WO2015109455A1

WO2015109455A1 - Method and apparatus for optimizing transmission in small cell

Info

Publication number: WO2015109455A1
Application number: PCT/CN2014/071105
Authority: WO
Inventors: Ming Lei; Chaofeng LI; Tianxiang LUAN; Feifei Gao
Original assignee: Nec Corporation
Priority date: 2014-01-22
Filing date: 2014-01-22
Publication date: 2015-07-30

Abstract

Embodiments of the disclosure provide methods and apparatuses for optimizing transmission in a small cell. In a method according to embodiments of the present invention, space knowledge of interference channels between the small cell and a macro cell is learnt, and a beamforming matrix for an operation model of the small cell is determined based on the space knowledge, wherein the operation model is a model of closed access or a model of open access.

Description

METHOD AND APPARATUS FOR OPTIMIZING

TRANSMISSION IN SMALL CELL

FIELD OF THE INVENTION

[0001] Embodiments of the present invention generally relate to communication techniques. More particularly, embodiments of the present invention relate to a method and apparatus for optimizing transmission in a small cell.

BACKGROUND OF THE INVENTION

[0002] In the 4G cellular networks utilizing wider and higher spectrum bands

(e.g., TD-LTE (U.S.): 2496 MHz-2690 MHz), indoor users may experience unacceptable signal receptions due to the long distance to the macro cell backbone. Small cell networking has been viewed as a promising solution for guaranteeing the demands of rising data rates of wireless local area services and the resultant network congestion in cross-tier links. In order to aggressively utilize the limited spectrum resources, which is desirable to future wireless communications, small cells should be able to ingeniously reuse the frequency bands that are serving macro cell traffics. In such scenario, efficient technique of resource allocation and transmission optimization is essential for practically applying small cells.

[0003] There have been two basic operation models for small cells (e.g., femto cells), i.e., closed and open access. In the model of closed access, macro users cannot access a small cell base station (e.g., a femto BS) even when they are very close to each other, whereas with open access, femto BS can serve both small cell users and macro users by assuming that macro users provide sufficient cooperation with the small cell. In both cases, joint resource scheduling for multiple small cell users is substantial for enhancing small cell performance. Meanwhile, efficient interference mitigation is very challenging, due to the fact that small cell terminals are user-deployed and thus macro cell nodes cannot easily cooperate with them, resulting in the fact that the cross-tier interference is difficult to predict and alleviated.

[0004] Motivated by the ability of flexible resource allocation, orthogonal frequency division multiple access (OFDMA) has been shown as an efficient scheme for practical small cell deployments. In this context, it is necessary to optimally allocate spectrum resources for femto cell users and meanwhile guarantee that no harmful performance affection should be made to the given macro cell network.

[0005] In view of the foregoing problem, there is a need to optimize the transmission in the small cell, so as to efficiently reduce or mitigate the cross-tier interference.

SUMMARY OF THE INVENTION

[0006] To address or mitigate the above problem, embodiments of the present invention would propose to optimize the transmission in a small cell by determining a beamforming matrix to be used by the small cell, such that the cross-tier interference can be efficiently mitigated.

[0007] According to a first aspect of the present invention, embodiments of the invention provide a method for optimizing transmission in a small cell. The method may comprise steps of: learning space knowledge of interference channels between the small cell and a macro cell; and determining a beamforming matrix for an operation model of the small cell based on the space knowledge, wherein the operation model is a model of closed access or a model of open access.

[0008] According to a second aspect of the present invention, embodiments of the invention provide an apparatus for optimizing transmission in a small cell. The apparatus may comprise: a learning unit configured to learn space knowledge of interference channels between the small cell and a macro cell; and a determining unit configured to determine a beamforming matrix for an operation model of the small cell based on the space knowledge, wherein the operation model is a model of closed access or a model of open access..

[0009] The following benefits are expected with the invention. With the solution according to the present invention, a two-layered beamforming scheme is proposed, wherein small cell nodes can efficiently mitigate interference to/from macro cell nodes, as well as optimize its own network performance.

