CN105072653A - Edge user cell selection algorithm in heterogeneous network - Google Patents

Edge user cell selection algorithm in heterogeneous network Download PDF

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CN105072653A
CN105072653A CN201510548347.4A CN201510548347A CN105072653A CN 105072653 A CN105072653 A CN 105072653A CN 201510548347 A CN201510548347 A CN 201510548347A CN 105072653 A CN105072653 A CN 105072653A
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bias
microcell
users
edge customer
macrocell
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CN105072653B (en
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韩东升
牛伟娇
赵振东
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North China Electric Power University
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North China Electric Power University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0055—Transmission or use of information for re-establishing the radio link
    • H04W36/0061—Transmission or use of information for re-establishing the radio link of neighbour cell information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0055—Transmission or use of information for re-establishing the radio link
    • H04W36/0072—Transmission or use of information for re-establishing the radio link of resource information of target access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/08—Reselecting an access point

Abstract

The invention provides an edge user cell selection algorithm in a heterogeneous network. Each of edge users selects a microcell in a macrocell where the edge user is located by referring to a fixed offset value; the fixed offset value is dynamically adjusted according to changes of the number of the users in all microcells before and after selection; each edge user re-selects a microcell in the macrocell where the edge user is located by taking the adjusted offset value as a reference; if the number of the users in the microcells before the offset value is adjusted is different from the number after the offset value is adjusted, the adjusted offset value is further adjusted on the basis of the number of the users of the microcells in the macrocell, and loads are balanced till the number of the users keeps unchanged. The edge users select cells by adopting the algorithm. Therefore, loads of a macrocell are reduced, loads among microcells are effectively balanced, and the throughput and the resource utilization rate of a light load microcell network are improved.

