Resource Allocation Of Efficient Energy In OFDM Systems Using . - IRDP

1y ago
6 Views
1 Downloads
897.12 KB
8 Pages
Last View : 2m ago
Last Download : 2m ago
Upload by : Matteo Vollmer
Transcription

Engineering and Scientific International Journal (ESIJ)Volume 2, Issue 2, April - June 2015ISSN 2394-187(Online)ISSN 2394-7179 (Print)Resource Allocation of Efficient Energy in OFDM Systems usingDistributed AntennasSamatha Maditham¹, Dr. A.Rajendra Babu²¹ M.Tech (DECS),Department of ECE, BCETFW, KadapaSamatha.maditham@gmail.com² Associate professor, Department of ECE, BCETFW, Kadapaarajendrababuavula@gmail.comAbstract— We develop an energy-efficient resourceallocation scheme with proportional fairness for downlinkmultiuser orthogonal frequency-division multiplexing(OFDM) systems with distributed antennas. Our aim is tomaximize energy efficiency (EE) under the constraints ofthe overall transmit power of each remote access unit(RAU), proportional fairness data rates, and bit error rates(BERs). Because of the non convex nature of theoptimization problem, obtaining the optimal solution isextremely computationally complex. Therefore, wedevelop a low-complexity suboptimal algorithm, whichseparates subcarrier allocation and power allocation. Forthe low-complexity algorithm, we first allocate subcarriersby assuming equal power distribution. Then, by exploitingthe properties of fractional programming, we transform thenon convex optimization problem in fractional form into anequivalent optimization problem in subtractive form, whichincludes a tractable solution. Next, an optimal energyefficient power-allocation algorithm is developed tomaximize EE while maintaining proportional fairness.Through computer simulation, we demonstrate theeffectiveness of the proposed low-complexity algorithmand illustrate the fundamental tradeoff between energy andspectral-efficient transmission designs.Keywords— Distributed Antenna System (DAS), EnergyEfficiency (EE), Fractional Programming, ProportionalFairness, Resource Allocation, Spectral Efficiency (SE).1. Introduction1.1 Distributed Antenna SystemThe distributed antenna system (DAS) has beenproposed as a capable candidate for future wirelesscommunication systems due to its advantages of increasedcapacity, extended coverage, and improved link reliabilityIn the DAS, remote access units (RAUs) are geographicallyseparated and are connected to a baseband processing unitvia optical fibers. Thus, the DAS can decrease accessdistance, transmit power, and co-channel interference,which can progress system performance, mainly for thosemobile stations (MSs) near the edge of a cell. Therefore,DAS techniques have been paid intensive attention in thestandardization of the Third-Generation Partnership Project(3GPP) Long-Term Evolution (LTE), LTE-Advanced, andIEEE 802.16 Worldwide Interoperability for MicrowaveAccess (Wi MAX), where they are also referred to ascooperative multiple point techniques On the other hand,orthogonal frequency division multiplexing (OFDM) caneffectively combat multipath fading and has been used inor proposed for many wireless communication systems,such as 3GPP LTE-Advanced and Wi MAX. In an OFDMsystem, the maximum sum capacity can be achieved byfirst allocating each subcarrier to the user with highchannel gain and then by adjusting the correspondingtransmit power through water-filling.1.2 Energy EfficiencyIn recent years, energy efficiency (EE) has receivedmuch more attention due to steadily rising energyconsumption and environmental concerns It has beenreported in that information and communicationtechnology already contributes to around 2% of the globalcarbon dioxide emissions. Recently, the dramatic growth inhigh-rate multimedia data traffic driven by usage of smartAndroid and iPhone devices, tablets, eBook readers, andother wireless devices has been straining the capacity oftoday’s networks and has caused a large amount of energyconsumption.It has been anticipated that mobile traffic will growfurther by over 100 times in the next ten years. As a result,energy-efficient system design has recently drawn muchattention in both academic and industrial worlds, and isbecoming the mainstream for the next-generation ofwireless communications. Four EE-related trade-offs forwireless networks have been revealed .A general EE–spectral efficiency (SE) trade off framework in thedownlink OFDM networks has been addressed .EE designbased on cooperative relaying and cognitive radio has beendiscussed An optimal energy-efficient covariance matrixalgorithm for a multiple-input–multiple-output (MIMO)broadcast channel has been proposed .Energy-efficientpower-allocation and mode-selection methods in virtualMIMO systems have been proposed .We have comparedthe EE between distributed MIMO (D-MIMO) systems andcollocated MIMO (C-MIMO) systems and showed that DMIMO systems are more energy efficient than C-MIMO60