[0010] Other features and advantages of the embodiments of the present invention will also be apparent from the following description of specific embodiments when read in conjunction with the accompanying drawings, which illustrate, by way of example, the principles of embodiments of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Embodiments of the invention are presented in the sense of examples and their advantages are explained in greater detail below, with reference to the accompanying drawings, where

[0012] FIG. 1 illustrates a schematic diagram of a communication system 100 comprising a small cell and a macro cells;

[0013] FIG. 2 illustrates a flow chart of a method 200 for optimizing transmission in a small cell according to embodiments of the invention;

[0014] FIG. 3 illustrates a flow chart of a method 300 for optimizing transmission in a small cell according to embodiments of the invention; and

[0015] FIG. 4 illustrates a block diagram of an apparatus 400 for optimizing transmission in a small cell according to embodiments of the invention. DETAILED DESCRIPTION OF EMBODIMENTS

[0016] Embodiments of the invention will be described thoroughly hereinafter with reference to the accompanying drawings. It will be apparent to those skilled in the art that the invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments and specific details set forth herein. Like numbers refer to like elements throughout the specification.

[0017] The features, structures, or characteristics of the invention described throughout this specification may be combined in any suitable manner in one or more embodiments. For example, the usage of the phrases "certain embodiments," "some embodiments," or other similar language, throughout this specification refers to the fact that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present invention. Thus, appearances of the phrases "in certain embodiments," "in some embodiments," "in other embodiments," or other similar language, throughout this specification do not necessarily all refer to the same group of embodiments, and the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0018] In the context of the disclosure, the term "user", "user equipment" or "UE" may refer to a terminal, a Mobile Terminal (MT), a Subscriber Station (SS), a Portable Subscriber Station (PSS), Mobile Station (MS), or an Access Terminal (AT), and some or all of the functions of the UE, the terminal, the MT, the SS, the PSS, the MS, or the AT may be included.

[0019] The term "base station" or "BS" may refer to a node B (NodeB), an evolved NodeB (eNodeB), a Base Transceiver Station (BTS), an Access Point (AP), a Radio Access Station (RAS), or a Mobile Multihop Relay (MMR)-BS, and some or all of the functions of the BS, the NodeB, the eNodeB, the BTS, the AP, the RAS, or the MMR-BS may be included. In view of coverage, a BS may be a macro BS, a micro BS, a pico BS, a femto BS, and so on.

[0020] The term "small cell" may refer to, for example, a microcell, a picocell, a femto cell, etc. A small cell may be controlled by a low power node (LPN), such as a micro BS, a pico BS, a femto BS, and so on. The LPN may be also called as a small cell node or a small cell BS.

[0021] It is to be noted that, in the disclosure, "small cell user" also refers to "small cell UE", and "macro user" also refers to "macro UE". Both the "small cell user" and the "small cell BS" are "small cell nodes," and both the "macro user" and the "macro BS" are "macro cell nodes."

[0022] In general, embodiments of the present invention provide a method and an apparatus for optimizing transmission in a small cell. Now some exemplary embodiments of the present invention will be described with reference to the figures.

[0023] Reference is first made to FIG. 1, which illustrates a schematic diagram of a communication system 100 comprising a small cell and a macro cells. FIG. 1 shows downlink transmission of a two-tier networking, wherein a macro BS servers multiple outdoor users (macro users) underlaid with a small cell which consists of one small cell BS (femto BS) and several indoor users (femto users). All the macro cell nodes and femto cell nodes are equipped with multiple antennas, accessing the same frequency that is split into a plurality of subbands. Since femto cell nodes and macro cell nodes may operate the same subband, there may exist cross-tier interference.

[0024] In the model of closed access, the femto BS can only serve femto users, wherein macro users cannot access the femto BS even when they are very close to the femto BS. In this event, downlink transmission from the macro BS to the macro users may interfere with the femto users and downlink transmission from the femto BS to the femto users may interfere with the macro users. Cross-tier interference may comprise both the interference from the macro BS to the femto users and the interference from the femto BS to the macro users.

[0025] In the model of open access, the femto BS can serve both femto users and macro users. In this event, downlink transmission from the macro BS to the macro users may interfere with the femto users, but downlink transmission from the femto BS to the femto users will no longer interfere with the macro users. Cross-tier interference may comprise the interference from the macro BS to the femto users.

[0026] It is also noted that the small cell BS, e.g., femto BS, shown in FIG. 1 may be configured to implement functionalities as described with reference to the method according to the present invention. Therefore, the features discussed with respect to the method according to the present invention apply to the corresponding components in the small cell BS.

[0027] Embodiments of the present invention may be applied in various communication systems, including but not limited to a Long Term Evolution Advanced (LTE-A) system, a Universal Mobile Telecommunications System (UMTS), and so on. Given the rapid development in communications, there will of course also be future type wireless communication technologies and systems with which the present invention may be embodied. It should not be seen as limiting the scope of the invention to only the aforementioned system.

