基于能效的多小区LTE系统资源分配算法
基于QoE的LTE多业务资源分配算法
2019，55（6）1引言随着移动用户的飞速增长及服务业务类型的不断扩展，紧缺的无线资源以及差异化的服务质量需求是LTE 网络面临的新的要求和挑战。
除了要尽可能满足用户服务质量（Quality of Service ，QoS ），未来无线通信系统应能够为不同业务提供适当程度的体验质量（Quality of Experience ，QoE ）[1]。
因此如何高效地使用有限频谱资源的同时满足不同用户QoS 以及QoE 成为LTE 网络面临的主要问题，而资源分配是解决这一问题的关键。
目前LTE 无线资源调度算法通常是针对单一业务场景设计的。
如适用于NRT 业务的比例公平（PF ）调度算法，针对RT 业务的改进最大权重时延优先（MLWDF ）、指数比例公平（EXP/PF ）调度算法等[2]。
文献[3]提出一基于QoE 的LTE 多业务资源分配算法余翔，王宏刚，段思睿重庆邮电大学通信与信息工程学院，重庆400065摘要：高效地利用无线频谱资源和保证用户体验质量是未来无线网络的主要目标。
基于此，提出一种基于QoE 的LTE 多业务资源分配算法。
在考虑信道信息、QoS 要求及公平性的基础上，引入QoE 来计算的用户优先级。
特别的，引入最小QoE 约束来保证RT 用户QoE 要求；提出一种次优资源块（Resource Block ，RB ）分配算法来解决复杂的资源分配优化问题，该算法主要分为两步：保证RT 用户最小QoE 要求；最大化系统加权和速率。
仿真结果表明，相较现有的RT/NRT 资源分配算法，该算法在用户分组丢失率、平均QoE 和小区频谱效率方面性能都有所提升。
关键词：LTE 网络；体验质量；资源分配；多业务文献标志码：A 中图分类号：TN915.07doi ：10.3778/j.issn.1002-8331.1712-0252余翔，王宏刚，段思睿.基于QoE 的LTE 多业务资源分配算法.计算机工程与应用，2019，55（6）：81-85.YU Xiang,WANG Honggang,DUAN Sirui.QoE-based multi-service resource allocation algorithm in LTE -puter Engineering and Applications,2019,55（6）：81-85.QoE-Based Multi-Service Resource Allocation Algorithm in LTE SystemYU Xiang,WANG Honggang,DUAN SiruiTelecommunication and Information Engineering Institute,Chongqing University of Posts and Telecommunications,Chongqing 400065,ChinaAbstract ：The main goal of future wireless networks is using wireless spectrum resources as efficently as possible and providing quality of experience guarantee for the users.Based on this,this paper presents a QoE-based multi-service resource allocation algorithm in LTE systems.Firstly,QoE is introduced to calculate user priority based on channel information,QoS requirements and fairness.In particular,a minimal QoE constraint is introduced to ensure QoE requirements of RT users.Secondly,a suboptimal resource block allocation algorithm is proposed to solve the complicated resource allocation optimization problem.The algorithm is divided into two steps ：ensuring minimum QoE requirements for RT user;maximizing system weighting and rate.The simulation results show that compared with the existing RT/NRT resource allocation algo-rithm,the proposed algorithm improves the performance of user packet loss rate,average QoE and cell spectrum efficiency.Key words ：LTE network;Quality of Experience （QoE ）;resource allocation;multi-service基金项目：国家科技重大专项（No.2015ZX03004004）。
基于LTE的D2D资源分配最优算法
Resource Sharing Optimization for Device-to-Device Communication Underlaying Cellular Networks Chia-Hao Yu,Klaus Doppler,C´a ssio B.Ribeiro,and Olav TirkkonenAbstract—We consider Device-to-Device(D2D)communication underlaying cellular networks to improve local services.The system aims to optimize the throughput over the shared resources while fulfilling prioritized cellular service constraints.Optimum resource allocation and power control between the cellular and D2D connections that share the same resources are analyzed for different resource sharing modes.Optimality is discussed under practical constraints such as minimum and maximum spectral efficiency restrictions,and maximum transmit power or energy limitation.It is found that in most of the considered cases,optimum power control and resource allocation for the considered resource sharing modes can either be solved in closed form or searched from afinite set.The performance of the D2D underlay system is evaluated in both a single-cell scenario,and a Manhattan grid environment with multiple WINNER II A1office buildings.The results show that by proper resource management, D2D communication can effectively improve the total throughput without generating harmful interference to cellular networks. Index Terms—Cellular networks,device-to-device,D2D,peer-to-peer,resource sharing,underlay.I.I NTRODUCTIONT HE increasing demand for higher data rates for local area services and gradually increased spectrum conges-tion have triggered research activities for improved spectral efficiency and interference management.Cognitive radio sys-tems[1]have gained much attention because of their poten-tial for reusing the assigned spectrum among other reasons. Conceptually,cognitive radio systems locally utilize“white spaces”in the spectrum for,e.g.,ad hoc networks[2][3] for local services.Major efforts have been spent as well on the development of next-generation wireless communication systems such as3GPP Long Term Evolution(LTE)1and WiMAX2.Currently,the further evolution of such systems is specified under the scope of IMT-Advanced.One of the main concerns of these developments is to largely improve the services in the local area scenarios.Device-to-Device (D2D)communication as an underlaying network to cel-lular networks[4][5]can share the cellular resources for Manuscript received November26,2010;revised February11,2011and March23,2011;accepted April20,2011.The associate editor coordinating the review of this paper and approving it for publication was N.Kato.C.-H.Yu and O.Tirkkonen are with the Department of Communi-cations and Networking,Aalto University,Finland(e-mail:{chiahao.yu, olav.tirkkonen}@aalto.fi).K.Doppler and C.B.Ribeiro are with Nokia Research Center,Nokia Group (e-mail:{klaus.doppler,cassio.ribeiro}@).Digital Object Identifier10.1109/TWC.2011.060811.1021201see /2see /better spectral utilization.In addition to cellular operations where the network services are provided to User Equipment (UE)through the Base Stations(BSs),UE may communicate directly with each other over D2D links while remaining control under the BSs.Due to its potential of improving local services,D2D communication has received much attention recently[6][7][8][9][10][11][12][13][14][15][16].The idea of enabling D2D connections in cellular networks for handling local traffic can be found in,e.g.,[17][18][19], where ad hoc D2D