Engineering and Scientific International Journal (ESIJ)Volume 2, Issue 2, April - June 2015systems. We have demonstrated that a trade-off existsbetween EE and SE in a downlink DAS when proportionalfairness among MSs is considered. However, to the best ofour knowledge, there is no study about energy-efficientresource allocation with proportional fairness among MSsin OFDM with a DAS.Here, we exploit the fractional programming method toinvestigate energy-efficient resource allocation withproportional fairness over composite fading channelsconsisting of small- and large-scale fading for a downlinkmultiuser OFDM DAS. The optimization objective is tomaximize EE under the constraints of overall transmitpower of each RAU, proportional fairness data rates, andbit error rates (BER). Because of the non convex nature ofthe optimization problem, obtaining the optimal solution isextremely computationally complex. By exploiting theproperties of fractional programming, we transform thenon convex optimization problem in fractional form into anequivalent optimization problem in subtractive form,which include a tractable solution. Then, a low-complexitysuboptimal algorithm is developed to maximize EE whilemaintaining proportional fairness for the downlinkmultiuser OFDM DAS.In Section II, we first describe the multiuser OFDMDAS circuit and fiber optic power consumption models,and we then formulate the problem of energy-efficientresource-allocation optimization for the downlinkmultiuser OFDM DAS with proportional fairness. InSection III, a suboptimal energy-efficient resourceallocation scheme is developed. Numerical results arepresented to demonstrate the effectiveness of the proposedenergy-efficient resource-allocation scheme in Section IV.Section V concludes.2. Energy Efficiency of anFrequency-Division MultiplexingAntenna SystemISSN 2394-187(Online)ISSN 2394-7179 (Print)We consider the downlink of a multiuser OFDM DASin a single cell with N subcarriers, K MSs, and MRAUs; both MSs and RAUs are equipped with a singleantenna, as shown in Fig. 1. The base station (BS) canbe regarded as a special RAU and is denoted by RAU 1.The regular RAUs are equipped with only up/downconverters and low-noise amplifiers (LNAs). Each RAUis physically connected with BS/RAU 1 via an opticalfiber. We assume that channel state information (CSI) isavailable at both transmitter and receiver.The base station (BS) can be regarded as a specialAU and is denoted by RAU 1. The regular RAUs areequipped with only up/down converters and low-noiseamplifiers (LNAs). Each RAU is physically connectedwith BS/RAU 1 via an optical fiber. We assume thatchannel state information (CSI) is available at bothtransmitter and receiver. The SNR of MS k onsubcarrier n from RAU m can be expressed asmodeled asWhere gk, n, m denotes the small-scale fading of a wirelesschannel and is an independent and identically distributedcomplex Gaussian random variable for different k’s, n’s orm’s with zero mean and unit variance, and wk,,m denotesthe large-scale fading and is independent of gk, n, m. Thelarge-scale fading can be expressedOrthogonalDistributedAfter briefly discussing OFDM DAS and circuit andfiber-optic power consumption models, we introduce theEE of an OFDM DAS.2.1 OFDM DAS ModelWhere α is the path-loss exponent and is typicallybetween 3 and 5, dk, m denotes the distance from MS k toRAU m, c is the median of the mean path gain at referencedistance dk, m 1 km, and sk, m is a lognormal shadowfading variable, i.e., 10 log10sn, m is a zero-mean Gaussianrandom variable with standard deviation σsh.If continuousrate adaptation is used, the overall date rate or the SE ofMS k can be written asWhere β 1.5/(ln(5PBER)) is a constant for a specificprobability of a BER (PBER) requirement2.2 Circuit Power ConsumptionFig.1: Circular layout of the OFDM DAS configuration.To design energy-efficient communication systems, thetotal power consumption should be included in theoptimization model. It contains three parts: 1) the power61