[0028] For better understanding, the following embodiments of the present disclosure are described based on the communication system 100 shown in Fig. 1. As can be appreciated by those skilled in the art, the present disclosure is not limited to the specific arrangement shown in FIG.1.

[0029] Embodiments of the present invention focus on transmission optimization and resource allocation in an OFDMA multiple-input-multiple-output (MIMO) small cell (e.g., femto cell) network. By advocating the concepts of cognitive radio (CR) communications, the small cell nodes are capable of learning partial space information of the interference channels. A two-layered beamforming scheme is proposed, whereby small cell nodes can efficiently mitigate interference to/from macro cell nodes, as well as optimize its own network performance. [0030] Reference is now made to FIG. 2, which illustrates a flow chart of a method 200 for optimizing transmission in a small cell according to embodiments of the invention. In accordance with embodiments of the present invention, the method 200 may be carried out by a small cell BS (e.g., the femto BS shown in FIG. 1) or some other suitable device, or may be carried out by an apparatus comprised in the small cell BS or some other suitable device.

[0031] At step S201, space knowledge of interference channels between the small cell and a macro cell is learned.

[0032] As discussed above, small cells can be configured in two basic ways to schedule their usage by macro users:

[0033] · Open access: all users covered by one small cell are allowed to access the small cell.

[0034] · Closed access: the small cell allows only its own prescribed users to establish connections.

[0035] In accordance with embodiments of the present invention, the interference channels between the small cell and a macro cell may be channels which suffer cross-tier interference. In some embodiments, if the operation model of the small cell is the model of open access, the interference channels may comprise channels from a macro BS to one or more small cell UEs. If the operation model is the model of closed access, the interference channels may comprise both channels from a small cell BS to one or more macro UEs (or users) and channels from a macro BS to one or more small cell UEs (or users).

[0036] The space knowledge may comprise information about a space spanned by the interference channels. According to embodiments of the present invention, the space knowledge of interference channels may be learned in several ways. For example, the interference channels between the small cell and a macro cell may be identified; and the space knowledge of the interference channels may be obtained. Details may be found in embodiments in connection with FIG. 3.

[0037] At step S202, a beamforming matrix for an operation model of the small cell is determined based on the space knowledge, wherein the operation model is a model of closed access or a model of open access.

[0038] Beamforming matrix may refer to a precoding matrix for using in the process of beamforming. In accordance with embodiments of the present invention, for different operation model of a small cell, the small cell may adopt different beamforming matrix to perform transmission. With respect to an operation model of a small cell, such as the open access and the close access, the beamforming matrix may be determined by deriving signals received from the small cell based on the space knowledge, and calculating the beamforming matrix by maximizing a user sum rate based on the derived signals. Details may be found in embodiments in connection with FIG. 3.

[0039] In accordance with embodiments of the present invention, the small cell may perform transmission by using the beamforming matrix determined at step S202, such that the throughput of the communication system is efficiently improved.

[0040] Reference is now made to FIG. 3, which illustrates a flow chart of a method 300 for optimizing transmission in a small cell according to embodiments of the invention. The method 300 may be considered as an embodiment of the method 200 described above with reference to Fig. 2. In the following description of method 300, the interference channels are first identified, and the beamforming matrix to be used by the small cell is determined based on space knowledge of the interference channels by maximizing a user sum rate. As such, the cross-tier interference is reduced and the throughput of the communication system is increased. However, it is noted that this is only for the purpose of illustrating the principles of the present invention, rather than limiting the scope thereof.

[0041] At step S301, the interference channels between the small cell and a macro cell are identified.

[0042] In some embodiments, considering the communication system all the macro cell nodes and femto cell nodes are equipped with multiple antennas, accessing the same frequency that is split into M subbands. Without loss of generality, we assume the number of femto UEs is K and the number of macro UEs is , i.e., each subband is used by a single macro UE. The antenna sizes of femto BS, femto UE, macro BS, and macro UE are denoted by N_F, {Λ¾ , N_M, and {¾} , respectively.

[0043] According to embodiments of the present invention, in the case that the small cell is configured with the model of open access, channels (denoted as ^{; ;} ) from a macro BS to one or more small cell UEs may be identified as the interference channels. In the case that the small cell is configured with the model of closed access, channels (denoted as ) from a small cell BS to one or more macro UEs and channels (denoted as -^;; ) from a macro BS to one or more small cell UEs (or users) may be identified as the interference channels.