connections are used for relaying pur-poses.However,with these methods the spectral utilization of licensed bands cannot be improved as D2D connections take place in license-exempt bands.Furthermore,ad hoc D2D connections may be unstable as interference coordination is usually not possible.In[20],non-orthogonal resource shar-ing between the coexisting cellular and ad hoc networks is considered.As the operations of both types of networks are independent(with independent traffic loads),interference coordination between them considers only the density of transmitters.Recent works on D2D communication assume the same air interface as the underlaying cellular networks. In[21],the cellular resources are reused by D2D connections in an orthogonal manner,i.e.,D2D connections use reserved resources.Although orthogonal resource sharing eases the task of interference management,better resource utilization may be achieved by non-orthogonal resource sharing.In[4][5],a non-orthogonal resource sharing scheme is assumed.Cellular users can engage in D2D operation when it is beneficial for the users or system.Further,D2D power control when reusing Uplink(UL)cellular resources,where cellular signaling for UL power control can be utilized,is addressed to constrain the interference impact to cellular operations.To better improve the gain from intra-cell spatial reuse of the same resources,multi-user diversity gain can be achieved by properly pairing the cellular and D2D users for sharing the resources[8][9][10].In[10],the resource allocation scheme over multiple cellular users and D2D users considers the local interference situations,making it possible for inter-cell interference avoidance.Interference randomization through resource hopping is considered in[11].This provides more homogeneous services among users in challenging interfer-ence environments,e.g.,when one cellular connection shares resources with multiple D2D pairs at the same time.Integra-tion of D2D communication into an LTE-Advanced network is investigated in[13][14],where schemes for D2D session setup1536-1276/11$25.00c⃝2011IEEEand interference management are proposed.The results show that D2D underlay communication applied to LTE-Advanced networks can increase total throughput in the cell area.Jus-tifications for applying the D2D underlay communication to licensed bands,from the perspectives of users and cellular operators,can be found in[14].Major efforts so far have been put to demonstrate the benefit of local D2D connections without generating much interfer-ence penalty to cellular users.However,the performance of D2D connections can be improved with slightly more D2D-oriented considerations.In[15],the interference from a BS to a D2D connection is avoided by aligning transmissions from the BS on the null space of the interference channel to the D2D connection.In[16],D2D users reuse UL cellular resources and full duplexing BSs are assumed.Accordingly,an interference retransmission scheme at BSs is proposed for assisting the interference cancelation at D2D users.In[15][16],improved D2D performance is shown with slight impact to cellular users. Different standards addressing the need for D2D operation in the same band as infrastructure-based operators can be found,such as HiperLAN2,TETRA and Wi-Fi.In HiperLAN2 and TETRA systems,D2D communication takes place in reserved resources.This restriction limits the interference from D2D connections and is beneficial for severely mutually interfered situations.However,dedicated resources also lead to inefficient utilization of resources in situations with weak mutual interference.For the part of Wi-Fi technology that is based on IEEE802.11standards,users can sense and access the radio medium only if the channel is free.Accordingly the access points do not have full control over the resources. Wi-Fi technology supports a Wi-Fi direct mode that allows direct D2D connection between peers.However,Wi-Fi direct mode requires users to manually pair the peers,as is the case for Bluetooth technology.In the proposed D2D underlay communication,the pairing can be handled by BSs and thus provides new use cases and better user experiences[5][14]. In this article,we analyze the resource sharing in a D2D communication underlaying cellular system.Cellular BSs are assumed capable of selecting the best resource sharing scheme for cellular and D2D connections.No specific assumptions on the background cellular networks are made.The alterna-tives addressed are1)non-orthogonal sharing:both cellular traffic and D2D traffic use the same resources,2)orthogonal sharing:D2D communication uses dedicated resources,and 3)cellular operation:the D2D traffic is relayed through the BS.We assume that the cellular network performs radio resource management for both the cellular and the D2D connections.The system aims to optimize the total throughput over the shared resources while fulfilling possible spectral efficiency restrictions and power constraints.We analyze two optimization cases.In greedy sum-rate maximization,cellular and D2D communication are treated as competing services. The maximization is subject to a maximum power or energy constraint.In sum-rate maximization with rate constraints,we prioritize the cellular users by guaranteeing a minimum trans-mission rate.Furthermore,we set an upper limit to the spectral efficiency to consider practical limitations in Modulation and Coding Schemes(MCS).Naturally,a maximum transmission rate is thus constrained by the highest MCS.It is noted that the resource sharing schemes considered here is not for harvesting multi-user diversity gain as addressed in[8][9][10].Instead,our resource sharing schemes are to further optimize the resource usage among