June 6, 2012 Energy consumption
More informationPlanning of UMTS Cellular Networks for Data Services Based on HSDPA
Planning of UMTS Cellular Networks for Data Services Based on HSDPA Diana Ladeira, Pedro Costa, Luís M. Correia 1, Luís Santo 2 1 IST/IT Technical University of Lisbon, Lisbon, Portugal 2 Optimus, Lisbon,
More informationProposal of Adaptive Downlink Modulation Using OFDM and MCCDMA for Future Mobile Communications System
Proposal of Adaptive Downlink Modulation Using and MCCDMA for Future Mobile Communications System Masato FURUDATE, Takeo OHSEKI, Hiroyasu ISHIKAWA, and Hideyuki SHINONAGA YRP Research Center, KDDI R&D
More informationSmart Mobility Management for D2D Communications in 5G Networks
Smart Mobility Management for D2D Communications in 5G Networks Osman N. C. Yilmaz, Zexian Li, Kimmo Valkealahti, Mikko A. Uusitalo, Martti Moisio, Petteri Lundén, Carl Wijting Nokia Research Center Nokia
More informationPacket Queueing Delay in Wireless Networks with Multiple Base Stations and Cellular Frequency Reuse
Packet Queueing Delay in Wireless Networks with Multiple Base Stations and Cellular Frequency Reuse Abstract  Cellular frequency reuse is known to be an efficient method to allow many wireless telephone
More informationInterCell Interference Coordination (ICIC) Technology
InterCell Interference Coordination (ICIC) Technology Dai Kimura Hiroyuki Seki Long Term Evolution (LTE) is a promising standard for nextgeneration cellular systems targeted to have a peak downlink bit
More informationDownlink MultiAntenna Heterogeneous Cellular Network with Load Balancing
Downlin MultiAntenna Heterogeneous Cellular Networ with Load Balancing Abhishe K. Gupta, Harpreet S. Dhillon, Member, IEEE, Sriram Vishwanath, Senior Member, IEEE, Jeffrey G. Andrews, Fellow, IEEE Abstract
More informationWhite Paper: Microcells A Solution to the Data Traffic Growth in 3G Networks?
White Paper: Microcells A Solution to the Data Traffic Growth in 3G Networks? By Peter Gould, Consulting Services Director, Multiple Access Communications Limited www.macltd.com May 2010 Microcells were
More informationLoad Balancing in Cellular Networks with Userintheloop: A Spatial Traffic Shaping Approach
WC25 Userintheloop: A Spatial Traffic Shaping Approach Ziyang Wang, Rainer Schoenen,, Marc StHilaire Department of Systems and Computer Engineering Carleton University, Ottawa, Ontario, Canada Sources
More information1 Lecture Notes 1 Interference Limited System, Cellular. Systems Introduction, Power and Path Loss
ECE 5325/6325: Wireless Communication Systems Lecture Notes, Spring 2015 1 Lecture Notes 1 Interference Limited System, Cellular Systems Introduction, Power and Path Loss Reading: Mol 1, 2, 3.3, Patwari
More informationPower Consumption Modeling of Different Base Station Types in Heterogeneous Cellular Networks
Future Network and MobileSummit 2010 Conference Proceedings Paul Cunningham and Miriam Cunningham (Eds) IIMC International Information Management Corporation, 2010 ISBN: 9781905824168 Power Consumption
More informationCloud Radios with Limited Feedback
Cloud Radios with Limited Feedback Dr. Kiran Kuchi Indian Institute of Technology, Hyderabad Dr. Kiran Kuchi (IIT Hyderabad) Cloud Radios with Limited Feedback 1 / 18 Overview 1 Introduction 2 Downlink
More informationA Novel Decentralized Time Slot Allocation Algorithm in Dynamic TDD System
A Novel Decentralized Time Slot Allocation Algorithm in Dynamic TDD System Young Sil Choi Email: choiys@mobile.snu.ac.kr Illsoo Sohn Email: sohnis@mobile.snu.ac.kr Kwang Bok Lee Email: klee@snu.ac.kr Abstract
More informationLogLikelihood Ratiobased Relay Selection Algorithm in Wireless Network
Recent Advances in Electrical Engineering and Electronic Devices LogLikelihood Ratiobased Relay Selection Algorithm in Wireless Network Ahmed ElMahdy and Ahmed Walid Faculty of Information Engineering
More informationIEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 55, NO. 1, JANUARY 2007 341
IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 55, NO. 1, JANUARY 2007 341 Multinode Cooperative Communications in Wireless Networks Ahmed K. Sadek, Student Member, IEEE, Weifeng Su, Member, IEEE, and K.