Description

Edge customer cell selection algorithm in a kind of heterogeneous network
Technical field
The present invention relates to the load of heterogeneous network, be specifically related to edge customer cell selection algorithm in a kind of heterogeneous network.
Background technology
In order to meet ever-increasing data traffic requirement, 3GPPLTE-Advance introduces heterogeneous network, to realize higher spectrum efficiency.Heterogeneous network, by introducing low power base station, can obtain larger cell splitting gain, and then improve the capacity of whole network.The deployment of multiple small-power base station there will be " cell edge region " in a large number, makes the performance of edge customer receive impact.If according to the strongest Received signal strength access base station, likely occur that the situation of overload appears in the base station of user-selected access, cause user to can not get good service.
For the access problem of heterogeneous network, how reasonably the load of macrocell being unloaded to that Microcell gets on is an emphasis of current heterogeneous network technologies research.Research as current comprises:
Document 1: " AamodKhandekar, NagaBhushan, JiTingfang, VieriVanghi, LTE-Advanced:HeterogeneousNetworks [C], 2010EuropeanWirelessConference:978-98 " and document 2: " CheShengChiu, Chia-ChiHuang, AnInterferenceCoordinationSchemeforPicocellRangeExpansio ninHeterogeneousNetworks [J], VehicularTechnologyConference (VTCSpring), 2012IEEE75th:1-6 " propose CRE (cellrangeexpansion, cell coverage expansion) Access Algorithm, but use fixing bias in the algorithm, the demand of actual user cannot be met,
Document 3: " ZhangYongJing; ZhangKui; ChiCheng.Anadaptivethresholdloadbalancingschemefortheend-to-endreconfigurablesystem.WirelessPersonalCommunication s; 2007; 46 (1): 47-65 " gives a kind of dynamic threshold algorithm of load balancing, and user selects network based on multiobject user's request.The method plays certain effect in equalizing network, but does not improve the throughput of whole network;
Document 4: " FalowolO; ZeadallyS; ChanH.Dynamicpricingforload-balancinginuser-centricjoint calladmissioncontrolofnext-generationwirelessnetworks; InternationalJournalofCommunicationSystems; 2010; 23 (3): 335368 " gives the network selecting method based on fuzzy logic, it is only for unique user, instead of the throughput of whole network is optimized;
Document 5: " XuePeng, GongPeng, ParkJH, eta1.Radioresourcemanagementwithproportionalrateconstrai ntintheheterogeneousnetworks.IEEETransactionsonWirelessC ommunications, 2012, 11 (3): 1066-1075 " remove from the angle of heterogeneous network the whole network the normalizing rate maximizing user, method in literary composition obtains good performance in maximization normalizing rate, and document 6: " Shirakabe, M., A.MorimotoandN.Mil (i. " Performanceevaluationofinter-cellinterferencecoordinatio nandcellrangeexpansioninheterogeneousnetworksforLTE-Adva nceddownlink ") .20118thInternationalSymposiumonWirelessCommunicationSys tems (ISWCS) .2011 " in be provided with different biased for each user in network, carry out Optimal performance, but above-mentioned two kinds of prioritization schemes are not considered when there is multiple adjacent Microcell, the setting scheme of bias,
Document 7: " Peng; T. " AnadaptivebiasconfigurationstrategyforRangeExtensioninLT E-AdvancedHeterogeneousNetworks " .1ETInternationalConferenceonCommunicationTechnologyandA pplication (ICCTA2011) .2011 " proposes the user for different distributions situation, different being biased to adapt to actual conditions is set dynamically, but this algorithm but introduces too many parameter.
Summary of the invention
In view of this, the invention provides edge customer cell selection algorithm in a kind of heterogeneous network, be intended to the resource utilization being improved Microcell by the load balanced between Microcell.
The technical solution used in the present invention is specially:
Edge customer cell selection algorithm in a kind of heterogeneous network, each edge customer is with reference to fixing bias, select the Microcell in its place macrocell, according to the number of users change selecting each Microcell, front and back, dynamic conditioning is carried out to fixing bias, using the bias after adjustment as reference, each edge customer reselects the Microcell in its place macrocell, if with reference to the number of users difference of the Microcell that the bias before and after adjustment obtains, then the bias after adjustment is adjusted according to the number of users of each Microcell in macrocell further, until number of users is constant, load reaches balanced.
Edge customer cell selection algorithm in above-mentioned heterogeneous network, specifically comprises the following steps:
S100: initialization bias, base station set and user's collection, with biased initial value for reference, edge customer selects the Microcell in its place macrocell, and the number of users in its region is added up in each Microcell;
S200: adjust each Microcell bias according to the number of users between Microcell, with the bias after adjusting for reference, edge customer carries out reselecting of Microcell and accesses, and the number of users in its region is added up in each Microcell;
S300: before and after bias adjustment, if the number of users in the region of Microcell changes, then return S200; Until number of users is constant, load reaches balanced.
In above-mentioned heterogeneous network in edge customer cell selection algorithm, in the step s 100:
If macrocell; Then biased initial value c=0,
If Microcell is c>0 then;
And the span of biased initial value c is [0,20dB].
In above-mentioned heterogeneous network in edge customer cell selection algorithm, in the step s 100, edge customer selects the Microcell in its place macrocell according to fixing bias; And
In the step s 100, edge customer carries out reselecting of Microcell according to fixing bias and accesses.
In above-mentioned heterogeneous network in edge customer cell selection algorithm, edge customer selects specifically being referenced as of community:
i*＝argmax{RSRP ik+bias i}；
Wherein:
I* represents the Serving cell that user k will select;
RSRP ikrepresent that user k receives the reference signal power from cell i;
Bias irepresent the side-play amount of cell i; If bias i=0, expression i is macrocell; If bias i>0, then represent that i is Microcell.
In above-mentioned heterogeneous network in edge customer cell selection algorithm, in step s 200, adjust each Microcell bias according to the number of users between Microcell to be specially:
bias i k+1＝bias i k+a(N m-N i)
Wherein:
N mrepresent the user in macrocell;
N ishow the number of users in the i of Microcell;
Bias i kan expression kth edge customer selects bias during cell i;
Bias i k+1represent bias during kth+1 edge customer selection cell i;
A represents step-length regulation coefficient, and a > 0.
In above-mentioned heterogeneous network in edge customer cell selection algorithm, the value of described step-length regulation coefficient a should meet:
Make bias i k+1=bias i k+ a (N m-N i) ∈ [0,20dB].
