WO2016000149A1 - Procédé et appareil de programmation multipoint coordonnée décentralisée, avec optimisation de la performance statistique - Google Patents

Procédé et appareil de programmation multipoint coordonnée décentralisée, avec optimisation de la performance statistique Download PDF

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Publication number
WO2016000149A1
WO2016000149A1 PCT/CN2014/081216 CN2014081216W WO2016000149A1 WO 2016000149 A1 WO2016000149 A1 WO 2016000149A1 CN 2014081216 W CN2014081216 W CN 2014081216W WO 2016000149 A1 WO2016000149 A1 WO 2016000149A1
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node
interest
cluster
neighbor nodes
transmission state
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PCT/CN2014/081216
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English (en)
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Zhenning Shi
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Orange
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Priority to PCT/IB2015/001114 priority patent/WO2016001742A1/fr
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/12—Wireless traffic scheduling

Definitions

  • the invention relates to a method for scheduling user equipment in a wireless communications network, such as, for example, a Long Term Evolution-Advanced (LTE-A) network (as described in the series of standards Ts36.xxx release 10 and further issued by the third generation partnership program (3GPP)). More precisely, the invention relates to a method for scheduling user equipment, which allows mitigating the downlink interference between cells in a homogeneous or heterogeneous communications network.
  • LTE-A Long Term Evolution-Advanced
  • 3GPP third generation partnership program
  • LTE-A Long Term Evolution-Advanced
  • LTE-A can significantly enhance the spectrum efficiency and lift the performance bars for mobile subscribers. Nevertheless, its performance is limited by the interference leakage from neighbor cells, also known as inter-cell interference (ICI). Neighbor cells operating on the same spectrum actually generate ICI, which can degenerate user signal quality. The detrimental effect is especially significant to users on the cell edge, causing very poor service or even service disruption.
  • ICI inter-cell interference
  • coordinated scheduling consists in mitigating ICI by coordinating resource allocation between cells.
  • centralized scheduler cooperating cells send reports, e.g., user channel state information (CSI) and/or cell benefit metric associated with certain resource allocation, to a central controller.
  • CSI user channel state information
  • cell benefit metric associated with certain resource allocation
  • a coordination module residing in each node is responsible for coordinating with other nodes in the network, based on exchanged signaling between the nodes.
  • Nodes may either be associated to local cells, or may be local control units, which are responsible for resource allocation for a number of inter-connecting cells.
  • the signaling is transmitted over enhanced X2 interface or proprietary inter-node interface.
  • a drawback of centralized scheduling is that it introduces a central controller, which is a new network element and thus implies to modify networks architecture.
  • De-centralized CS as described by R. Agrawal et al.in “Centralized and Decentralized Coordinated Scheduling with Muting,” IEEE VTC Spring, 2014,has less impact on the network architecture. It typically relies on the information of neighbor cell performance metrics (via inter- node signaling) to make mutual-beneficial decisions. Nevertheless, explicitly calculated performance metrics are known to be dependent on the resource usage of node-of-interest (where the de-centralized controller is sited) as well as resource usage in cooperating nodes, thus not suitable for de-centralized implementation.
  • a method for scheduling user equipment in an access node of a communication network comprising several access nodes which comprises, for at least one of said nodes, called a node-of-interest, belonging to a cluster of cooperating nodes:
  • the method according to an embodiment of the invention thus relies on a novel and inventive approach of de-centralized coordinated scheduling in a wireless network, such as, for example, a LTE-A network (it must be noted that, although embodiments of the invention are described throughout this document in the context of LTE-A networks, the scope of the invention is not limited to this peculiar type of network and may find applications in any communication network where de-centralized coordination between cooperating nodes is of interest). Actually, such a method allows considering all likelihoods of resource allocation in neighbor cells and deriving decisions based on these statistics.
  • resource coordination is performed to optimize the statistical performance metrics of cooperating nodes.
  • a method is described from the point of view of a peculiar node in the cluster, called the node-of-interest, it must be understood that every node in the cluster of cooperating nodes becomes a node-of-interest at a certain point in time, and thus implements the method described here above and here after.
  • each cooperating node in the cluster implements algorithms to calculate the statistical performance metrics associated with the node itself (conditioned either on its own transmission state or the transmission state of neighbor cells), as well as algorithms to determine the resource allocation for the associated node with the statistical performance metrics calculated for the node-of-interest and those received from cooperating nodes.