More informationPerformance of TDCDMA systems during crossed slots
Performance of TDCDMA systems during s Jad NASREDDINE and Xavier LAGRANGE Multimedia Networks and Services Department, GET / ENST de Bretagne 2 rue de la châtaigneraie, CS 1767, 35576 Cesson Sévigné Cedex,
More informationThe Vertical Handoff Algorithm using Fuzzy Decisions in Cellular Phone Networks
International Journal of Electronics Engineering, 2(), 200, pp. 2934 The Vertical Handoff Algorithm using Fuzzy Decisions in Cellular Phone Networks Chandrashekhar G.Patil & R.D.Kharadkar 2 Department
More informationSoftware Reliability Measuring using Modified Maximum Likelihood Estimation and SPC
Software Reliaility Measuring using Modified Maximum Likelihood Estimation and SPC Dr. R Satya Prasad Associate Prof, Dept. of CSE Acharya Nagarjuna University Guntur, INDIA K Ramchand H Rao Dept. of CSE
More informationA Traffic Load Balancing Framework for Softwaredefined Radio Access Networks Powered by Hybrid Energy Sources
arxiv:47.778v3 [cs.ni] 6 Dec 4 A Traffic Load Balancing Framework for Softwaredefined Radio Access Networks Powered by Hybrid Energy Sources Tao Han, Student Member, IEEE, and Nirwan Ansari, Fellow, IEEE
More informationDynamic Reconfiguration & Efficient Resource Allocation for Indoor Broadband Wireless Networks
Dynamic Reconfiguration & Efficient Resource Allocation for Indoor Broadband Wireless Networks Tim Farnham, Brian Foxon* Home Communications Department HP Laboratories Bristol HPL98123 June, 1998 broadband,
More informationNonLinear Regression 20062008 Samuel L. Baker
NONLINEAR REGRESSION 1 NonLinear Regression 20062008 Samuel L. Baker The linear least squares method that you have een using fits a straight line or a flat plane to a unch of data points. Sometimes
More informationMultiservice Load Balancing in a Heterogeneous Network with Vertical Handover
1 Multiservice Load Balancing in a Heterogeneous Network with Vertical Handover Jie Xu, Member, IEEE, Yuming Jiang, Member, IEEE, and Andrew Perkis, Member, IEEE Abstract In this paper we investigate
More informationMULTIHOP cellular networks have been proposed as an
1206 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 22, NO. 7, SEPTEMBER 2004 On the Throughput Enhancement of the Downstream Channel in Cellular Radio Networks Through Multihop Relaying Jaeweon
More informationPerformance Improvement of DSCDMA Wireless Communication Network with Convolutionally Encoded OQPSK Modulation Scheme
International Journal of Advances in Engineering & Technology, Mar 0. IJAET ISSN: 3963 Performance Improvement of DSCDMA Wireless Communication Networ with Convolutionally Encoded OQPSK Modulation Scheme
More informationAN AMBIENT AIR QUALITY MONITORING NETWORK FOR BUENOS AIRES CITY
AN AMBIENT AIR QUALITY MONITORING NETWORK FOR BUENOS AIRES CITY Laura Venegas and Nicolás Mazzeo National Scientific and Technological Research Council (CONICET) Department of Atmospheric and Oceanic Sciences.