Engineering and Scientific International Journal (ESIJ)Volume 2, Issue 2, April - June 2015consumption of amplifiers; 2) the circuit powerconsumption by RAUs; and 3) the power consumption bythe fiber-optic transmission, which can be expressed asThe circuit power consumption in the given equationincludes the power dissipation in the digital-to-analogconverter, the mixer, the active filters at the transmitterside, the frequency synthesizer, the LNAs, the intermediatefrequency amplifier, the active filters at the receiver side,and the analog-to-digital converter. Moreover, the circuitpower consumption is independent of the actual transmitpower.2.3 Circuit Power ConsumptionAs in most literature, the EE of an OFDM DAS isdefined as the ratio of the overall data rate or SE over thetotal power consumption (in bits/J/Hz) i.e.,Where R is the overall data rate or the SE, and it can bewritten asAs in most literature, the EE of an OFDM DAS is definedas the ratio of the overall data rate or SE over the totalpower consumption (in bits/J/Hz)2.4 EE OptimizationFrom (6), the objective of EE optimization for thedownlink multiuser OFDM DAS with proportional fairnesscan be expressed asISSN 2394-187(Online)ISSN 2394-7179 (Print)In our previous work, we exploited a multi criteriaoptimization method to get a Pareto optimal solution of EE.In this paper, we will obtain the optimal solution of the EEproblem according to the fractional programming theorydifferent from the EE problem, we take the frequencyselectivity of wireless channels into consideration in (7),which is more practical but is more complex.3. Energy-Efficient Resource AllocationHere, we will investigate the energy-efficient resourceallocation scheme for an OFDM DAS.3.1 Subcarrier AllocationThe optimization problem in (7) is non convex andcombinatorial and has nonlinear constraints. It isimpossible to get a closed-form solution. It is also verycomplicated to obtain a numerical solution. Therefore, wefocus on the low-complexity and suboptimal solution of(7). In this paper, we assume that the proportion ofsubcarriers assigned to each MS is approximately thesame as their data rates after power allocation, which hasbeen confirmed in According to the nature of theoptimization problem, we will first perform subcarrierallocation and then power allocation, as shown in thefollowing steps. Number of subcarriers per RAU. According to thelarge scale fading Wk,m in (2), calculate the accessprobability between MS k and RAU m. We first allocate the MSs and subcarriers to each RAU,and the remaining N unallocated subcarriers is thenassigned in a way to maximize the overall SE whilemaintaining rough proportionality by assuming equalpower allocation among the subcarriers. Table I showshow subcarriers are allocated, where K, N, and Marethe sets of MSs, subcarriers, and RAUs, respectively.Table I shows how subcarriers are allocated, where K,N, and M are the sets of MSs, subcarriers, and RAUs,respectively.Step (b) assigns the unallocated subcarriers and RAUsto each MS with high channel gain.Step (c) first finds the MS that has the least SE dividedby its proportionality constant and then assigns theunallocated subcarrier and RAU to each MS with highchannel gain.Step (d) assigns the remaining N unallocatedsubcarriers to the best MSs, Where in each MS can get atmost one unassigned subcarriers.3.2 Power Allocation for Each RAUIndicates that subcarrier n is assigned to MS k fromRAU m; otherwise, δk, n,m 0. Pmax m denotes themaximum transmit.After the MSs and the subcarriers have been determinedfor each RAU, we have the following energy - efficient62

Engineering and Scientific International Journal (ESIJ)Volume 2, Issue 2, April - June 2015ISSN 2394-187(Online)ISSN 2394-7179 (Print)Let F (ω) max p h(p, ω) and f (ω) arg max p h(p, ω). Ithas been proven that problems are equivalent to eachother if and only if F (ω ) 0 and f (ω ) p .That is, for any optimization problem with an objectivefunction in fractional form, there always exists anequivalent objective function in subtractive form. As aresult, we only need to focus on the equivalent objectivefunction .For k 1, the optimal solution can be obtainedbyoptimization:[x] is equal to 0 when x is less than zero; otherwise, it isequal to x. For k 2, the optimal solution can beTable 1: Subcarrier AllocationAnd ϑ(i) and η(i) are small positive step sizes for the ithiteration. The sub gradient updates of (12) and (13) areguaranteed to converge to the optimal λk for k 1 as longas ϑ(i) and η(i) are chosen to be sufficiently small. Forexample, ϑ(i) 0.1/ i.Table II shows the details of the optimal powerallocation algorithm.is the proportional fairness constants among MSs onRAU m. Define a new optimal problem asTable 2: Subgradient Power-Allocation Algorithmp { pk,n,m for k 1, 2, . . . , K, n 1, 2, . . . , N, m 1,2, . . . , M }.63