  • Such a method for scheduling user equipment allows enhanced de-centralized coordination, as compared to known prior art techniques, leading to improved interference management (for operators) and better user experience (for subscribers) while introducing minor impact to existing radio access network architecture.
  • no centralized controller is needed.
  • said method for scheduling comprises sending to said neighbor nodes in said cluster said transmission state determined for said node-of-interest and receiving from said neighbor nodes in said cluster corresponding transmission states determined by and for said neighbor nodes on the basis of computed and received statistical performance metrics, and said information relating to transmission states of said neighbor nodes in said cluster comprises said transmission states determined by and received from said neighbor nodes in said cluster.
  • scheduling user equipment may be implemented by accurately estimating user SINR (Signal Plus Interference to Noise Ratio) and scheduling priorities with explicit knowledge of cell cluster transmission states. Nevertheless, signaling of transmission state information is needed and this signaling is constrained by the end-to-end backhaul latency between cells in legacy networks.
  • SINR Signal Plus Interference to Noise Ratio
  • said information relating to transmission states of said neighbor nodes in said cluster comprises said likelihoods of transmission state of said neighbor nodes in said cluster.
  • This alternate embodiment is not constrained by this additional backhaul latency, but only statistical likelihood information of neighbor cell transmission states is accessible for scheduling user equipment, which may impact the scheduling performance.
  • said transmission state determined for said node-of-interest maximizes a sum of the statistical performance metrics of said node-of-interest and of said neighbor nodes in said cluster.
  • such a method for scheduling also comprises a state expurgation mechanism allowing taking into account only the most probable transmission states of said nodes in said cluster, called survivor states.
  • the most probable transmission states are evaluated based on the information on transmission states of the neighbor nodes received from the neighbor nodes.
  • said state expurgation mechanism relies on a defined state-expurgation threshold, and said survivor states are the transmission states of said nodes in said cluster whose likelihood is above said threshold.
  • Such a threshold may be advantageously set according to the computation complexity, which is aimed at.
  • such a method also comprises setting a maximum number of survivor states. Hence, the computing complexity can be further reduced.
  • said communication network is a homogeneous network
  • said nodes are base transceiver stations of said homogeneous network.
  • the communication network is a homogeneous LTE-A network
  • the nodes are eNodeBs (for evolved NodeBs), which each manage several radio cells (also called macro cells), corresponding to sectors (each sector corresponding to one or several radio antennas pointing to the same direction) of a radio site.
  • the method described above may be implemented at a radio site level or at a cell level, in order to either achieve de-centralized coordination between cooperating cells or between cooperating sites.
  • said communication network is a heterogeneous cloud radio access network comprising at least one macro cell and at least one small cell having at least partially overlapping coverage
  • said nodes are central offices aggregating a base transceiver station of one of said macro cells, called a parent macro cell, and a baseband unit of one of said small cells having at least partially overlapping coverage with said parent macro cell, if any.
  • the communication network is a hybrid C- AN comprising macro-cells each associated to eNodeBs, and small cells whose coverage is overlapped with that of macrocells.
  • Baseband units (BBUs) of small cells are positioned at a central office (CO), which is collocated with eNodeB of the parental macro cell.
  • CO central office
  • macro eNodeBs are inter-connected with legacy backhauls
  • BBUs of macrocells are located at eNodeBs according to the conventional RAN architecture.
  • the method according to such an embodiment of the invention offers a de-centralized approach based on statistical performance metric, which can be applied to inter-site coordination in hybrid C-RAN.
  • the invention also concerns a computer program, in particular a computer program on or in an information medium or memory, suitable for implementing the method according to embodiments of the invention.
  • This program can use any programming language, and be in the form of source code, object code, or of intermediate code between source code and object code such as in a partially compiled form, or in any other desirable form for implementing the configuration method according to the invention.
  • the information medium may be any entity or device capable of storing the programs.
  • the medium can comprise a storage means, such as a ROM, for example a CD ROM or a microelectronic circuit ROM, or else a magnetic recording means, for example a diskette (floppy disk) or a hard disk.
  • the information medium may be a transmissible medium such as an electrical or optical signal, which may be conveyed via an electrical or optical cable, by radio or by other means.
  • the program according to embodiments of the invention may in particular be downloaded from a network of Internet type.