cellular and D2D users that have been allocated with the same resources.Similar problem is also considered in[6][7],where resource sharing mode selection and transmit power allocation are considered jointly to fulfill some target Signal-to-Interference-plus-Noise Ratio(SINR)values for each link.Our works differ from those in[6][7]in that we consider more extensive set of resource sharing modes and the target for optimization is throughput, rather than SINR targets.The non-orthogonal resource shar-ing problem has been discussed in different contexts[22], [23].There,authors consider power allocation of two-user interference channel in a two-cell network,under a maximum transmit power constraint.It is shown that the optimal power allocation scheme resides on afinite set of possible solutions. Our work extends the throughput-maximizing power control in[22][23]by giving a minimum service guarantee to the prioritized user and introducing a maximum transmission rate constraint.Moreover,we consider the selection of resource sharing methods subject to power and energy constraints. Part of this work has been published in[24],where optimal power control in the non-orthogonal sharing is analyzed and evaluated in a single-cell scenario.In this work,we further apply sum-rate optimization to orthogonal sharing and cellular modes,to enable a fair comparison between different modes. We generalize the power constraint by separately considering it in the time and frequency domains for the orthogonal sharing and cellular operation modes.Moreover,we apply our analysis to a Manhattan grid with WINNER II A1[25]office buildings to evaluate the performance in a multi-cell scenario.As a WINNER II A1office is a well-known indoor scenario with widely accepted channel models,it provides a realistic simula-tion environment for evaluating the results.These generalized considerations give an extensive and complete set of results on the considered problem of resource sharing mode selection. The remainder of this article is organized as follows: In Section II we present the system model,the considered resource sharing modes,and the optimization constraints.In Section III we solve the optimal power control problem of the non-orthogonal resource sharing method.In Section IV and Section V,we present the results of optimal radio resources allocation for the two orthogonal resource sharing modes.In Section VI we evaluate the performance improvement from the D2D underlay communication in both single cell and multi-cell scenarios.We conclude this work in Section VII.II.S YSTEM M ODELWe study the resource sharing between two types of com-munication,traditional cellular communication between a BS and a user,and direct D2D communication.We assume that a BS scheduler knows about the D2D communication need based on communication request between two potential D2D users,and the BS decides to offload that traffic to a direct D2D connection.Based on handover and other measurements provided by the cellular and potential D2D users,the BS may select by which way to reuse the resources of a specific cellular link for serving the D2D communication need.Fig.1.D2D communication as an underlay network to a cellular network. UE1is a cellular user whereas UE2and UE3are in D2D communication.We consider the case where one cellular user(UE1)and two D2D users(UE2and UE3)share the radio resources.We assume that inter-cell interference is managed efficiently with inter-cell interference control mechanisms based on power control or resource scheduling.Thus we can assume individual power constraints for transmitters,based on which further optimization on power and resource allocations is performed for better intra-cell spatial reuse of spectrum enabled by D2D underlay communication.Fig.1illustrates the considered scenario,where g i is the channel response between the BS and UE i,and g ij is the channel response between UE i and UE j. The D2D pair can communicate directly with coordination from the BS.The channel response can include the path loss, shadow and fast fading effects.Channel State Information (CSI)of all the involved links is assumed at the BS for co-ordination.To acquire full CSI,in addition to normal cellular measurement and reporting procedures,a method is required for the D2D transmitter to transmit probe signals,which are then measured at the D2D receiver and the interference victim, and reported to the BS.For more details,see[13].A.Resource Sharing ModesThe sharing of resources between D2D and cellular con-nections is determined by the BS.If D2D users are assigned resources that are orthogonal to those occupied by the cellular user,they cause no interference to each other and the analysis is simpler.On the other hand,the resource usage efficiency can be higher in non-orthogonal resource sharing.Here,we consider three resource allocation modes:∙Non-Orthogonal Sharing mode(NOS):D2D and cellular users re-use the same resources,causing interference to each other.The BS coordinates the transmit power for both links.∙Orthogonal Sharing mode(OS):D2D communication gets part of the resources and leaves the remaining part of resources to the cellular user.There is no interference between cellular and D2D communication.The resourcesallocated to D2D and cellular connections are to be optimized.