Engineering and Scientific International Journal (ESIJ)Volume 2, Issue 2, April - June 2015ISSN 2394-187(Online)ISSN 2394-7179 (Print)Table 4: Rate ConstraintsTable 3: Optimal Energy-Efficient Power-AllocationAlgorithmAfter the optimal solution for (9) is derived, we canobtain the optimal energy-efficient power allocation of (8),which is described in Table -III.Algorithm 1 will converge to the global optimal solutionfor sufficiently small positive step sizes for the ithiteration ϑ(i) and η(i), which can be proven in a similarmethod in our previous work .The convergence ofAlgorithm 2 has been proven.The low-complexity suboptimal solution developed herecan be summarized as follows.1) Determine the number of subcarriers initially assignedto each RAU by the algorithm2) Assign the MSs and the subcarriers to each RAUproportionally using the algorithm in Table I.3) For each RAU, assign the overall power p max,m for theselected subcarriers and MSs to maximize the EE whileenforcing proportional fairness using the algorithm.4. Numerical ResultsHere, the proposed energy-efficient resourceallocation scheme is evaluated via Monte Carlosimulations. In our simulation, the number of RAUs M 5 and subcarriers N 64. Noise power σz2 is 104 dBm,and the maximum power pmax,n is 36 dBm. Cell radius Ris 1 km, and the system BER requirement is 0.001.Circuit power consumption Pc is 40 dBm, and fiber-opticpower consumption Po is 0.6 dBm. Power amplifierefficiency τ 38%. Path-loss exponent α 3.7, and thestandard deviation of the shadow fading is σsh 8 dB[23]. The rate constraints are listed in Table IV.Fig.2: SE and EE versus number of iterations with differentrate constraints for p 30 dBm and K 10.For convenience, assume that the cell shape isapproximated by a circle of radius D. The polarcoordinates of RAUs relative to the center of the cell aredenoted as (d, θm), m 1, 2, . . ,M. We assume that theMSs are uniformly distributed in the cell. For the cell withfive RAUs, the polar coordinates of the RAUs are (0, 0),(d, 0), (d, π/2), (d, π), (d, 3π/2) Where d (3 3)D/2.64

Engineering and Scientific International Journal (ESIJ)Volume 2, Issue 2, April - June 2015ISSN 2394-187(Online)ISSN 2394-7179 (Print)4.1 Convergence of Algorithms 1 and 2Fig. 2 and 3 shows the evolution of Algorithms 1 and2 for different rate constraints defined in Table IV,respectively. The results in Figs. 2 and 3 are averagedover 100 000 channel realizations. In Figs. 2 and3,Algorithms 1 and 2 converge to the optimal valuewithin 70 and 3 iterations for the maximum transmitppower of each RAU max,m 30 dBm and the number ofthe MS K 10, respectively. The overall algorithm takesaround 210 iterations in total to converge.4.2 SE and EE Versus Different Transmit PowerFig. 4 compares the SE versus the total power ofRAUs for different rate constraints and resourceallocation methods. In this case, the SE of the proposedenergy-efficient resource-allocation scheme is better thanthe equal resource-allocation scheme. In Fig. 4, the gapbetween the proposed energy-efficient resource-allocationscheme and the equal resource-allocation scheme isbecoming smaller when the transmit power is increasing.The reason is that the CSI is not very good when thetransmit power is small, but when the transmit power isincreasing, the CSI is becoming better. Therefore, theperformance of the equal resource-allocation scheme isclose to the proposed energy-efficient resource-allocationscheme. As in Fig. 4, when rate constraint index k 0, theSE of the proposed energy-efficient resource-allocationscheme is approximately 91.8% higher than the equalresource-allocation scheme when the total power of RAUis 36 dBm.Above figure compares the EE versus SE for differentrate constraints and resource-allocation methods.Compared with equal resource allocation, the proposedenergy-efficient resource-allocation scheme outperformsthe equal resource-allocation scheme in terms of EE.When rate constraint index k 0, the EE of the sapproximately 169.3% higher than the equal resourceallocation scheme when SE is 5 bit/s/Hz.In Fig., the EE–SE curve shows the existence of asaturation point, beyond which the EE no longer increaseswith SE, regardless of how much additional transmitpower is used. Based on this result, we can design optimalenergy-efficient networks. On the other hand, we canreduce as much power consumption as possible whilesatisfying the given SE requirement.In figures 4 & 5, the low-transmit-power regime, theproposed energy efficient resource-allocation scheme thatachieves the maximum EE also achieves the maximumSE. However, in the high-transmit-power regime, nosolution exists for a OFDM DAS to optimize both SE andEE simultaneously.Fig.3: SE versus total power of RAU with different rateconstraints for pmax,m 30 dBm and K 10Fig.4: SE versus total power of RAU with different rateconstraints for pmax,m 30 dBm and K 10Fig.5: EE versus SE with different rate constraints andtransmit power for pmax,m 30 dBm and K 10.65