  • the invention also concerns an access node, called a node-of-interest, belonging to a cluster of cooperating nodes of a communication network, said node-of-interest comprising:
  • a sending unit for sending to neighbor nodes in said cluster a likelihood of transmission state of said node-of-interest
  • a receiving unit for receiving from said neighbor nodes in said cluster likelihoods of transmission state of said neighbor nodes
  • a computing unit for computing a statistical performance metric of said node-of- interest conditioned on its own likelihood of transmission state and a statistical performance metric of said node-of-interest conditioned on likelihoods of transmission state of said neighbor nodes in said cluster, called S2;
  • said sending unit also allowing sending to neighbor nodes in said cluster said statistical performance metric S2 and said receiving unit also allowing receiving from said neighbor nodes in said cluster corresponding statistical performance metrics of said neighbor nodes conditioned on said likelihood of transmission state of said node-of-interest ;
  • a deciding unit for determining a transmission state of said node-of-interest on the basis of said computed and received statistical performance metrics
  • the invention further relates to a communication system comprising user equipment receiving and transmitting data from and to access nodes in a communication network, said communication system comprising a cluster of cooperating nodes, comprising a node-of- interest and neighbor nodes,
  • said node-of-interest comprising:
  • a sending unit for sending to said neighbor nodes a likelihood of transmission state of said node-of-interest
  • a receiving unit for receiving from said neighbor nodes likelihoods of transmission state of said neighbor nodes
  • a computing unit for computing a statistical performance metric of said node-of- interest conditioned on its own likelihood of transmission state and a statistical performance metric of said node-of-interest conditioned on likelihoods of transmission state of said neighbor nodes, called S2;
  • said sending unit also allowing sending to said neighbor nodes said statistical performance metric S2 and said receiving unit also allowing receiving from said neighbor nodes corresponding statistical performance metrics of said neighbor nodes conditioned on likelihoods of transmission state of their own neighbor nodes in said cluster;
  • a deciding unit for determining a transmission state of said node-of-interest on the basis of said computed and received statistical performance metrics
  • a scheduling unit for scheduling user equipment served by said node-of-interest on the basis of said transmission state determined for said node-of-interest and on information relating to transmission states of said neighbor nodes.
  • Figure 1 illustrates inter-cell signaling according to a de-centralized Coordinated Scheduling method of the prior art
  • Figure 2 illustrates inter-cell signaling according to statistical coordinated multipoint scheduling based de-centralized coordination, according to an embodiment of the invention
  • Figure 3 shows the different steps of a method for scheduling in the homogeneous network of figure 2, according to an embodiment of the invention
  • Figure 4 illustrates a transmission state expurgation mechanism according to an embodiment of the invention
  • Figure 5 illustrates a known hybrid C- AN architecture for a communication system comprising seven eNodeBs
  • Figure 6A and 6B respectively illustrate a cooperation cluster of dynamic elCIC according to the prior art and of enhanced elCIC according to an embodiment of the invention
  • Figure 7 illustrates a cluster of cells involved in performance metric calculation for a peculiar cell in the embodiment of figure 6B;
  • Figure 8 shows the different steps of a method for scheduling in the hybrid C-RAN network of figure 5, according to an embodiment of the invention.
  • Figure 9 shows the structure of an access node implementing the methods of scheduling user equipment.
  • FIG. 1 shows a homogeneous network consisting of seven eNodeBs, denoted as R-l, R-2, R-3, Y-l, Y-2, Y-3 and B-l, respectively.
  • each eNodeB is illustrated by a set of three arrows with an angular spacing of 120°, along with a label showing its name.
  • an eNodeB for evolved Node B
  • LTE Long Term Evolution
  • LTE Advanced standard for Long Term Evolution
  • eNodeBs behave as a gateway between the radio antennas and the core of the LTE network, also known as EPC (Evolved Packet Core).
  • each sector, or cell is represented as a hexagon.
  • one of the three arrows of the associated set of arrows points towards one of the three cells a , ⁇ and ⁇ .
  • C B _i_ a designates cell a of eNodeB B-l.
  • H B _i_ a is the transmission state of cell-( B— l— a ), with 0 indicating the cell is OFF and
  • N(B - 1 - a) designates the set of tier-1 neighbors of C B _i_ a . It can be seen from
  • N(B - 1 - a) , C B _ x _ , C B _ X _ Y , C R _ 3 _ y , C Y _ x _ , C Y _ x _ y ⁇ (1)
  • transmission state hypothesis H B _ x _ a For instance, it can be the proportional fair (PF) metric of C B _ x _ a .