More informationEnergy Efficiency Metrics for LowPower Near Ground Level Wireless Sensors
Energy Efficiency Metrics for LowPower Near Ground Level Wireless Sensors Jalawi Alshudukhi 1, Shumao Ou 1, Peter Ball 1, Liqiang Zhao 2, Guogang Zhao 2 1. Department of Computing and Communication Technologies,
More informationAssessment of Cellular Planning Methods for GSM
Assessment of Cellular Planning Methods for GSM Pedro Assunção, Rui Estevinho and Luis M. Correia Instituto das Telecomunicações/Instituto Superior Técnico, Technical University of Lisbon Av. Rovisco Pais,
More informationInvestigations into Relay Deployments within the LTE Advanced Framework
Investigations into Relay Deployments within the LTE Advanced Framework Abdallah Bou Saleh, Ömer Bulakci PhD candidates of Helsinki University of Technology (TKK) @ Nokia Siemens Networks 18.02.2010 1
More informationCooperative Communication for Spatial Frequency Reuse Multihop Wireless Networks under Slow Rayleigh Fading
his full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 211 proceedings Cooperative Communication for Spatial Frequency
More information5G UltraDense Cellular Networks
5G UltraDense Cellular Networks Xiaohu Ge 1, Senior Member, IEEE, Song Tu 1, Guoqiang Mao 2, Senior Member, IEEE, ChengXiang Wang 3, Senior Member, IEEE, Tao Han 1, Member, IEEE 1 Department of Electronics
More informationCHAPTER 13 SIMPLE LINEAR REGRESSION. Opening Example. Simple Regression. Linear Regression
Opening Example CHAPTER 13 SIMPLE LINEAR REGREION SIMPLE LINEAR REGREION! Simple Regression! Linear Regression Simple Regression Definition A regression model is a mathematical equation that descries the
More informationOptimized Mobile Connectivity for Bandwidth Hungry, DelayTolerant Cloud Services toward 5G
Optimized Mobile Connectivity for Bandwidth Hungry, DelayTolerant Cloud Services toward 5G Osman N. C. Yilmaz 1, 2, Carl Wijting 1, Petteri Lundén 1, Jyri Hämäläinen 2 1 Nokia Research Center, 2 Aalto
More informationIntercell Interference Mitigation Reduction in LTE Using Frequency Reuse Scheme
International Journal of Scientific and Research Publications, Volume 5, Issue 8, August 2015 1 Intercell Interference Mitigation Reduction in LTE Using Frequency Reuse Scheme Rupali Patil 1, R.D.Patane
More informationHeterogeneous Networks: a Big Data Perspective
Heterogeneous Networks: a Big Data Perspective Arash Behboodi October 26, 2015 Institute for Theoretical Information Technology Prof. Dr. Rudolf Mathar RWTH Aachen University Wireless Communication: History
More informationThe Economic & Sustainability Future of Cellular Networks Year 3 Individual Project. University ID: 1016557, [Pick the date] 1 P a g e 1 016 557
The Economic & Sustainability Future of Cellular Networks Year 3 Individual Project University ID: 1016557, [Pick the date] 1 P a g e 1 016 557 Acknowledgements This project would not have been possible
More informationAn Efficient Hybrid Data Gathering Scheme in Wireless Sensor Networks
An Efficient Hybrid Data Gathering Scheme in Wireless Sensor Networks Ayon Chakraborty 1, Swarup Kumar Mitra 2, and M.K. Naskar 3 1 Department of CSE, Jadavpur University, Kolkata, India 2 Department of
More informationComparison of Network Coding and NonNetwork Coding Schemes for Multihop Wireless Networks
Comparison of Network Coding and NonNetwork Coding Schemes for Multihop Wireless Networks JiaQi Jin, Tracey Ho California Institute of Technology Pasadena, CA Email: {jin,tho}@caltech.edu Harish Viswanathan
More informationOligopoly Games under Asymmetric Costs and an Application to Energy Production
Oligopoly Games under Asymmetric Costs and an Application to Energy Production Andrew Ledvina Ronnie Sircar First version: July 20; revised January 202 and March 202 Astract Oligopolies in which firms
More informationChannel assignment for GSM halfrate and fullrate traffic