∙Cellular Mode(CM):The D2D users communicate with each other through the BS that acts as a relay node.The portion of resources allocated to each user is to be optimized.Note that this mode is conceptually the same as a traditional cellular system.Here,we optimize the transmission in all of these modes, to understand what can be optimally reached in a D2D system based exclusively on NOS,exclusively on OS,or on an opti-mal mode selection.In particular,optimizing the cellular mode allows a fair comparison between a pure cellular network and a D2D enabled cellular network.Resource sharing may take place in either UL or Downlink(DL)resources of the cellular user.For each UL and DL resource,the BS selects one out of the three possible allocation modes to maximize the sum rate. With non-orthogonal sharing,the source and the receiver of the interference may be different when sharing the cellular user’s UL and DL resources.We indicate non-orthogonal sharing of the cellular user’s UL and DL resources by NOSul and NOSdl, respectively.We define the sum rate of the D2D and the cellular connections by applying the Shannon capacity formula.To maximize the sum rate of the two connections when sharing UL or DL resources of the cellular user,the BS selects the resource allocation mode according toR DLmax=max(R NOSdl,R OSdl,R CMdl),R ULmax=max(R NOSul,R OSul,R CMul),(1)where R NOSul and R NOSdl are the sum rate when non-orthogonally sharing the UL and DL resources of the cellular user,respectively,R OSul and R OSdl denote the sum rate when the D2D pair shares orthogonally the UL and DL resources of the cellular user,respectively,and similarly for R CMul and R CMdl.It is noted that when the cellular mode is chosen,we need both the UL and DL transmissions for D2D communi-cation.Hence,cellular mode is used for both UL and DL if selected.Decisions on the used D2D mode are taken at the BS subject to existing channel and buffer status information.In the extreme case,mode selection can be done at the same frequency as allocation decisions.Preferably,however,the D2D pair is semi-statically configured to a resource sharing mode.In a packet switched radio access network,actual transmission conditions would be governed by short-term scheduling decisions made by the BS.A control channel would be used by the BS to inform the UE about scheduling decisions.D2D users in the cellular mode are served as normally scheduled shared channel users.In the orthogonal sharing mode,the D2D traffic would be explicitly scheduled by the BS.In the non-orthogonal sharing mode,the D2D pair would be allowed transmission with specific parameters always when specific shared channel resources are allocated to a specific cellular user with whom the D2D pair shares the channel resources.This is subject to potential delay issues for sharing DL resources–the D2D transmitter needs to be able to configure its transmission rapidly after reading a DL control channel allocation for the paired cellular user.Fig.2.Resource allocation in non-orthogonal and orthogonal sharing modes.B.Optimization with Power and Energy Constraints It is possible to maximize the sum rate of the considered resource sharing modes by optimizing the power or resource allocation.When sharing resources non-orthogonally,opti-mization can be conducted in power domain only.On the other hand,to optimize the sum rate of the orthogonal sharing and cellular modes,resource allocation can be manipulated.When optimizing the resource allocation,two constraints will be discussed.We assume that in the orthogonal sharing and cellular modes,all transmitters use their maximum power when transmitting.As there is no intra-cell interference in these two modes,the maximum sum rate is achieved with our system setting where inter-cell interference is assumed managed properly.Depending on the domain of resource allocation,this may lead to different types of constraints.One alternative is that the power density per resource does not depend on the resource allocation size.This would be the case,e.g.,if resources are shared in the time domain,and we call this a power constraint.In the other alternative,the energy used for transmission is fixed,and the power density per resource depends on the resource allocation.This corresponds to a case where resources are allocated in the frequency domain,and each transmitter concentrates all the power in the available bandwidth.We call this an energy ing the energy constraint may lead to higher spectral ef ficiency,as multiple transmitters may simultaneously use their maximum transmit power,leading to a higher total energy usage.With non-orthogonal sharing,the interference caused by D2D connection depends on which one of the D2D users is transmitting.Unless stated otherwise,we assume the worst-case interference condition where the interference from D2D connection is caused by the user that could create the strongest interference.If there is a clear de finition on the D2D trans-mitter,one can modify the interference condition accordingly.We denote the power of the Additive White Gaussian Noise (AWGN)at the receiver by N 0,the common maximum transmit power by P max ,and the assigned transmit powers of the cellular and the D2D links by P c and P d ,respectively.The sum rate equations for non-orthogonal sharing can be found by summing up rates from the cellular link and the D2D link:R NOS (P c ,P d )=log 2(1+Γc (P c ,P d ))+log 2(1+Γd (P c ,P d ))=log 2((1+Γc (P c ,P d ))(1+Γd (P c ,P d ))),(2)where Γc (P c ,P d )=g 1P c /(g dc P d +I c )and Γd (P c ,P d )=g 23P d /(g cd P c +I d ).We have denoted by g cd thechannelFig.3.Resource allocation in cellular mode with maximum power con-straint (TDD/TDMA),and with maximum energy constraint for cellular DL resources (TDD/FDMA).response of the interference link from the cellular connection to the D2D connection,and vice versa for g dc .We used I c and I d to indicate the interference-plus-noise power at the receiver of the cellular link and the D2D link,respectively.The interference power I c and I c models inter-cell interference according to our system setting.Denote R as the general term for rate,e.g.,R