  • PF proportional fair
  • each cell determines the transmission state to maximize the total performance metric of itself and its neighbors, given by:
  • the first term on the right-hand-side (RHS) of the equation denotes the performance metric (PM ) of cell C B _y_ a and the second term on the RHS is the performance metric of neighbor cells.
  • Neighbor cell PMs can be obtained via inter-cell signaling, as illustrated by the arrows pointing towards cell C B _i_ a ⁇ n Figure 1, and coming from ceNsCy.. ⁇ , C Y _ ⁇ ,
  • the inherent drawback with autonomous muting scheme is the difficulty in calculating the performance metric PM of a cell conditioned only on the transmission state of the cell-of-interest C B _i_ a .
  • PM of cell C B _y_ a depends not only ⁇ 3 ⁇ 4_ ⁇ ⁇ , but also on the transmission states of neighbor cells c £ N(B— I— a) .
  • the explicit calculation of PM should be ⁇ p[c B _ x _ a
  • an embodiment of the invention relies on a new performance metric and an innovative de-centralized resource coordination scheme with the newly defined PM.
  • the new PM should satisfy the following two criteria:
  • the performance metric is explicitly dependent on the transmission state of cell-of- interest only to allow for local decision made by the de-centralized controller.
  • Neighbor cell transmission state should be implicitly accounted for in the performance metric whereby the de-centralized decision is made based on the likely transmission states of cooperating cells.
  • an embodiment of the invention introduces a statistical PM which is the expected cell performance metric conditioned only on the transmission state of the cell-of- interest
  • transmission state of cell C B _i_ a is chosen to optimize the overall statistical performance metrics as:
  • P ⁇ H N ⁇ B _i_ a ⁇ is the joint probability of transmission states for neighbor cells c & N(B - ⁇ - a) .
  • the joint probability can be calculated by multiplying the transmission state likelihoods associated with each cell as:
  • Figure 2 shows the coordination messages that cell C B _i_ a receives from cooperating cells, i.e., its tier-1 neighbors (i.e. cells located inside the dashed circle line on figure 2), which comprise two pieces of information of cell c e N(B - I - a) :
  • SI statistical performance Information
  • S2 is used by the local coordinator in determining transmission state ⁇ ( ⁇ _ ⁇ _ ⁇ ) as in equation (7).
  • Figure 3 presents the statistical de-centralized coordination procedures for a three-cell cluster comprising Cell 1, Cell 2 and Cell 3. The one skilled in the art will easily extend the teachings of this example to a cluster comprising a greater number of cells.
  • each cell calculates the following performance metrics:
  • step 31 for Cell 1 Such operations are implemented in step 31 for Cell 1, step 32 for Cell 2 and step 33 for Cell 3.
  • step 34 Cells exchange statistical PM values corresponding to information (S2) in step 34. Based on statistical PM calculated and received from neighbor cells (note all these PMs are conditioned only on the transmission state of the local cell), each cell determines its transmission state resulting in the largest sum statistical PM. Such operations are implemented in step 35 for Cell 1, step 36 for Cell 2 and step 37 for Cell 3.
  • each cell schedule users given the transmission state determined from the previous step and the likelihoods of neighbor cell transmission states (i.e., information (SI)).
  • SI information
  • cells exchange the transmission state they have selected (step 41) and then each cell schedules users according to the transmission states of itself as well as of neighbor cells (step 42 for Cell 1, step 43 for Cell 2 and step 44 for Cell 3).
  • LTE scheduler can accurately estimate user SIN (Signal to Interference plus Noise ratio) and scheduling priorities with explicit knowledge of cell cluster transmission states. Nevertheless, signaling of transmission state information is needed and this signaling is constrained by the end-to-end backhaul latency between cells in legacy networks.
  • the first embodiment is not constrained by this additional backhaul latency but only statistical likelihood information of neighbor cell transmission states is accessible to LTE scheduler, which may impact the scheduler performance.
  • SI Signaling
  • the PM function must be evaluated over all possible transmission states of a number of cooperating cells. Therefore, the complexity is exponential w.r.t. the number of cells to marginalize over. For example, the complexity of calculating #>(C£_ j _ a
  • Figure 4 shows an example of locating survivor states for a four-cell cluster comprising cells a, b, c and d.
  • the man skilled in the art will easily adapt this example to a cluster comprising any other number of cells.