Computer Communications 23 (2000) 476 482 www.elsevier.com/locate/comcom Channel assignment for GSM halfrate and fullrate traffic P. Lin, Y.B. Lin* Department of Computer Science and Information Engineering,
More informationA MultiPoller based EnergyEfficient Monitoring Scheme for Wireless Sensor Networks
A MultiPoller based EnergyEfficient Monitoring Scheme for Wireless Sensor Networks Changlei Liu and Guohong Cao Department of Computer Science & Engineering The Pennsylvania State University Email:
More informationLoad Balanced OpticalNetworkUnit (ONU) Placement Algorithm in WirelessOptical Broadband Access Networks
Load Balanced OpticalNetworkUnit (ONU Placement Algorithm in WirelessOptical Broadband Access Networks Bing Li, Yejun Liu, and Lei Guo Abstract With the broadband services increasing, such as video
More informationAnalysis of Macro  Femtocell Interference and Implications for Spectrum Allocation
Analysis of Macro  Femtocell Interference and Implications for Spectrum Allocation Juan Espino, Jan Markendahl, Aurelian Bria Wireless@KTH, The Royal institute of Technology, Electrum 48, SE4 4 Kista,
More informationOptimal load balancing algorithm for multicell LTE networks
International Journal of Wireless Communications and Mobile Computing 2014; 2(2): 2329 Published online March 10, 2014 (http://www.sciencepublishinggroup.com/j/wcmc) doi: 10.11648/j.wcmc.20140202.11 Optimal
More informationCourse 4 Examination Questions And Illustrative Solutions. November 2000
Course 4 Examination Questions And Illustrative Solutions Novemer 000 1. You fit an invertile firstorder moving average model to a time series. The lagone sample autocorrelation coefficient is 0.35.
More informationRadio Resource Allocation Algorithm for Relay aided Cellular OFDMA System
Radio Resource Allocation Algorithm for Relay aided Cellular OFDMA System Megumi Kaneo # and Petar Popovsi Center for TeleInFrastructure (CTIF), Aalborg University Niels Jernes Vej 1, DK90 Aalborg, Denmar
More informationCommunication in wireless ad hoc networks
Communication in wireless ad hoc networks a stochastic geometry model Institute for Applied and Numerical Mathematics RG Numerical Simulation, Optimization and High Performance Computing 0 Aug. 14 2013
More informationCooperative Access Class Barring for MachinetoMachine Communications
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 11, NO. 1, JANUARY 2012 27 Cooperative Access Class Barring for MachinetoMachine Communications ShaoYu Lien, TzuHuan Liau, ChingYueh Kao, and KwangCheng
More informationEnergy Efficiency of Cooperative Jamming Strategies in Secure Wireless Networks
Energy Efficiency of Cooperative Jamming Strategies in Secure Wireless Networks Mostafa Dehghan, Dennis L. Goeckel, Majid Ghaderi, and Zhiguo Ding Department of Electrical and Computer Engineering, University
More informationFull Duplex DevicetoDevice Communication in Cellular Networks
Full Duplex DevicetoDevice Communication in Cellular Networks Samad Ali Email: saali@ee.oulu.fi Nandana Rajatheva Email: rrajathe@ee.oulu.fi Matti Latvaaho Email: matla@ee.oulu.fi Abstract In this paper
More informationImproved Algorithm for Throughput Maximization in MCCDMA
Improved Algorithm for Throughput Maximization in MCCDMA Hema Kale 1, C.G. Dethe 2 and M.M. Mushrif 3 1 ETC Department, Jhulelal Institute of Technology Nagpur, India. Email: hema.kale72@gmail.com 2 ECE
More informationMultiDimensional OFDMA Scheduling in a Wireless Network with Relay Nodes
MultiDimensional OFDMA Scheduling in a Wireless Network with Relay Nodes Reuven Cohen Guy Grebla Department of Computer Science Technion Israel Institute of Technology Haifa 32000, Israel Abstract LTEadvanced
More informationImpact of Remote Control Failure on Power System Restoration Time
Impact of Remote Control Failure on Power System Restoration Time Fredrik Edström School of Electrical Engineering Royal Institute of Technology Stockholm, Sweden Email: fredrik.edstrom@ee.kth.se Lennart
More informationCooperation Strategies for Two Colocated receivers, with no CSI at the transmitter