NOS in (2).Strictly speaking,R is not a rate but a spectral ef ficiency.When multiplied with system bandwidth,we get a rate.As we restrict the spectral ef ficiency R to be with respect to the system bandwidth and the system bandwidth is not altered by resource allocation strategies,all R s in this paper are in one-to-one correspondence with rates.The resource allocation of the non-orthogonal sharing mode is illustrated in the left half of Fig.2.To simplify the notation,from now on we assume that all receivers experience the same interference-plus-noise power I 0.However,for performance evaluation,we shall then replace I 0with the experienced interference-plus-noise power of different receivers.For the remaining two modes,we can control the portion of the resources used to serve the D2D and the cellular users,and we may apply either power or energy constraints.With orthogonal resource sharing,the sum rate expressions with power/energy constraints areR OS-P (α)=αlog 2(1+γ1)+α′log 2(1+γ23),(3)R OS-ℰ(α)=αlog 2(1+γ1α)+α′log 2(1+γ23α′),(4)where R OS-P and R OS-ℰare the sum rate with maximum power constraint and maximum energy constraint,respectively,0≤α≤1,α′=1−α,γ1=g 1P max /I 0,and γ23=g 23P max /I 0.The right half of Fig.2illustrates the resource allocation of the orthogonal sharing mode.When sharing resources in time (or frequency)domain,the power (or energy)constraint is used.In cellular mode,in addition to the division of resources αbetween the cellular user and the two D2D users,we may optimize the division of resources βbetween the UL and DL phases of the cellular relaying service replacing the D2D link.Thus one D2D user will first convey the data to the BS before the BS can relay it to the other D2D user.It implies that D2D UL phase has to happen before D2D DL phase.We assume that the cellular service is realized by flexible switching Time-Division Duplexing (TDD),so that UL and DL resources are using the same frequency and the switching between ULFig.4.Resource allocation in cellular mode with maximum energy constraint for cellular UL resources (TDD/FDMA).and DL may be optimized.If Time Division Multiple Access (TDMA)is used we have a resource allocation as illustrated in Fig.3,and the power constraint is applied.The sum rate is R CM-P (α,β)=αlog 2(1+γ1)+α′min (βlog 2(1+γ2),β′log 2(1+γ3)),(5)where β′=1−βand γi =g i P max /I 0for i =1,2,3.If Frequency or Code Division Multiple Access (FDMA or CDMA)is used,we may apply the energy constraint—when transmitting,all transmit power is concentrated to the resources used.However,difference exists for DL and UL resources.When the cellular user is an UL user,we can have a resource allocation as illustrated in Fig.4.The sum rate is R CMul-ℰ(α,β)=αβlog 2(1+γ1/β)+min (αβ′log 2(1+γ2/β′),α′log 2(1+γ3)).(6)If the cellular user is a DL user,a resource allocation scheme similar to Fig.4would not lead to using the energy constraint.As there is only one transmitter in the DL phase,manipulation of resource allocation from time to frequency domain would not result in increasing the energy consumption,implying the same situation as in maximum power-constrained case.Therefore R CMdl-ℰ(α,β)=R CM-P (α,β).C.Optimization with Spectral Ef ficiency Constraints Practical considerations of communication systems require setting a highest achievable spectral ef ficiency due to the limitation caused by the supported MCSs.In addition,cellular communication might need to be protected in the presence of D2D underlay system.We consider two different sets of constraints in spectral ef ficiency.In the first case,the BS simply runs a greedy sum-rate maximization.In the second case,the cellular user has priority over D2D users in the sense that the BS gives a guaranteed minimum rate R l bps,with respect to total bandwidth to be shared,to the cellular user.A cellular user is in outage if the rate is smaller than R l bps.In the second case an upper limit on the link spectral ef ficiency,r ℎbps/Hz,is further assumed.The link spectral ef ficiency is the spectral ef ficiency experienced on resources utilized by a link,so resulting rate depends on the resource allocation.We consider the rate constraints in the Signal to Interference plus Noise Ratio (SINR)domain by assuming that an SINR higher than a maximum value,γℎ,does not increase thethroughput when the link spectral ef ficiency is limited to r ℎbps/Hz,and a spectral ef ficiency of r l bps/Hz is achievable for an SINR no lower than a minimum value,γl .The assumption is in line with stat-of-the-art link adaptation technique with a limited amount of MCSs [26].The throughput cannot be further improved by increasing SINR if the current SINR is high enough to support the highest MCS.On the other hand,there is a lower limit on SINR to support the stable transmission using the lowest MCS.The value r l bps/Hz here re flects the cellular service guarantee R l and is the spectral ef ficiency required for the cellular link in non-orthogonal sharing mode.A higher link spectral ef ficiency of at least r l /αbps/Hz is needed in the bandwidth assigned to cellular user in the orthogonal sharing and cellular modes with power constraint,and r l /(αβ)bps/Hz in the bandwidth assigned to the cellular user in cellular mode with energy constraint.In the following,we assume that P max is large enough to compensate for g 1in the cell area to ful fill the lowest rate constraint.In many cases,the transmit power will be limited and a minimum transmission rate without outage cannot necessarily be guaranteed in,e.g.,Rayleigh fading channels.Based on the analysis presented below,the algorithmic complexity of the mode selection can be estimated.For the power and rate constrained variant,which is shown to be better in