Engineering and Scientific International Journal (ESIJ)Volume 2, Issue 2, April - June 2015ISSN 2394-187(Online)ISSN 2394-7179 (Print)References[1][2][3][4][5][6][7]Fig. 6: SE and EE versus number of Mss with differentrate constraints for Pmmax 30 dBm.The SE and EE achieved by the proposed energyefficient resource-allocation scheme vary with the datarate constraints. This result demonstrates that theproportional fairness constraints can explicitly control theSE and EE ratios among MSs. Therefore, we can alwaysensure the target data rates and EE for each MS if there issufficient transmit power for RAUs.4.3 SE and EE Versus Number of MSsFig.6 shows the EE and SE versus the number of MSs fordifferent rate constraints and p max,m 30 dBm, respectively.Both the EE and SE grow with the number of MSs underdifferent rate constraints since the proposed energyefficientresource-allocation scheme is able to exploit clusionIn this paper, we have investigated the optimal energyefficient resource-allocation methods for the downlinkmultiuser OFDM DAS with proportional fairness, andproposed a suboptimal energy-efficient resourceallocation scheme to maximize EE. Numerical resultshave shown that the proposed algorithm converges to theoptimal solution within a small number of iterations anddemonstrated the tradeoff between EE and SE, which isvery important for future wireless communicationsystems.[15][16][17][18]X.-H.You, D.-M. Wang, B. Sheng,X.-Q. Gao, X.-S. Zhao, andM. Chen “Cooperative distributed antenna systems formobilecommunications,” IEEE Wireless Communication.,vol. 17,no. 3, pp. 35–43, Jun. 2010.H .- L. Zhu, “Performance comparison between distributedantenna and microcellular systems,”IEEE J. Sel. AreasCommunication., vol. 29, no. 6, pp. 1151–1163, Jun. 2011.H. - L. Zhu, S. Karachontzitis, and D. Toumpakaris, “Lowcomplexity resource allocation and its application todistributed antenna systems,” IEEE WirelessCommunication.,vol. 17, no. 3, pp. 44–50, Jun. 2010.H. Kim, S.-R. Lee, K.-J. Lee, and I. Lee, “Transmissionschemes based on sum rate analysis in distributed antennasystems,” IEEE Trans. Wireless Communication., vol. 11,no.3, pp. 1201–1209, Mar. 2012.X.-H. You D.-M. Wang,P.-C. Zhu, and B. Sheng,“Cell edgeperformance of cellular mobile systems,” IEEE J. Sel. AreasCommunication., vol. 29, no. 6, pp. 1139–1150, Jun. 2011.Z.-K. Shen, J. Andrews, and B. Evans, “Adaptive resourceallocation in multiuser OFDM systems with proportional rateconstraints,” IEEE Trans. Wireless Commun., vol. 4, no. 6, pp.2726–2737, Nov. 2005.G.-W. Miao, N. Himayat, G. Y. Li, and A. Swami, “Crosslayeroptimiza-tion for energy-efficient wirelesscommunications:A survey,” J. Wireless Commun. MobileComputer., vol. 9, no.4, pp. 529–542, Apr. 2009.Z. Hasan, H. Boostanimehr, and V. K. Bhargava, “ Greencellular net-works: A survey, some research issues and challenges,”IEEE Commun. Surveys Tuts., vol. 13, no. 4, pp. 524–540, 4thQuart., 2011.W. Miao, N. Himayat, G. Y. Li, and S. Talwar, “Distributedinterference-aware energy-efficient power optimization,” IEEETrans. Wireless Commun., vol. 10, no. 4, pp. 1323–1333, Apr.2011.C. Xiong, G. Y. Li, S.-Q. Zhang,Y. Chen, and S.-G. Xu, “Energyand spectral-efficiency tradeoff in downlink OFDMA networks,”IEEE Trans. Wireless Commun., vol. 10, no. 11, pp. 3874–3886,Nov. 2011.D.-Q. Feng,C.-Z. Jiang,G. Lim, L. Cimini, G. Feng, and G. Y. Li, “Asurvey of energy-efficient wireless communications,” IEEECommunication. Surveys Tuts., vol. 15, no. 1, pp. 167–178, 1stQuart., 2012.L. Deng, Y. Rui, P. Cheng, J. Zhang,Q.-T.Zhang, and M.-Q. Li, “Aunified energy efficiency and spectral efficiency trade off metric inwireless networks,”IEEE Communication. Lett., vol. 17, no. 1, pp.55–58, Jan. 2013.G. P. Fettweis and E. Zimmermann, “ICT energy consumptiontrends and challenges,” in Proc. 11th Int. Symp. WPMC, Sep. 2008,pp. 1–4.Cisco Visual Networking Index, Global Mobile Data Traffic DataForecast Update, Cisco Systems, Inc., San Jose, CA, USA, WhitePaper, pp. 2011– 2016, 2012.Y. Chen,S.-Q. Zhang,S.-G. Xu, and G. Y. Li, “Fundamental tradeoffs on green wireless networks,” IEEE Communication. Mag., vol.49, no. 6, pp. 30–37, Jun. 2011.C.-M. Jiang, Y.Shi, Y. Hou, and S. Kompella, “On optimalthroughput-energy curve for multi-hop wireless networks,” in Proc.IEEE INFOCOM, Apr. 2011, pp. 1341–1349.G. Gur and F. Alagoz, “Green wireless communications viacogni-tive dimension: An overview,” IEEE Netw., vol. 25, no.2, pp. 50–56, Mar./Apr. 2011.Y. Wang,W.-J. Xu, K.-W. Yang, and J.-R. Lin, “Optimalenergy-efficient power allocation for OFDM-based cognitive radionetworks, “IEEE Commun. Lett., vol. 16, no. 9, pp. 1420–1423, Sep.2012.66