Cooperation Strategies for Two Colocated receivers, with no CSI at the transmitter Amichai Sanderovich, Avi Steiner and Shlomo Shamai (Shitz) Technion, Haifa, Israel Email: {amichi@tx,savi@tx,sshlomo@ee}.technion.ac.il
More informationWireless Link Quality Modelling and Mobility Management Optimisation for Cellular Networks
Wireless Link Quality Modelling and Mobility Management Optimisation for Cellular Networks PhD Thesis Defence Van Minh Nguyen Paris, June 20 th 2011 Interference Link quality expressed in SINR Resource
More informationCOMPATIBILITY STUDY FOR UMTS OPERATING WITHIN THE GSM 900 AND GSM 1800 FREQUENCY BANDS
Electronic Communications Committee (ECC) within the European Conference of Postal and Telecommunications Administrations (CEPT) COMPATIBILITY STUDY FOR UMTS OPERATING WITHIN THE GSM 900 AND GSM 1800 FREQUENCY
More informationActuarial Present Values of Annuities under Stochastic Interest Rate
Int. Journal of Math. Analysis, Vol. 7, 03, no. 59, 9399 HIKARI Ltd, www.mhikari.com http://d.doi.org/0.988/ijma.03.3033 Actuarial Present Values of Annuities under Stochastic Interest Rate Zhao Xia
More informationQUADRATIC EQUATIONS EXPECTED BACKGROUND KNOWLEDGE
MODULE  1 Quadratic Equations 6 QUADRATIC EQUATIONS In this lesson, you will study aout quadratic equations. You will learn to identify quadratic equations from a collection of given equations and write
More information10.1 Systems of Linear Equations: Substitution and Elimination
726 CHAPTER 10 Systems of Equations and Inequalities 10.1 Systems of Linear Equations: Sustitution and Elimination PREPARING FOR THIS SECTION Before getting started, review the following: Linear Equations
More informationFemtoHaul: Using Femtocells with Relays to Increase Macrocell Backhaul Bandwidth
FemtoHaul: Using Femtocells with Relays to Increase Macrocell Backhaul Bandwidth Ayaskant Rath, Sha Hua and Shivendra S. Panwar Dept. of Electrical and Computer Engineering, Polytechnic Institute of NYU,
More informationAdmission Control for Variable Spreading Gain CDMA Wireless Packet Networks
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 49, NO. 2, MARCH 2000 565 Admission Control for Variable Spreading Gain CDMA Wireless Packet Networks TsernHuei Lee, Senior Member, IEEE, and Jui Teng Wang,
More informationEnergy Efficient Load Balancing among Heterogeneous Nodes of Wireless Sensor Network
Energy Efficient Load Balancing among Heterogeneous Nodes of Wireless Sensor Network Chandrakant N Bangalore, India nadhachandra@gmail.com Abstract Energy efficient load balancing in a Wireless Sensor
More informationEvolution in Mobile Radio Networks
Evolution in Mobile Radio Networks Multiple Antenna Systems & Flexible Networks InfoWare 2013, July 24, 2013 1 Nokia Siemens Networks 2013 The thirst for mobile data will continue to grow exponentially
More informationA Power Efficient QoS Provisioning Architecture for Wireless Ad Hoc Networks
A Power Efficient QoS Provisioning Architecture for Wireless Ad Hoc Networks Didem Gozupek 1,Symeon Papavassiliou 2, Nirwan Ansari 1, and Jie Yang 1 1 Department of Electrical and Computer Engineering
More informationProbability, Mean and Median
Proaility, Mean and Median In the last section, we considered (proaility) density functions. We went on to discuss their relationship with cumulative distriution functions. The goal of this section is
More informationInterference in Large Wireless Networks. Contents
Foundations and Trends R in Networking Vol. 3, No. 2 (2008) 127 248 c 2009 M. Haenggi and R. K. Ganti DOI: 10.1561/1300000015 Interference in Large Wireless Networks By Martin Haenggi and Radha Krishna
More informationCharacterization of Ultra Wideband Channel in Data Centers
Characterization of Ultra Wideband Channel in Data Centers N. Udar 1,K.Kant 2,R.Viswanathan 1, and D. Cheung 2 1 Southern Illinois University, Carbondale, IL 2 Intel Corporation, Hillsboro, OR Abstract.