Section VI,the worst case of one mode selection decision for one set of D2D pair and a cellular user requires 9base-2logarithms,14divisions,23multiplications and 30additions.III.O PTIMIZATION FOR N ON -ORTHOGONAL S HARING A.Greedy sum-rate maximizationWithout giving priority to either cellular or D2D com-munication,the optimal power allocation for greedy sum-rate maximization is a feasible solution to the optimization problem(P ∗c ,P ∗d)=arg max (P c ,P d )∈Ω1R NOS (P c ,P d ),(7)Ω1={(P c ,P d ):0≤P c ,P d ≤P max },where Ω1de fines the feasible set of (P c ,P d ).According to theresults in [22],binary power control is enough for the above optimization problem.Thus,the optimal power allocation is searched over the following 3possible sets ΔΩ1={(P c ,P d ):(0,P max ),(P max ,0),(P max ,P max )}.B.Sum-rate Maximization Subject to Rate Constraints Following [24],the results above can be generalized to a situation where there is priority for the cellular user and an upper limit on the spectral ef ficiency of all users.In this case,we have the following optimization problem(P ∗c ,P ∗d )=arg max (P c ,P d )∈Ω2R NOS (P c ,P d ),(8)Ω2={(P c ,P d ):0≤P c ,P d ≤P max ,γl ≤Γc (P c ,P d )≤γℎ,Γd (P c ,P d )≤γℎ},(9)where Ω2de fines the feasible set of (P c ,P d ).In [23]it is shown that the optimal power allocation(P ∗c ,P ∗d)resides on the boundary ∂Ω2of the feasible set Ω2,indicating that (P ∗c ,P ∗d )has at least one binding constraint.。
LTE多天线系统资源分配算法与性能分析
为了和各项多天线技术的性能进行对比，我们首先 讨论单天线模式下SINR的计算[8]：
s ( n ) = G ( n )∗ y ( n )
(10)
P
( j) u
、P
( j) loss
、
H ( j ) (n ) 表示的含义与上面类似，只是主服务基站换成了干
扰基站 j。
表示噪声的功率。
(2) 最大比合并下的1×2接收分集（SIMO） 系统在 1 × 2 接收分集模式下采取最大比合并，用户 接收信号SINR的计算为[8]：
( j)
( j)
其中， W (n) 和 W ( j ) (n) 表示在时频资源块 n 上主服 务基站和干扰基站 j 采用的预编码矩阵。然后再根据公式 (16)即可计算接收信号的SINR。类似的，SU-MIMO-Pre 和MU-MIMO-Pre模式SINR的计算公式是一样的。
3
调度算法分析
为了既满足用户公平性准则又能提高系统的吞吐量
公平调度算法。
新 业 务
3.2 SU-MIMO-Pre复用和MU-MIMO-Pre复用模式
对 SU-MIMO-Pre 复用，用户在每个时频资源块上 计算使用不同预编码矩阵后接收到的两个数据流的 SINR
性能，我们必须对各多天线模式采用相应的调度算法才能 达到要求。
3.1 不含预编码的多天线模式
在这里我们只讨论单天线（SISO）、1×2接收分集 （ SIMO ）、STBC 分集、 SU-MIMO复用和MU-MIMO 复 用等多天线模式。 对单天线、1×2接收分集、STBC分集，系统的数据 流层数是1，我们仿真采取的调度算法是比例公平调度算 法，其基本思想是：每个用户计算在时频资源块n上接收到 信号的SINR，根据信道质量向基站提出速率申请，选取优 先级最高的用户j在时频资源块n上进行调度传输，即：
多用户大规模MIMO系统能效资源分配算法
多用户大规模MIMO系统能效资源分配算法佚名【摘要】该文针对多用户大规模多输入多输出(MIMO)移动通信上行系统，提出一种基于能效优化的资源分配算法。
所提方法在采用最大比合并(MRC)接收情况下，满足用户数据速率和可容忍的干扰水平约束条件下，以最大化系统能效下界为准则建立优化模型。
根据分数规划的性质，把原始的分数最优化问题转换成减式的形式，进而采用凸优化的方法，通过联合调整基站端的发射天线数和用户的发射功率来优化能效函数。
仿真结果表明，所提算法与穷举算法在能效上的差距不足9%，并且有较好的系统频谱效率性能，同时算法复杂度得到了显著降低。
%An energy-efficient resource allocation scheme is proposed for multi-user massive MIMO mobile communication uplink system. A mathematical formulationof optimization issue is provided with the objective of maximizing system energy efficiency lower bound under the data rate of user and tolerable interference level constraint, meanwhile the Base Station (BS) uses a Maximum-Ratio Combining (MRC) receiver. By transforming the originally fractional optimization problem into an equivalent subtractive form using the properties of fractional programming, then convex optimization is adopted to maximize the energy efficiency. Specifically, both the numbers of antenna arrays at the BS and the transmit data rate at the user are adjusted. Simulation results show that the energy-efficiency difference between the proposed algorithm and the exhaustive algorithm is less than 9%, at the same time, the performance of spectral-efficiency of the proposed algorithm is very well and the complexity is significantly reduced.【期刊名称】《电子与信息学报》【年(卷),期】2015(000)009【总页数】6页(P2198-2203)【关键词】无线通信;大规模多输入多输出;多用户;资源分配;上行系统;能效【正文语种】中文【中图分类】TN921 引言随着无线通信设备的能量消耗急剧增加和对全球变暖问题的高度关注，绿色通信逐渐成为一种趋势。
超密集网络中基于能效最优的资源分配算法
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专题 ：物联 网技 术与应 用
LTE系统中资源分配算法的研究分解
LTE系统中资源分配算法的研究1 LTE 概述1.1移动通信的发展Antonio Meucci于1860年在纽约首次向公众展示电话发明，随后，经过近百年的历程，第一个电话系统在1940年末问世，直到70年代末“蜂窝系统”进入通信这个广阔的天地，让人们感受到电话给生活带来的巨大改变。
如今围绕着“电话通信”业务以惊人的速度发展，同时也改变着我们的生活，随之产生的新的通信方式移动通信也不断向着新的阶梯迈进。
通信发展现在正立足于2G（secondgeneration，第二代移动通讯及其技术）和3G（3rd Generation，第三代移动通讯及其技术）之间，相关的研究人员仍在不断进行新一代的通信研究。
从第一代通信系统到全球移动通讯系统GSM（Global System for Mobile Communications，全球移动通讯系统，俗称“全球通”第二代移动通讯技术的代表），移动通信系统的运营经历着飞速的发展。
虽然二代网络系统中解决了很多一代中存在的缺陷，而且数据速率上限到达144Kbps，但对于数据速率的需求，仍无法满足用户。
为了满足用户需求，保证网络的持续发展于2002年开始3G的网络建设。
现在正在建设的3G网络在速率上已可以提供至少144kbps的车辆移动通信、384kbps的行人通信、卫星移动环境9.6kbps以及固定地点达到2Mbps的通信，可以提供最高数据速率达8~10Mbps，并且带宽也可达5MHz以上的要求。
整个移动通信其发展从起初的模拟到数字，再到称之为准宽带移动通信的第三代移动通信。
通信的方式上已打破有线一统天下的格局，实现了在空间环境中无线传输的无线通信。
这种利用电磁波而不通过电缆进行的无线通信是一个因用户需要而连接并提供服务，用户不需要时没有连接的一种通信方式。
非常便捷，也不会出现资源使用独占的情况。
这些改变让大家都不断享受到移动通信的信息丰富性，便捷性，而这也在无形中改变着社会，使得人们期待着未来的移动通信的发展必是更大容量、更高速率以及更多更强功能的多媒体业务的宽带移动通信系统。
超密集网络中基于能效最优的资源分配算法
超密集网络中基于能效最优的资源分配算法章跃跃;夏玮玮;朱亚萍;燕锋;左旭舟;沈连丰【摘要】The energy-efficient resource allocation problem was taken in an ultra-dense network(UDN) consisted of multiple femtocells.Aiming at maximizing the energy efficiency (EE) of the whole network,quality of service (QoS) constraint and interference limitation wasconsidered.Then,the established nonconvex and nonlinear problem was relaxed relying on Cauchy inequality,and was developed into a non-cooperative game.Additionally,a distributed EE maximization algorithm was proposed satisfying the max-min fairness (MMF) criterion.Simulation results show that the proposed algorithm outperformances the existing algorithms in terms of the EE and the throughput.%为了实现超密集网络中的绿色通信,提出一种基于能效最优的资源分配算法.首先,在考虑用户服务质量(quality of service,QoS)需求和干扰容限的情况下,建立最大化网络能效的优化问题.其次,为了降低求解原问题的计算复杂度,采用柯西不等式将原优化问题进行松弛,从而转化为非合作博弈问题.进而,在满足最大最小公平(max-min fairness,MMF)准则的情况下,提出一种分布式能效最优算法(distributed EE maximization algorithm,DEMA).仿真结果表明,所提算法较传统算法可以更好地兼顾系统的能效和吞吐量性能.【期刊名称】《电信科学》【年(卷),期】2017(033)010【总页数】8页(P26-33)【关键词】超密集网络;能效最优;非合作博弈;分布式能效最优算法【作者】章跃跃;夏玮玮;朱亚萍;燕锋;左旭舟;沈连丰【作者单位】东南大学,江苏南京210096;东南大学,江苏南京210096;东南大学,江苏南京210096;东南大学,江苏南京210096;电子科技大学,四川成都610054;东南大学,江苏南京210096【正文语种】中文【中图分类】TP393物联网（internet of things，IoT）作为新一代信息技术的重要组成部分，旨在为许多智能对象和应用程序提供网络互联和信息交互。