Engineering and Scientific International Journal (ESIJ)Volume 2, Issue 2, April - June 2015[19][20][21][22][23]S. Althunibat and F. Granelli, “On the reduction of power losscaused by imperfect spectrum sensing in OFDMA-based cognitiveradio access,” in Proc. IEEE GLOBECOM, 2012, pp. 3383–3387.J. Xu and L. Qiu, “Energy efficiency optimization for MIMObroadcast channels,” IEEE Trans. Wireless Communication., vol. 12,no. 2, pp. 690–701, Feb. 2013.Y. Rui, Q. T. Zhang, L. Deng, P. Cheng, and M.-Q. Li, “Modeselection and power optimization for energy efficiency in uplinkvirtual MIMO systems,” IEEE J. Sel. Areas Communication., vol.31, no. 5, pp. 926–936, May 2013.C.-L. He, B. Sheng, P.-C. Zhu, D.-M. Wang, and X.-H. You,“Energy efficiency comparison between distributed MIMO and colocated MIMO systems,” Int. J. Communication. Syst., pp. y.wiley.com/doi/10.1002/dac.2345/pdf.C.-L. He, B. Sheng, P.-C. Zhu, X.-H. You, and G. Y. Li, “Energyand spectral-efficiency tradeoff for distributed antenna systems withpropor-tional fairness,” IEEE J. Sel. Areas Commun., vol. 31, no. 5,ISSN 2394-187(Online)ISSN 2394-7179 (Print)pp. 894–902, May 2013.[24] C.-L. He, B. Sheng, P.-C. Zhu, and X.-H. You, “Energy efficiencyand spectral efficiency tradeoff in downlink distributed antenna systems,”IEEE Wireless Commun. Lett., vol. 1, no. 3, pp. 153–156, Jun. 2012.[25] L.-S. Ling, T. Wang, Y. Wang, and C. Shi,“Schemes of powerallocation and antenna port selection in OFDM distributed antennasystems,” in Proc. IEEE VTC-Fall, Sep. 2010, pp. 1–5.[26] D.-M. Wang, X.-H. You, J.-Z. Wang, Y. Wang, and X.-Y. Hou,“Spectral efficiency of distributed MIMO cellular systems in acomposite fading channel,” in Proc. IEEE ICC, May 2008, pp.1259–1264.[27] X .- X. Qiu and K. Chawla, “On the performance of adaptivemodulation in cellular systems,” IEEE Trans. Commun., vol. 47, no.6, pp. 884–895, Jun. 1999.[28] O. Arnold, F. Richter, G. Fettweis, and O. Blume, “Power consumption modeling of different base station types in heterogeneouscellular networks,” in Proc.Future Netw. Mobile Summit, 2010,pp.1–8.67