[0044] At step S302, space knowledge of the interference channels is obtained, wherein the space knowledge comprises information about a space spanned by the interference channels.

[0045] At step S303, signals received from the small cell is derived based on the space knowledge.

[0046] According to embodiments of the present invention, signals received from the small cell may be derived based on the space knowledge in several ways. In some embodiments, whether the operation model is the model of closed access or the model of open access may be determined; if the operation model is the model of closed access, signals received at one or more small cell user equipments (UEs) from the small cell may be derived; and if the operation model is the model of open access, signals received at one or more small cell UEs and signals received at one or more macro UEs from the small cell may be derived.

[0047] Details for deriving the received signals in the two models will be described below. In the following embodiments, the small cell is exemplified as a femto cell. It is to be noted that, the small cell may be a pico cell, a micro cell and/or the like, and the femto cell is illustrated for example, rather than limitation.

[0048] Closed Access

[0049] In the closed-access operation model, all macro cell nodes do not cooperative with femto cell nodes, and thus the femto cell nodes have to learn by themselves the cross-tier interference channel knowledge that is of vital importance to interference mitigation. Assuming that the femto cell nodes are capable of implementing cognitive beamforming (CB), by which femto cell nodes transmit/receive signals through the subspaces orthogonal to the cross-tier interference channel matrices.

[0050] Denote the channel matrices from femto BS to the zth macro user and to the Mi femto user over the zth subband by ¾ -~ ' and ^s " , respectively. Assuming that channel reciprocity exists between the cross-tier links, the channel matrix that femto BS learns from the ith macro user signal is then ^{J ;} . Moreover, channel from macro BS to the Mi femto user over the ith subchannel is captured by matrix < ^~

[0051] By applying CB, for the ith subband femto BS and the kth femto user can obtain subspace matrices ¾ _an(j ^{; ,} _k <^·. ^: v ^' , which satisfy

C^x&i ^{■■■■■■■■} 0^.^ *··^>*.^,¾ i.b ^~ However, due to limited channel learning time, the CB matrices can only be inexactly maintained as ^J;*, with first-order estimation perturbations *-^{i U}-« ^:::: ^ - u, _t ^«-^{* ·}'*·* ^{~ '}· * ^; , which implies that there exists residue cross-tier interference when using CB.

[0052] For femto cell transmission, femto BS first precodes its transmit data and then further filters the precoded data by CB matrices. Let the ^{f! < i; A}" vector t \ ⁽0^{, ;} b_{e me} source data transmitted to the kth femto user over the ith subband, and ^; ' be the corresponding precoding matrix. Then, through OFDMA the signal received by the Mi femto user over the ith subband is

V; /.· - IX.a U'V/ s,, + V_;",s.- - 7H_ik , _{( ] )} where ^b' ^ ^^{; (T}^^¾' denotes the signal from macro BS to the ith macro user and £·Λ ⁽ 0^» i.k^-Nk ) is the noise vector. To mitigate the received cross-tier interference, the Mi femto user equalizes ^ by CB matrix ^J , yielding

y_{! k} ^ V jlIi.kVi kS k + Δυ¾;ν¾8, + I¾«i._fc .

"

Vi,k (2)

[0053] Open Access

[0054] In the open access operation model, macro users covered by the femto cell can freely link to the femto BS for data transmission, given the collaboration mechanism built in the underlying structure of macro users. However, since macro BS is usually far from femto cell the collaboration between macro BS and femto cell nodes is still unavailable.

[0055] In such case the femto BS has to transmit data for both femto user and macro user within the same subband. Let " * vectors ^{"h J;} ·· denote the transmit data for the kth femto user (j = 1) or the zth macro user (j = 2) over the zth subband. The signal received by the kth femto user can be written by

V = ΤΤ.' ;- ί F' ^' s'i 4- P^.'s¹-^" ) 4- — S_>- 4- ??; >■. where ^{! A'} represent the precoding matrix for femto user (j = 1) or macro user (j = 2). To mitigate cross-tier interference from macro BS, the th femto user equalizes " by ^' ^^' and generates

where

Similarly, the signal received by the zth macro user assisted by the femto BS is given by " ^< '" "^Λ -^!" ^r" ^' (5) where ^»* and " ^'< -^{k ! J}' ^i; , with being channel matrix from

7 ^c C A ^!'0 ( ³ I_" 1

macro BS to the zth macro user and ^{J< ¾ ^} " ^{' fc} ' being the noise vector.