LTEA系统中基于QoE能效的无线资源分配算法
ＯＦＤＭ系统单小区场景下能效资源分配问题进行了研究，在考 虑用户最小数据速率约束及功率限制下，通过资源分配实现最 大化系统能效。通过分数规划性质并根据拉格朗日对偶分解 求解优化问题，分别实现子信道和功率分配，并给出了一种次 优的基于 ＱｏＳ感知资源分配算法，在有效提高系统能效的同 时很好地保证了用户 ＱｏＳ需求。文献［７］在考虑比例速率公 平约束下，提出一种多用户 ＯＦＤＭ 系统中最大化能效的资源 分配算法。
上述算法虽然 都 使 系 统 得 到 了 不 错 的 性 能 提 升，但 依 然无法做到 ＱｏＥ和能耗的 同 时 优 化。不 同 于 最 大 化 “比 特 每焦耳”参数的传统能效资源分配算法，文献［８］首先给 出 了一种表示用户满意 度 和 能 耗 比 值 的 效 用 参 数；其 次 以 最 大化效用参 数 为 目 标，提 出 一 种 ＯＦＤＭＡ系 统 中 基 于 能 效 效 用 的 资 源 分 配 算 法 。 然 而 该 算 法 忽 略 了 用 户 间 公 平 性 ，缺 乏 ＱｏＥ保证。文献［９］对多小区 ＯＦＤＭＡ系统中 ＱｏＥ和能耗 联 合 优 化 问 题 进 行 了 研 究 ，以 最 大 化 一 种 新 颖 的 可 以 同 时 表 征基于公平性的用户 ＱｏＥ和能耗的效用函数为目标进行建 模，并分别提出迭代的 ＲＢ和功率分配方案完成优化问题的 次优求解。
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LTE中基于能效的多小区资源分配算法
ＤＯＩ ： １ ０ ． １ ６ ２ ５ ５ ／ ｊ ． ｃ ｎ ｋ ｉ ． １ ｄ ｘ ｂ ｚ ． ２ ０ １ ７ ． ０ ２ ． ０ １ ３
Ｌ Ｔ Ｅ中基 于 能效 的多小 区资 源分 配算 法
刘远航 ， 胡 冰 ， 黄马驰 ， 彭 帅
（ 重庆 邮电大学 移动通信技术重庆市重点实验室 ， 重庆 ４ ０ ０ ０ ６ ５ ）
表 明， 与传 统 资源 分配 算法相 比 ， 所提 算 法在 满足 用户 最 小速 率要 求 的 同 时更有 效 地 降低 了系统
的能耗 ， 优 化 了 系统 能 效 。
［ 关键 词 ］ Ｌ Ｔ Ｅ网络 ； 能效 ； 负载 均衡 ； Ｐ Ｒ Ｂ分 配
［ 中图分 类号 ］ Ｔ Ｎ ９ ２ ９ ． ５
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［ 文献标 志码 ］ Ａ
［ 文章 编号 ］ １ ０ ０ ５ － ０ ３ １ ０ （ ２ ０ １ ７ ） ０ ２ ． ０ ０ ７ ２ ． ０ ５
Ｅｎ ｅ ｒ ｇ ｙ Ｅｆ ｉｃ ｆ ｉ ｅ ｎ ｃ ｙ Ｒｅ ｓ ｏ ｕ ｒ ｃ ｅ Ａｌ ｌ ｏ ｃ ａ ｔ ｉ ｏ ｎ ｆ ｏ ｒ Ｍｕ ｌ ｔ ｉ － ｃ ｅ ｌ ｌ ｉ ｎ ＬＴＥ Ｎｅ ｔ ｗｏ ｒ ｋ
ＬＩ Ｕ Ｙｕ ａ ｎ— ｈａ ｎ ｇ， ＨＵ Ｂｉ ｎ ｇ， ＨＵＡＮＧ Ｍａ － ｃ ｈ ｉ ，ＰＥＮＧ Ｓ ｈｕ ａ ｉ
LTE网络中基于能效的资源分配方法[发明专利]
![LTE网络中基于能效的资源分配方法[发明专利]](https://img.taocdn.com/s3/m/6db0f1f94431b90d6d85c796.png)
专利名称：LTE网络中基于能效的资源分配方法专利类型：发明专利
发明人：李云,刘文晶,刘期烈
申请号：CN201210270566.7
申请日：20120801
公开号：CN102791002A
公开日：
20121121
专利内容由知识产权出版社提供
摘要：本发明LTE网络中基于能效的资源分配方法。
适用于LTE网络，由于用户终端和对应的业务流量进入小区时具有随机性、时变性，导致整个网络中的负载可能出现不均衡分布状态。
本发明对于不均衡低负载网络，首先根据各小区的负载情况，判断是否需要进行虚拟负载均衡，使整个网络得到一个均衡状态。
虚拟负载均衡过程完成之后自行对各小区中的用户启动带宽扩展，将各用户的信道质量进行优先级从高到低的排序，高优先级的用户在每轮分配中，具有优先分配额外的一个RB的权利。
在合理有效地利用整个网络的频谱资源基础上，最终达到整个网络能耗降低的目的，并且极大地提高了整个网络的频谱利用率。
申请人：重庆邮电大学
地址：400065 重庆市南岸区黄桷垭崇文路2号
国籍：CN
代理机构：重庆市恒信知识产权代理有限公司
代理人：刘小红
更多信息请下载全文后查看。
LTE系统中资源分配算法的研究分解
LTE系统中资源分配算法的研究1 LTE 概述1.1 移动通信的发展Antonio Meucci 于1860 年在纽约首次向公众展示电话发明，随后，经过近百年的历程，第一个电话系统在1940年末问世，直到70 年代末“蜂窝系统”进入通信这个广阔的天地，让人们感受到电话给生活带来的巨大改变。
如今围绕着“电话通信” 业务以惊人的速度发展，同时也改变着我们的生活，随之产生的新的通信方式移动通信也不断向着新的阶梯迈进。
通信发展现在正立足于2G （seco ndge nerati on,第二代移动通讯及其技术）和3G （3 rd Gen eration, 第三代移动通讯及其技术）之间，相关的研究人员仍在不断进行新一代的通信研究。
从第一代通信系统到全球移动通讯系统GSM（Global System for Mobile Communications ,全球移动通讯系统,俗称“全球通”第二代移动通讯技术的代表） ,移动通信系统的运营经历着飞速的发展。
虽然二代网络系统中解决了很多一代中存在的缺陷,而且数据速率上限到达144Kbps，但对于数据速率的需求，仍无法满足用户。
为了满足用户需求，保证网络的持续发展于2002 年开始3G 的网络建设。
现在正在建设的3G 网络在速率上已可以提供至少144kbps 的车辆移动通信、384kbps 的行人通信、卫星移动环境9.6kbps 以及固定地点达到2Mbps的通信，可以提供最高数据速率达8~10Mbps，并且带宽也可达5MHz以上的要求。
整个移动通信其发展从起初的模拟到数字, 再到称之为准宽带移动通信的第三代移动通信。
通信的方式上已打破有线一统天下的格局,实现了在空间环境中无线传输的无线通信。
这种利用电磁波而不通过电缆进行的无线通信是一个因用户需要而连接并提供服务, 用户不需要时没有连接的一种通信方式。
非常便捷, 也不会出现资源使用独占的情况。
这些改变让大家都不断享受到移动通信的信息丰富性, 便捷性, 而这也在无形中改变着社会, 使得人们期待着未来的移动通信的发展必是更大容量、更高速率以及更多更强功能的多媒体业务的宽带移动通信系统。
基于能耗感知的包簇资源分配算法研究
基于能耗感知的包簇资源分配算法研究随着无线通信技术的不断发展和普及，无线网络已经成为人们日常生活不可缺少的一部分，但是无线网络设备的能源限制导致了网络资源的有限。
因此，有效的资源管理成为无线网络设计的关键。
包簇资源分配算法是一种有效的无线网络资源管理算法，它通过对数据包进行分类和聚类，实现对密集区域中的资源分配和调度，提高网络的能效和传输效率。
本文通过研究基于能耗感知的包簇资源分配算法，对算法进行详细评估和分析。
首先，介绍了包簇资源分配算法的基本原理与流程，并提出了针对包簇资源分配算法的能耗感知策略。
接着，利用模拟实验的方式对算法的性能进行评估与分析，并与其他无线网络资源分配算法进行对比。
研究表明：基于能耗感知的包簇资源分配算法能够提高无线网络的传输效率和网络的能效。
在网络拥塞较为严重的情况下，能够更好地满足用户的需求。
在实验数据中，我们发现，与其他无线网络资源分配算法相比，算法的平均传输速率提高了约30%，整体网络能源消耗降低了约20%。
研究结论表明：基于能耗感知的包簇资源分配算法是一种高效、可行的无线网络资源管理算法。
对于现有的基于包簇的无线网络资源分配算法，如LEACH和PEGASIS，在满足网络传输效率和能效的基础上，加上能耗感知策略，可以进一步提高网络的性能。
虽然基于能耗感知的包簇资源分配算法可以提高网络的能效和传输效率，但目前还存在着一些问题。
例如，算法的复杂度较高，需要较为强大的计算机处理能力。
因此，如何提高算法的时间和空间效率，以及如何在实际应用中逐步推广和提高算法的可行性，是下一步需要进一步研究的问题。
总之，基于能耗感知的包簇资源分配算法是一种有效的无线网络资源管理算法，具有重要的实际应用价值。
我们相信，随着无线通信技术的不断发展和改进，基于能耗感知的包簇资源分配算法在未来将会更加成熟和完善，为无线网络的发展和应用带来更多的机会和挑战。
LTE-A系统多小区联合处理算法研究与仿真的开题报告
LTE-A系统多小区联合处理算法研究与仿真的开题
报告
标题：
LTE-A系统多小区联合处理算法研究与仿真
研究背景：
随着无线通信技术的不断发展，LTE-A系统已经成为了4G时代的标志性技术之一。
但是，由于LTE-A系统中采用了频率复用的方式，当用
户数量增多时，系统的吞吐量和信号质量就会出现下降。
为了解决这个
问题，多小区联合处理算法被提出。
该算法将多个小区之间的资源进行
共享，提高了系统的网络覆盖率和吞吐量，同时也提高了用户的体验。
研究内容：
本研究旨在研究LTE-A系统多小区联合处理算法，包括小区间协作、功率控制、资源分配等多个方面。
具体研究内容包括：
1. 分析多小区联合处理算法的原理，并结合LTE-A系统的特点，设
计合适的算法；
2. 针对LTE-A系统中的小区间干扰问题，分析多小区联合处理算法
的协作机制，探讨干扰最小化的途径；
3. 将算法应用于LTE-A系统中，利用仿真实验进行验证和对比，评
估算法的性能优劣；
4. 探讨多小区联合处理算法在实际应用中的潜力和发展前景，为未
来通信技术的发展提供参考。
预期成果：
通过本研究，预期达到以下几个方面的成果：
1. 研究多小区联合处理算法，提出适用于LTE-A系统的算法，并探究其协作机制；
2. 分析LTE-A系统中的小区间干扰问题，并提出有效的解决策略；
3. 进行算法的仿真实验，验证算法的性能，并进行对比分析；
4. 提出多小区联合处理算法在实际应用中的前景和潜力，并为未来通信技术的发展提供参考意见。
关键词：
LTE-A系统、多小区联合处理、小区间干扰、资源分配、仿真实验。
TD-LTE小区间资源调度机制以及算法研究的开题报告
TD-LTE小区间资源调度机制以及算法研究的开题报告一、研究背景随着移动通信技术的不断发展，TD-LTE技术作为4G通信技术标准之一，具有高速率、低延迟、频谱利用率高等优势，被广泛应用于移动通信领域。
TD-LTE网络中，基站将网络划分为多个小区进行管理，为满足不同用户的需求，需要对小区间进行资源调度。
资源调度是一种动态的过程，通过合理分配小区之间的资源，实现用户服务的最优化。
二、研究目的本课题旨在研究TD-LTE小区间资源调度机制和算法，探讨如何在保证网络服务质量的前提下，提高网络资源利用率，降低网络拥塞程度，优化网络性能。
三、研究内容1. TD-LTE小区间资源调度机制的研究针对TD-LTE网络中不同小区之间资源的分配问题，研究小区间资源调度机制。
首先分析TD-LTE网络中小区间资源分配的特点，结合网络负载等信息，设计合理的调度模型，并通过仿真实验验证模型的可行性和合理性。
2. TD-LTE小区间资源调度算法的研究研究TD-LTE小区间资源调度算法，探讨如何通过算法实现小区之间的资源优化分配。
主要包括基于遗传算法和混沌粒子群算法的调度算法研究，通过比较不同算法的优劣，选择最合适的算法实现小区间资源调度，为TD-LTE网络提供更高效的资源利用方案。
四、研究方法本研究主要采用文献研究、实验仿真和算法设计等方法。
1. 文献研究通过查阅TD-LTE网络资源调度方面的相关文献，了解当前研究和应用情况，以及存在的问题和挑战，为研究提供支撑和依据。
2. 实验仿真利用MATLAB、NS2等软件平台，构建TD-LTE网络的仿真模型，验证不同调度策略的性能，保证研究结果的准确性和有效性。
3. 算法设计设计基于遗传算法和混沌粒子群算法的TD-LTE小区间资源调度算法，通过仿真实验验证不同算法的优劣，选择最优方案。
五、研究意义本研究旨在提高TD-LTE网络中小区间资源利用率，降低网络拥塞程度，优化网络性能，具有重要的理论意义和实践应用价值。
LTE系统跨层资源分配算法研究的开题报告
LTE系统跨层资源分配算法研究的开题报告一、研究背景LTE系统作为第四代移动通信系统的代表，已经成为移动通信领域的主流技术。
在LTE系统中，由于数据传输不断增加，网络资源的分配与调度变得越来越复杂。
当前，基于层次化调度的资源分配算法已经被广泛应用。
然而，这些算法只考虑了单层资源的分配，而没有很好地解决不同层次之间的资源分配问题。
因此，研究跨层资源分配算法已经成为了当前研究的热点之一。
二、研究目的本研究旨在探究LTE系统跨层资源分配算法，在现有算法的基础上进一步提高资源利用率和系统性能，提高用户体验，提高网络的吞吐量和可靠性。