  • the transmission state likelihood associated with each cell is specified as in Table I.
  • leaf nodes denote the composite transmission states of cells up to this level. It is stipulated the left child of each leaf node corresponds to the 'ON' status for the cell, while the right child is for the 'OFF' state.
  • the likelihood of leaf nodes can be calculated by multiplying all probability figures associated with the paths from the top to the leaf node.
  • the probability of leaf nodes is compared against the expurgation threshold ⁇ — 0.01 . If the associated probability is below the threshold, the leaf node will be discarded, so are all the children of this node.
  • FIG. 5 shows a hybrid C-RAN system consisting of seven eNodeBs denoted as R-l, R-2, R-3, Y-l, Y-2, Y-3 and B-l, respectively.
  • each eNodeB is illustrated by a set of three arrows with an angular spacing of 120°, along with a label showing its name.
  • each macrocell is represented as a hexagon.
  • one of the three arrows of the associated set of arrows points towards one of the three cells a , ⁇ and ⁇ .
  • low-power small cell nodes there are also a number of low-power small cell nodes (denoted by black triangles in figure 5) whose coverage is overlapped with that of macrocells. It is actually recalled that in heterogeneous or hybrid networks (HetNet), small cells are introduced, as a complement to macro cells. Such low power nodes (i.e. femto, pico, relay nodes) allow to significantly offload the traffic from macro cells, and to enhance the network coverage and throughput.
  • macrocells and small cells share the same LTE spectrum, which allows increasing the spectral efficiency, but also gives rise to cross-tier interference in the downlink.
  • an heterogeneous network as described in this document usually comprises macro eNodes B (eN Bs), which are deployed for initial coverage of the network by macro cells, and pico access nodes or HeN Bs, which serve small cells, and are added to the network for capacity growth and better user experience.
  • eN Bs macro eNodes B
  • HeN Bs pico access nodes
  • Cloud RAN architecture is assumed by the small cell layer and black triangles in Figure 5 stand for the antennas and remote radio heads (RRHs) of low-power nodes.
  • Baseband units (BBUs) of small cells are detached from RRHs and are positioned at a central office (CO), which is collocated with eNodeB of the parental macrocell.
  • Small cell RRHs are connected to CO via high- bandwidth, low-latency transport links which are named as fronthaul (solid curves linking a black triangle, i.e. a small cell RRH, to a macro eNodeB in Figure 5).
  • Fronthaul solid curves linking a black triangle, i.e. a small cell RRH, to a macro eNodeB in Figure 5).
  • Signals received by RRHs are filtered and digital samples are sent over fronthaul to CO for processing.
  • ABS almost blank sub-frame
  • Figure 6A shows the cooperation cluster of dynamic elCIC for eNodeB-(6-l)(shown as the three hexagons located roughly inside the dashed circle line), which is confined to three co-sited macrocells (i.e., C B _i_ a , C ⁇ _ ⁇ _ ⁇ and C B _i_ y ) and small cells aggregated at the same site.
  • Figure 6A hence corresponds to prior art.
  • dynamic elCIC scheme can be formulated as:
  • Transmission status Hg_ j not only affects the performance of ceWsc e Q B _i , but also those in the coverage of neighbor (macro)-sites. Inter-sites cells subject to the interference of eNodeB-(S-l) are marked with circles in Figure 6B. To selectHg ⁇ resulting in overall optimum performance, performance of these cells should also be taken into account.
  • elCIC for hybrid C- AN is enhanced by taking into account performance of both macro and small cells in neighbor sites and employing statistical performance optimization.
  • the new elCIC scheme then chooses the transmission state that optimizes the overall statistical performance of the cooperation cluster as:
  • the first sum on the right hand side stands for the statistical PM of cells in the coverage of eNodeB-(S-l), and the second sum denotes the performance of cells in the coverage of neighbor sites.
  • cell C Y _ 2 - A and C r _2_ j are interfering with cells in eNodeB-(S-l), while cell C _ 2 _ r causes little interference to cell c and is not considered.
  • the relevant neighbor site transmission states and the associated likelihoods in equation (14) can be defined as below.
  • Figure 8 presents schematically the steps of the method for scheduling user equipment according to an embodiment of the invention in the context of elCIC for hybrid C-RAN with statistical performance optimization, when considering three sites, namely Site-R, Site-Y and Site- B.