Since macro users fully cooperate with femto BS under the open access, we assume macro users could correctly feed channel knowledge ^> and ^{J i} to femto BS.

[0056] At step S304, the beamforming matrix is calculated by maximizing a user sum rate based on the derived signals.

[0057] Details for deriving the received signals in the two operation models, closed access and open access, will be described below.

[0058] Closed Access

[0059] Through resource allocation and transmission optimization, the femto BS aims to maximize femto cell performance as well as efficiently restrict the residue cross-tier interference to macro users, which is caused by the imperfect channel learning.

Let the binary variable ^{¾ k} represent subchannel assignment, i.e., ^t -^{k ""'} ^ means that the ith subband is assigned to the kth femto user, and otherwise " ' ^¾ To maximize multi-user sum rate, the femto cell transmission optimization problem can be formulated as

where ^ ^'^ ^{1 s>} ·^¾■ denotes the achievable rate of the Mi femto user by using the ith subchannel, ^Uk is the user weight, ^{1 "}i is the transmit power limitation on the ith subband, represents the residue cross-tier interference, and denotes the tolerable interference level. Constraint (7) admits orthogonal subband assignment based on OFDMA, and by applying the perturbation analysis technique introduced in ^{' k} can be calculated as

E {tr { G . U . F, j. - G .:. U ^< , }

^~~- (M_t -I-^■ tiv ( (<r G G_t )^r} ) ir(F ¾ h

(9)

...

where '^{7 ,} and ^ί;' denote the macro cell transmission power and noise level when femto BS listens to the ith macro user during the channel learning. It has been demonstrated that ¹ ' ** can be replaced by its sampled version when · · Ms not too small, implying °·^; can be taken as a constant.

[0060] Consider the following subproblem obtained from (6):

i.i. tr( Fj._A,F¾.) < imn{/ ?; /

(11)

Through primal decomposition, it is easy to verify that the optimal subband assignment for (6) can be determined as

:'.— are max rr l °^{< olhet} - (12) The above results suggest that for the transmission optimization in closed-access femto cell, we only have to solve subproblem (10), where ' ^K" ^{S; A} ' can be computed as

[0061] Similar as calculating '' ··^■'· . we ha

(14) where ⁱ * denotes the received noise level when the Mi femto user listens to macro BS over the zth subband during the channel learning, and once again ^{f i i A} can be viewed as a constant. Substituting (14) into (13), it is easy to observe that subproblem (10) equals to eigenmode transmission, which is indeed a convex scalar power allocation problem and can be efficiently treated. In general, the computation complexity involved in solving (10) by water-filling is around ^{11 <}'''^■ ' , and the total complexity of resource allocation would be less than 0^{Al ^i\. a ^j _wh_ere

[0062] Open Access

[0063] In open-access femto cell undertakes the traffic burden of femto users. As a return, femto BS is free from cross-interference management. In this scenario, femto BS should optimize data rates of both femto users and macro users, as the femto cell performance criteria. Then the transmission optimization problem may be formulated as

! € {() , i }, Vi /■;

(16)

From (4) and (5), it is can be seen that directly calculating ^' 'H'- ^{' '} _1S difficult and would turn (15) into nonconvex problem. To overcome this difficulty, the classic alternating optimization process of rate calculation, which encourages to solve the beamforming problem in an iterative manner, may be adopted. In this way, may be equivalently rewritten as

where *>* represent test posterior probabilistic parameters that should be optimized with the precoding matrices in an alternating way for rate calculation. Given the precoding matrices, their optimal values are

(19) where and

[0064] Define

which can be expanded as

are defined b

(22)

[0065] Fixing the probabilistic parameters, problem (15) can be decomposed into subproblems shown as follows: mm

(23) (24)

[0066] Notice that ^{" i} can be further cast as

(25) where

Then subproblem (23) can be equivalent!}' rewritten as

(26) where ^{" <} ' ^{' '} . Then, subproblem (26) can be converted into a unconstraint convex problem and has closed- form solution:

[0067] Based on the above discussion we provide the iterative beamforming scheme for subproblem (23) as shown in the following Algorithm 1.