具体目标如下：1.分析现有跨层资源分配算法的局限性和不足之处；2.提出一种基于跨层调度的资源分配算法，并通过仿真和实验验证算法的有效性；3.探究算法在不同网络场景下的适用性，如高速移动、室内覆盖等场景。
三、研究内容本研究的内容主要包括以下三个方面：1.跨层资源调度算法的基本原理首先，本研究将分析已有的跨层调度算法的基本原理，包括功控、速控等算法，并提出一种新的跨层资源调度算法。
2.算法的验证与分析在分析算法的基本原理之后，本研究将通过理论仿真和实验验证算法的有效性。
在验证过程中，本研究将分析算法的调度效率、资源利用率、端到端延迟等性能指标，并与已有的跨层调度算法作比较。
3.算法在不同场景下的适用性研究最后，本研究将进一步探究算法在不同的网络场景下的适用性，如高速移动、室内覆盖等场景。
同时，本研究将针对不同的场景，提出相应的算法优化策略。
四、研究方法本研究将采用文献综述、理论分析、仿真实验等多种方法进行研究。
1.文献综述：本研究将对相关文献进行综述，了解现有跨层调度算法的基本原理和不足之处。
2.理论分析：本研究将基于已有算法的基础上，提出一种新的跨层资源调度算法，并对算法进行理论分析。
3.仿真实验：本研究将通过仿真和实验验证算法的有效性，并分析算法在不同网络场景下的适用性。
五、预期结果和意义通过研究，本研究将提出一种基于跨层调度的资源分配算法，并通过仿真和实验验证算法的有效性。
LTE网络中基于博弈论的资源分配及网络选择算法研究中期报告
LTE网络中基于博弈论的资源分配及网络选择算法研究中期报告一、研究背景随着移动通信技术的发展，LTE网络已经成为当前移动通信网络的主流技术之一。
然而，LTE网络的资源分配和网络选择问题一直是当前研究的热点问题之一，关乎用户在网络中获得的服务质量和网络性能。
传统的资源分配和网络选择算法往往是基于贪心策略或者启发式算法，难以充分考虑到多方面因素对用户服务造成的影响。
针对这种情况，基于博弈论的资源分配及网络选择算法应运而生。
二、研究内容本研究旨在探究基于博弈论的资源分配及网络选择算法在LTE网络中的应用，主要研究内容包括：1. 建立LTE网络的资源分配与网络选择模型根据LTE网络的特点和资源分配的目标，构建了包含用户、基站和系统三个主要参与方的资源分配与网络选择模型。
采用博弈论的相关理论及方法，考虑不同参与方之间的利益关系及竞争关系，对模型进行优化。
2. 分析LTE网络资源分配及网络选择的博弈策略结合实际情况，针对不同用户的服务需求和基站的覆盖范围等因素，分析了不同参与方在资源分配与网络选择中可能采取的策略。
通过建立博弈模型，确定了不同博弈策略的收益与代价。
3. 设计基于博弈论的资源分配及网络选择算法基于博弈论分析结果，提出了一种基于博弈论的资源分配及网络选择算法。
该算法考虑了不同参与方的利益和竞争关系，通过策略与收益之间的博弈，找出最优的资源分配与网络选择方案。
三、研究进展目前，本研究已经完成了LTE网络资源分配与网络选择模型的构建，并初步分析了不同参与方的博弈策略。
接下来，我们将进一步深入研究基于博弈论的资源分配及网络选择算法，并验证算法在LTE网络中的实际效果。
四、研究意义本研究对于解决LTE网络资源分配和网络选择等问题具有重要意义。
一方面，基于博弈论的算法可以充分考虑不同参与方的利益和竞争关系，从而找出更优的解决方案。
另一方面，该算法可以为未来5G网络的资源分配和网络选择提供有益的参考。
基于QoS的TD-LTE集群通信系统资源分配算法
基于QoS的TD-LTE集群通信系统资源分配算法
孙聪;唐宏;魏忠祥;周到
【期刊名称】《广东通信技术》
【年(卷),期】2012(32)12
【摘要】针对集群通信系中的有QoS(Quality of Service)要求的业务和尽力而为的业务,提出一种基于QoS的TD-LTE集群通信系统下行的一个调度周期内的资源分配算法.根据业务性质主要分为两步:先为有QoS要求的业务,根据其所对应的群组大小、平均信道增益和最低速率要求进行优先级的确定,然后依次分配
RB(ResourceBlock);其次为尽力而为的业务进行分配,考虑群组大小把剩下的RB 分配给能为系统带来最大容量的群组.仿真结果表明:该算法能满足其QoS,同时系统容量比传统的一些调度算法有一定的提升,且比传统算法更适合集群通信系统.【总页数】6页(P74-79)
【作者】孙聪;唐宏;魏忠祥;周到
【作者单位】重庆邮电大学
【正文语种】中文
【相关文献】
1.基于一种公平性的智能电视系统资源分配算法研究 [J], 邓源基
2.基于组合投资理论与主用户QoS保证的认知系统资源分配算法 [J], 梁辉;赵晓晖
3.基于业务QoS保证的中继系统资源分配算法 [J], 张继荣;李永宝
4.基于SWIPT的多用户双向中继协作系统资源分配算法研究 [J], 周方; 张信明
5.基于校园场景的中继系统资源分配算法 [J], 赵婉君
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LTE网络中绿色自适应资源分配方案
LTE网络中绿色自适应资源分配方案LTE（Long Term Evolution）是一种高速无线通信技术，为4G移动通信技术的一种。
在LTE网络中，绿色自适应资源分配方案是为了提高网络效率和能源利用率，减少网络碳排放量而采取的一种方法。
下面是对LTE网络中绿色自适应资源分配方案的详细介绍。
绿色自适应资源分配方案是利用LTE网络中的自适应调制与编码（AMC）技术和功率控制（PC）技术来实现的。
在传统的资源分配方案中，无论用户的需求和网络的负载如何变化，资源都是固定分配的。
而绿色自适应资源分配方案可以根据用户需求和网络负载的变化，动态调整资源的分配方式，从而提高网络的效率和能源利用率。
在LTE网络中，AMC技术通过调整调制方式和编码方式来适应不同信道条件下的传输需求。
绿色自适应资源分配方案可以根据当前信道条件的好坏，灵活选择合适的调制方式和编码方式。
当信道条件较好时，可以采用高阶调制和编码方式，以提高传输速率；当信道条件较差时，可以降低调制和编码的复杂度，以提高信号的可靠性。
通过优化调制和编码方式的选择，绿色自适应资源分配方案可以在不降低用户体验的情况下，更有效地利用无线频谱资源。
另外，绿色自适应资源分配方案还可以结合功率控制技术来降低功率消耗。
在LTE网络中，功率控制技术可以根据用户的接收信号质量和信道条件的变化，动态调整发送功率。
绿色自适应资源分配方案可以根据当前网络的负载情况，灵活控制发送功率。
当网络负载较低时，可以降低发送功率；当网络负载较高时，可以增加发送功率。
通过优化发送功率的调整，绿色自适应资源分配方案可以在不降低信号质量的情况下，减少网络的能耗。
综上所述，LTE网络中的绿色自适应资源分配方案可以提高网络的效率和能源利用率，减少网络碳排放量。
通过灵活调整调制和编码方式以及控制发送功率，绿色自适应资源分配方案可以在不降低用户体验的情况下，更有效地利用无线频谱资源和减少网络能耗。
未来，随着LTE网络的进一步发展和技术的不断创新，绿色自适应资源分配方案有望得到更广泛的应用，并在其他无线通信技术中推广使用，以进一步提高网络的可持续发展性能。
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优先出版 计 算 机 应 用 研 究 第32卷
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基金项目：国家863重点项目(2012AA011402)；新世纪优秀人才支持计划项目(NCET-10-0294)
作者简介：付锦华(1990-)，男，硕士，主要研究方向为无线移动通信(fooboy163@)；黄晓燕(1982-)，女，讲师，博士，主要研究方向为无线网络资源优化；吴凡(1978-)，男，讲师，硕士，主要研究方向为无线网络资源管理；冷甦鹏(1978-)，男，教授，博士，主要研究方向为无线自组织网络、无线传感器网络，宽带无线接入网络与下一代移动通信网络；马立香(1964-)，女，副教授，主要研究方向为无线自组织网络、下一代移动通信网络．
基于能效的多小区LTE 系统资源分配算法
付锦华，黄晓燕，吴 凡，冷甦鹏，马立香
(电子科技大学 通信与信息工程学院，成都 611731)
摘 要：针对多小区LTE 移动通信系统，考虑用户的最小速率要求，以最大化系统能效为目标，提出了一种迭代式的资源分配算法，通过不断迭代子信道分配和功率控制两个子过程来优化系统能效。
针对子信道分配问题，提出了一种基于三种基本模式的子信道调整算法；针对功率控制问题，建立了多小区非合作博弈模型，理论证明了纳什均衡点的存在性，并设计了算法收敛于该纳什均衡点。
仿真结果表明，与多小区最大化系统吞吐量算法相比，本文提出的算法获得了明显的能效增益，同时也达到了较好的系统吞吐量。
尤其在强干扰环境下该算法的优势更加明显。
关键词：多小区；能效；子信道调整；功率控制；非合作博弈论； 中图分类号：TN92 文献标志码：A
Energy-efficient resource allocation in multi-cell LTE system
FU Jin-hua, HUANG Xiao-Yan, WU Fan, LENG Su-peng, MA Li-xiang
(School of Communication & Information Engineering, University of Electronic Science & Technology of China, Chengdu, China)
Abstract: This paper proposed an algorithm for resource allocation under QoS constraint with the objective of maximizing system energy-efficiency in multi-cell LTE system. It is decomposed into sub-channel adaption and power control, and solved iteratively. The sub-channel adaption algorithm is based on three basic models. We develop an non-cooperative game for energy-efficient power control, and prove the existence of equilibrium. We also propose an algorithm to solve the equilibrium. Simulation results demonstrate that the performance of our algorithm is more energy-efficient and result in most throughput. The performance is better in stronger interference.
Key Words: multi-cell; energy-efficiency; sub-channel adaption; power control; non-cooperative game 0 引言
随着移动互联网的快速发展，用户对高速率数据业务的需求越来越大。
LTE 移动通信网络保障了视频、游戏、多媒体社交等应用的良好用户体验，同时也带来了巨大的能耗问题[1]。
构建绿色网络，节省网络能耗已成为LTE 系统的一个研究热点。
目前针对LTE 系统的资源分配技术的研究多以最大化系统吞吐量，最小化时延[2-4]，或是用户公平性[4-7]为优化目标，而以最大化能效为目标的研究则较少，尤其针对存在小区间干扰的多小区系统的基于能效的资源分配问题的研究更是有限。
以最大化系统吞吐量为目的的LTE 单小区网络的功率控制算法已经有了比较成熟的研究，理论证明注水算法能够实现单小区系统吞吐量的最大化[8]，文献[9]中针对单小区功率控制问题设计了基于梯度下降法的GABS 算法，但它缺乏对于用户QoS 的保障。
文献[10]中提出的BPA 算法有效的解决了固定子信道分配方案条件下的单小区能效优化问题。
而多小区网络的资源分配问题
与单小区相比，最大的挑战在于多小区环境下的同频干扰，而干扰抑制是解决同频干扰的常见技术手段。
文献[11]中提出了基于顺序博弈的SGC/RRM 算法，但博弈小区的配对过于依赖于频域规划，且该算法仅减小了两个博弈小区之间的干扰。
基于非合作博弈论进行功率控制则是国内外研究的另一热点，文献[12]中提出了一种基于非合作博弈论的分布式资源分配算法，但对用户最小速率约束的保障使得可行域变得复杂，给这种基于搜索的算法带来了障碍。
基于上述分析，本文针对同构多小区系统的资源分配算法的能效特性进行研究，提出了一种子信道和功率联合分配算法（MEERA ），该算法在保障用户最小速率要求基础上，优化系统整体能效。
算法基本思想是：采用排序优化的贪婪算法进行子信道初始分配，在此基础上采用功率控制和子信道调整相结合，以迭代的方式进一步优化系统能效。
另外，针对功率控制，本文建立了静态完全信息非合作博弈模型，并证明了该模型纳什均衡点的存在性，在此基础上，提出了MICPC 算法获得纳什
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