  • Site-R Site-R
  • Site-Y Site-Y
  • Site- B Site- B
  • each site calculates the following performance metrics:
  • step 81 for Site-R Such operations are implemented in step 81 for Site-R, step 82 for Site-Y and step 83 for Site-B.
  • Sites exchange statistical PM values with respect to different transmission state hypothesis in step 84. Based on statistical PM calculated and received from neighbor sites, each site determines its transmission state resulting in the largest statistical sum PM. Such operations are implemented in step 85 for Site- , step 86 for Site-Y and step 87 for Site-B.
  • Each site then schedule users given the transmission state determined from the previous step and the likelihoods of neighbor sites transmission states. Such operations are implemented in step 88 for Site-R, step 89 for Site-Y and step 90 for Site-B.
  • Figure 9 shows the structure of an access node implementing the methods of scheduling user equipment as described above.
  • the access node shown on figure 9, also called a node-of-interest, comprises a sending unit Tx 91, which sends a likelihood of its own transmission state to neighbor nodes in a cluster of cooperating nodes of a communication network.
  • the access node of figure 9 further comprises a computing unit ⁇ 93 for computing a statistical performance metric of the node-of-interest conditioned on its own likelihood of transmission state and a statistical performance metric of the node-of-interest conditioned on likelihoods of transmission state of the neighbor nodes in the cluster, called S2.
  • the sending unit Tx 91 also allows sending to neighbor nodes in the cluster the statistical performance metric S2.
  • the receiving unit Rx 92 also allows receiving from the neighbor nodes in the cluster corresponding statistical performance metrics of the neighbor nodes conditioned on transmission state of node-of-interest .
  • the sending unit Tx 91 also allows sending to neighbor cells in the cluster the transmission state of said node-of-interest
  • the receiving unit Rx 92 also allows receiving from the neighbor nodes in the cluster the transmission state of neighbor nodes in the cluster.
  • the access node of figure 9 also comprises a deciding unit DEC 94 for determining a transmission state of said node-of-interest on the basis of said computed and received statistical performance metrics respectively received from the receiving unit Rx 92 and from the computing unit ⁇ 93.
  • a scheduling unit SCH 95 allows scheduling user equipment served by the access node of figure 9 on the basis of the transmission state determined by the deciding unit DEC 94 and on information relating to transmission states of said neighbor nodes in said cluster.
  • the deciding unit DEC 94 provides the sending unit Tx 91 with the transmission state it has determined for the node-of-interest, in order for the sending unit Tx 91 to send it to neighbor nodes.
  • Embodiments of the invention described above are directed to methods and apparatus to enhance de-centralized coordination between cooperating nodes (e.g., cells, sites, cluster of cells etc).
  • cooperating nodes e.g., cells, sites, cluster of cells etc.
  • a statistical performance-based approach is proposed to model the optimization object function for resource coordination. By doing this, the overall performance of the cooperation cluster can be described as a function of a local node transmission state which lends itself to decentralized implementation.
  • the de-centralized coordination approach can be applied to the base stations in the homogenous networks to enable autonomous muting. It is also applicable to the central office (where macro and small cell BBUs are aggregated) in hybrid C- AN systems to enable inter-site coordination. In both scenarios, new inter-site signaling including transmission state likelihood and statistical performance metric associated with neighbor nodes are defined.

Abstract

Le procédé concerne une nouvelle approche de planification coordonnée décentralisée dans un réseau sans fil (un réseau LTE-A par ex.), ladite approche tenant compte de toutes les probabilités d'attribution de ressources dans des cellules voisines et calculant des décisions à partir de ces statistiques. Le procédé selon l'invention repose sur un nouveau schéma CS centralisé comprenant : une nouvelle signalisation internodale pour acheminer des statistiques d'attribution de ressources de cellules appartenant à l'ensemble de cellules collaboratives (également appelé grappe); de nouveaux algorithmes pour calculer des mesures de performance statistique d'après la signalisation; et une programmation coordonnée exécutée dans des nœuds individuels pour attribuer des ressources d'après les mesures de performance statistique.
PCT/CN2014/081216 2014-06-30 2014-06-30 Procédé et appareil de programmation multipoint coordonnée décentralisée, avec optimisation de la performance statistique WO2016000149A1 (fr)

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CN110493826B (zh) * 2019-08-28 2022-04-12 重庆邮电大学 一种基于深度强化学习的异构云无线接入网资源分配方法

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