Algorithm 1 N_F xd _k i.) Calculate the probabilistic parameters ¾ ,∑ _A: based on (1 ), and let ii) Update via (27); update F ^ according to (28) and (29);

iii) Compute the objective value

Till the objective value converges.

[0068] Note that since each iteration of the proposed algorithm keeps the objective value of subproblem (23) increasing (or (26) decreasing) and the weighted sum rate (WS ) must have an upper bound, it is easy to verify that the convergence of the iterative beamforming algorithm is ensured.

[0069] Define ^{" ! ;} ·'- . As addressed in the closed-access scenario, the optimal subband assignment for the open-access model should be

The computation complexity involved in solving subproblem (23) mainly comes from the Hermitian matrix inversion operation in (29), which is less than ''^■·'·' ^{' 5} · ^' _? and thus the total complexity of the resource allocation would be less than

0(2MK(dN_F ) _where d > <%l v k

[0070] In view of the foregoing, embodiments of the present invention propose a two-layered beamforming scheme, whereby small cell nodes can efficiently mitigate interference to/from macro cell nodes, as well as optimize its own network performance. For closed access, the beamforming design can be implemented by solving convex optimization problems. For open access, what is solved is a nonconvex beamforming problem, for which a convergence-ensured iterative optimization algorithm is proposed. The subproblems involved in each iteration of proposed algorithm all have closed-form solutions.

[0071] Reference is now made to FIG. 4, which illustrates a block diagram of an apparatus 400 for optimizing transmission in a small cell according to embodiments of the invention. The apparatus 400 may be implemented at a small cell BS (for example, the femto BS shown in FIG. 1) or some other suitable devices.

[0072] According to embodiments of the present invention, the apparatus 400 comprises: a learning unit 410 configured to learn space knowledge of interference channels between the small cell and a macro cell; and a determining unit 420 configured to determine a beamforming matrix for an operation model of the small cell based on the space knowledge, wherein the operation model is a model of closed access or a model of open access.

[0073] According to embodiments of the present invention, the learning unit may comprise: an identifying unit configured to identify the interference channels between the small cell and a macro cell; and an obtaining unit configured to obtain space knowledge of the interference channels, wherein the space knowledge comprises information about a space spanned by the interference channels.

[0074] According to embodiments of the present invention, if the operation model is the model of open access, the interference channels comprise channels from a macro BS to one or more small cell UEs, and if the operation model is the model of closed access, the interference channels comprise channels from a small cell BS to one or more macro UEs and channels from a macro BS to one or more small cell UEs.

[0075] According to embodiments of the present invention, the determining unit may comprise: a deriving unit configured to derive signals received from the small cell based on the space knowledge; and a calculating unit configured to calculate the beamforming matrix by maximizing a user sum rate based on the derived signals.

[0076] According to embodiments of the present invention, the deriving unit may comprise: a model determining unit configured to determine whether the operation model is the model of closed access or the model of open access; a first signal deriving unit configured to, if the operation model is the model of closed access, derive signals received at one or more small cell UEs from the small cell; and a second signal deriving unit configured to, if the operation model is the model of open access, derive signals received at one or more small cell UEs and signals received at one or more macro UEs from the small cell.

[0077] It is noted that the apparatus 400 may be configured to implement functionalities as described with reference to FIGs. 2 and 3. Therefore, the features discussed with respect to any of methods 200 and 300 may apply to the corresponding components of the apparatus 400. It is further noted that the components of the apparatus 400 may be embodied in hardware, software, firmware, and/or any combination thereof. For example, the components of the apparatus 400 may be respectively implemented by a circuit, a processor or any other appropriate selection device. Those skilled in the art will appreciate that the aforesaid examples are only for illustration not limitation.

[0078] In some embodiment of the present disclosure, the apparatus 400 comprises at least one processor. The at least one processor suitable for use with embodiments of the present disclosure may include, by way of example, both general and special purpose processors already known or developed in the future. The apparatus 400 further comprises at least one memory. The at least one memory may include, for example, semiconductor memory devices, e.g., RAM, ROM, EPROM, EEPROM, and flash memory devices. The at least one memory may be used to store program of computer executable instructions. The program can be written in any high-level and/or low-level compilable or interpretable programming languages. In accordance with embodiments, the computer executable instructions may be configured, with the at least one processor, to cause the apparatus 400 to at least perform according to any of methods 200 and 300 as discussed above.