resource-allocation optimization for the downlink multiuser OFDM DAS with proportional fairness. In Section III, a suboptimal energy-efficient resource-allocation scheme is developed. Numerical results are presented to demonstrate the effectiveness of the proposed energy-efficient resource-allocation scheme in Section IV.

Related Documents:

e-mail: jonathan.apodaca@colostate.edu J. Smith DigitalGlobe, Longmont, CO, USA. Deadline and energy constrained dynamic resource allocation 327 Abstract Energy-efficient resource allocation within clusters and data centers is im-portant because of the growing cost of energy. We study the problem of energy-

the resource allocation, the system energy consumption can be further reduced. Recently, Penda et al. [22] have discussed the energy-efficient mode selection for the communication pairs in cel-lular networks. They considered the dynamic time-resource allocation for the uplink and downlink transmissions when cellular mode is adopted.

Motivated by the above observations, propose the resource allocation problem for energy - efficient communication in secure OFDMA downlink systems with artificial noise generati on . By using iterative resource algorithm the closed -form power, secrecy data rate, and subcarrier allocation policies for maximizing the energy efficiency are .

430 allocation to elianto cfd o&m 20,577.32 440 allocation to trillium west cfd o&m 27,267.00 450 allocation to west park cfd o&m 70,008.22 460 allocation to festival ranch cfd o&m 177,790.54 480 allocation to tartesso west cfd o&m 27,809.17 481 allocation to anthem sun valley cfd o&

ing dynamic resource allocation, transmission power minimization and BBU-RRH assignment in one framework. Other attempts re- garding centralized resource allocation have been previously tack- led under rate constraint such as [13-15]. Authors in [13] pre- sented a QoS-based Power Control and resource allocation in LTE Femtocell network (QP-FCRA).

resource and power allocation problem for a single cell network [8-10]. Moreover, low-complexity suboptimal algo-rithms are proposed to perform resource and power allocation [10]. Therefore, the optimal solution is not always guaranteed. In this paper, we formulate the joint resource and power allocation problem for multiuser OFDMA networks, as a

Here, resource allocation is of great importance. It determines the type resource allocation and importance of various parameters based on the nature of a production system and amount, type and importance of resources. Developing a plan differs from developing a program in the issue of resource allocation in project control (Kotler,1999).

AutoCAD 2D Tutorial - 166 - Creating Local Blocks (BMAKE) 19.1 1. Choose Draw, Block, Make. or 2. Click the Make Block icon. or 3. Type BMAKE at the command prompt. Command: BMAKE or BLOCK 4. Type the name of the block. 5. Pick an insertion point. 6. Select objects to be included in the block definition. 7. Click OK