More informationBalancing Network Traffic Load in Geographic Hash Table (GHT)
Balancing Network Traffic Load in Geographic Hash Table (GHT) R. Asha, V.Manju, Meka Sindhu & T. Subha Department of Information Technology, Sri Sai Ram Engineering College, Chennai. Email : ashaniteesh@gmail.com,
More informationMAXIMIZING THE CAPACITY OF WIRELESS NETWORKS USING MULTICELL ACCESS SCHEMES
MAXIMIZIN HE CAPACIY OF WIRELESS NEWORKS USIN MULICELL ACCESS SCHEMES Jan Egil Kirkebø Department of Informatics University of Oslo, P.O. Box 1080, N 0316 Oslo, Norway Email: janki@ifi.uio.no David esbert,
More informationCoverage Related Issues in Networks
Coverage Related Issues in Networks Marida Dossena* 1 1 Department of Information Sciences, University of Naples Federico II, Napoli, Italy Email: marida.dossena@libero.it Abstract Wireless sensor networks
More informationDynamic Load Balancing Through Coordinated Scheduling in Packet Data Systems
Dynamic Load Balancing Through Coordinated Scheduling in Packet Data Systems Suman Das, Harish Viswanathan, Gee Rittenhouse Wireless Technology Research Lucent Technologies, Bell Labs. 6 Mountain Ave,
More informationWhite paper. Mobile broadband with HSPA and LTE capacity and cost aspects
White paper Mobile broadband with HSPA and LTE capacity and cost aspects Contents 3 Radio capacity of mobile broadband 7 The cost of mobile broadband capacity 10 Summary 11 Abbreviations The latest generation
More informationInterference Analysis of a Total Frequency Hopping GSM Cordless Telephony System 1
Interference Analysis of a Total Frequency Hopping GSM Cordless Telephony System 1 Jürgen Deißner, André Noll Barreto, Ulrich Barth*, and Gerhard Fettweis Endowed Chair for Mobile Communications Systems
More informationSmallCell Wireless Backhauling
SmallCell Wireless Backhauling A NonLineofSight Approach for PointtoPoint Microwave Links M. Coldrey*, H. Koorapaty**, J.E. Berg***, Z. Ghebretensaé***, J. Hansryd****, A. Derneryd*, S. Falahati***
More informationDynamic Load Balancing in 3GPP LTE MultiCell Networks with Heterogenous Services
Dynamic Load Balancing in 3GPP LTE MultiCell Networks with Heterogenous Services Hao Wang 1,2, Lianghui Ding 2, Ping Wu 2, Zhiwen Pan 1, Nan Liu 1, Xiaohu You 1 1 National Mobile Communication Research
More informationCooperative Multiple Access for Wireless Networks: Protocols Design and Stability Analysis
Cooperative Multiple Access for Wireless Networks: Protocols Design and Stability Analysis Ahmed K. Sadek, K. J. Ray Liu, and Anthony Ephremides Department of Electrical and Computer Engineering, and Institute
More informationA Theoretical Framework for Incorporating Scenarios into Operational Risk Modeling
A Theoretical Framework for Incorporating Scenarios into Operational Risk Modeling Bakhodir A. Ergashev This Draft: January 31, 2011. First Draft: June 7, 2010 Astract In this paper, I introduce a theoretically
More informationCommunication on the Grassmann Manifold: A Geometric Approach to the Noncoherent MultipleAntenna Channel
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 48, NO. 2, FEBRUARY 2002 359 Communication on the Grassmann Manifold: A Geometric Approach to the Noncoherent MultipleAntenna Channel Lizhong Zheng, Student
More informationSupplement to Call Centers with Delay Information: Models and Insights
Supplement to Call Centers with Delay Information: Models and Insights Oualid Jouini 1 Zeynep Akşin 2 Yves Dallery 1 1 Laboratoire Genie Industriel, Ecole Centrale Paris, Grande Voie des Vignes, 92290
More informationCrosslayer Scheduling and Resource Allocation in Wireless Communication Systems
Crosslayer Scheduling and Resource Allocation in Wireless Communication Systems Srikrishna Bhashyam Department of Electrical Engineering Indian Institute of Technology Madras 21 June 2011 Srikrishna Bhashyam
More information