[0079] Based on the above description, the skilled in the art would appreciate that the present disclosure may be embodied in an apparatus, a method, or a computer program product. In general, the various exemplary embodiments may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. For example, some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, although the disclosure is not limited thereto. While various aspects of the exemplary embodiments of this disclosure may be illustrated and described as block diagrams, flowcharts, or using some other pictorial representation, it is well understood that these blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

[0080] The various blocks shown in FIGs. 2-3 may be viewed as method steps, and/or as operations that result from operation of computer program code, and/or as a plurality of coupled logic circuit elements constructed to carry out the associated function(s). At least some aspects of the exemplary embodiments of the disclosures may be practiced in various components such as integrated circuit chips and modules, and that the exemplary embodiments of this disclosure may be realized in an apparatus that is embodied as an integrated circuit, FPGA or ASIC that is configurable to operate in accordance with the exemplary embodiments of the present disclosure.

[0081] While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any disclosure or of what may be claimed, but rather as descriptions of features that may be specific to particular embodiments of particular disclosures. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.

[0082] Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0083] Various modifications, adaptations to the foregoing exemplary embodiments of this disclosure may become apparent to those skilled in the relevant arts in view of the foregoing description, when read in conjunction with the accompanying drawings. Any and all modifications will still fall within the scope of the non-limiting and exemplary embodiments of this disclosure. Furthermore, other embodiments of the disclosures set forth herein will come to mind to one skilled in the art to which these embodiments of the disclosure pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings.

[0084] Therefore, it is to be understood that the embodiments of the disclosure are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are used herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

Claims

WHAT IS CLAIMED IS:

1. A method for optimizing transmission in a small cell, comprising:

learning space knowledge of interference channels between the small cell and a macro cell; and

determining a beamforming matrix for an operation model of the small cell based on the space knowledge, wherein the operation model is a model of closed access or a model of open access.

2. The method of Claim 1, wherein learning space knowledge of interference channels between the small cell and a macro cell comprises:

identifying the interference channels between the small cell and a macro cell; and obtaining space knowledge of the interference channels, wherein the space knowledge comprises information about a space spanned by the interference channels.

3. The method of Claim 2, wherein if the operation model is the model of open access, the interference channels comprise channels from a macro BS to one or more small cell UEs, and

wherein if the operation model is the model of closed access, the interference channels comprise channels from a small cell base station (BS) to one or more macro user equipments (UEs) and channels from a macro BS to one or more small cell UEs.

4. The method of Claim 1, wherein determining a beamforming matrix for an operation model of the small cell based on the space knowledge comprises:

deriving signals received from the small cell based on the space knowledge; and calculating the beamforming matrix by maximizing a user sum rate based on the derived signals.

5. The method of Claim 4, wherein deriving signals received from the small cell based on the space knowledge comprises:

determining whether the operation model is the model of closed access or the model of open access; if the operation model is the model of closed access, deriving signals received at one or more small cell user equipments (UEs) from the small cell; and

if the operation model is the model of open access, deriving signals received at one or more small cell UEs and signals received at one or more macro UEs from the small cell.

6. An apparatus for optimizing transmission in a small cell, comprising:

a learning unit configured to learn space knowledge of interference channels between the small cell and a macro cell; and

a determining unit configured to determine a beamforming matrix for an operation model of the small cell based on the space knowledge, wherein the operation model is a model of closed access or a model of open access.

7. The apparatus of Claim 6, wherein the learning unit comprises:

an identifying unit configured to identify the interference channels between the small cell and a macro cell; and

an obtaining unit configured to obtain space knowledge of the interference channels, wherein the space knowledge comprises information about a space spanned by the interference channels.

8. The apparatus of Claim 7, wherein if the operation model is the model of open access, the interference channels comprise channels from a macro BS to one or more small cell UEs, and

9. The apparatus of Claim 6, wherein the determining unit comprises:

a deriving unit configured to derive signals received from the small cell based on the space knowledge; and

a calculating unit configured to calculate the beamforming matrix by maximizing a user sum rate based on the derived signals.

10. The apparatus of Claim 9, wherein the deriving unit comprises: a model determining unit configured to determine whether the operation model is the model of closed access or the model of open access;

a first signal deriving unit configured to, if the operation model is the model of closed access, derive signals received at one or more small cell user equipments (UEs) from the small cell; and

a second signal deriving unit configured to, if the operation model is the model of open access, derive signals received at one or more small cell UEs and signals received at one or more macro UEs from the small cell.