The beneficial effect that the present invention produces is:
In heterogeneous network of the present invention, edge customer cell selection algorithm carries out precoding suppression interference by transmitting terminal, improve the Signal to Interference plus Noise Ratio (SINR) that user receives, according to community active user's number, with reference to the bias that Received signal strength adds, dynamically adjust the Microcell accessed to Microcell edge customer, avoid the congested cells that edge customer access customer number is too much.Compared with traditional algorithm, while effectively reducing macrocell load, the load balanced between each Microcell distributes, and improves throughput and the resource utilization of underloading micro cells networks.And the bias of dynamic conditioning Microcell is carried out by introducing single parameter, algorithm is simple; Be particularly suitable for edge customer number is less or two adjacent Microcell number of users differences are larger scene (as can be seen from the simulation result of embodiment, when number of users difference between two Microcells is greater than 5, technical scheme of the present invention can well balance two micro-cell load).
Accompanying drawing explanation
When considered in conjunction with the accompanying drawings, more completely the present invention can be understood better.Accompanying drawing described herein is used to provide a further understanding of the present invention, and embodiment and explanation thereof, for explaining the present invention, do not form inappropriate limitation of the present invention.
Fig. 1 is the structural representation (comprising a macrocell and two Microcells) of a kind of heterogeneous wireless network topology of the present invention;
Fig. 2 is the schematic flow sheet of edge customer cell selection algorithm in a kind of heterogeneous network of the present invention;
Fig. 3 is the throughput schematic diagram that the edge customer in heterogeneous wireless network adopts various cell selection algorithm;
Fig. 4 is the user distribution comparison diagram that the edge customer in heterogeneous wireless network adopts various cell selection algorithm;
Fig. 5 is the edge customer employing fixed bias algorithm in heterogeneous wireless network and the throughput comparison diagram after dynamic bias algorithm;
Fig. 6-1 is the community user distribution map after the edge customer employing fixed bias algorithm in heterogeneous wireless network;
Fig. 6=2 are the community user distribution map after the edge customer employing dynamic bias algorithm in heterogeneous wireless network.
Embodiment
Below in conjunction with drawings and Examples, technical scheme of the present invention is described in further detail.
Consider a heterogeneous network downlink transmission system having single macro base station and multiple micro-base station to coexist, micro-base station is covered completely by macro base station, several users of random distribution in each base station, and user can only access a base station at every turn.As shown in Figure 1, assuming that there is 1 macrocell in system, have 2 Microcells in each macrocell, macrocell covers Microcell to system model completely.Each cell base station has M transmit antennas, user is single antenna reception, and user can estimate the channel condition information of each base station to mobile client exactly, all channel matrixes are all non-singular matrixs, and obey complex-valued Gaussian independent distribution, if the active user's number in macrocell and two Microcells is respectively K m,i, K p1i, K p2i.
Suppose that receiving terminal channel condition information is known, namely base station is to the transmission channel matrix H={ h of all users i, 1h i, Kiknown, wherein h i, 1represent the transmission channel matrix between cell i to this community user 1, K irepresent the number of users in i community, i ∈ (1,2,3).For without loss of generality, might as well establish during i=1 and represent macrocell, i=2,3 interval scale two Microcells.
The signal that so in cell i, user k receives can be expressed as:
y i , k = h i , k w i , k s i , k + h i , k Σ b = 1 , b 1 k K i w i , b s i , b + Σ j I ^ L , j 1 i h j , k w j , k s j , k + n i , k - - - ( 1 )
Section 1 in formula (1) represents that user k expects the signal received, and Section 2 is then multi-user interference, and Section 3 is presence of intercell interference.
Base station transmit signals vector is S ∈ C m × 1, vector signal S=[S 1s m] trepresent the M bar data streams in parallel that transmits.H i,k∈ C 1 × M, be base station i (i=1,2 ..., B) and to the channel vector of user k, L represents the set of all communities, in the present embodiment, L={1,2,3}.K irepresent the number of users in the i of affiliated subdistrict, S i,kthe vector signal that base station i is transmitted into user k, h j,kthe channel vector of base station j to user k, w i,krepresent that base station i is sent to the vector signal pre-coding matrix of user k, last n in above formula i,kbe that in transmitting procedure, average is 0, variance is the white Gaussian noise of 1.
Adopt ZF (ZF) method for precoding to suppress common-channel interference, namely suppress other subscriber signals in cell i to the interference of user k, to meet: H=H h(HH h) -1, make h i,kw i,b=0 ( ∀ b ∈ K i , b≠K)。
In above formula, H represents channel transfer matrices, h ikfor the channel transfer matrices of user k in base station in cell i to cell i, w ibfor the pre-coding matrix of user b in base station in cell i to cell i, make h i,kw i,b=0, user k in cell i can be eliminated and receive the interference of other users from this community.
When design precoding, demand fulfillment transmitting power limits, that is:
E [|| Hs|| 2]=tr (HH hp s 2)=MP s 2, wherein P srepresent the source signal power before precoding, H represents channel transfer matrices, and s represents the vector signal that base station sends.
After adopting precoding, signal and the Signal to Interference plus Noise Ratio of the user k reception of cell i can be expressed as:
y i , k = h i , k w i , k s i , k + Σ j ∈ L , j ≠ i h j , k w j , k s j , k + n i , k - - - ( 2 )
S I N R i , k = P i | | h i , k w i , k | | 2 P j h i , k Σ b = 1 , b ≠ k K i | | w j , b s j , b | | 2 + σ 2 - - - ( 3 )
In formula (3): P ibe the transmitting power of base station i, the Section 1 in denominator is the presence of intercell interference that user receives, σ 2for acoustical power.
Putting before this, the speed that user can obtain is:
R i,k＝Blog(1+SINR i,k)(4)
The throughput of corresponding cell i is:
Traditional CRE algorithm is that Microcell signal adds a fixing bias, is two Microcells and arranges identical fixing bias to unload the user of macrocell; Particularly:
i*＝argmax{RSRP ik+bias i}(6)
In above formula:
I* represents the Serving cell that user k will select;
RSRP ikrepresent that user k receives the reference signal power from cell i;
Bias irepresent the side-play amount of cell i: if bias i=0, expression i is macrocell; If bias i>0, then represent that i is Microcell.
As shown in Figure 1 one heterogeneous network coexisted by single macro base station and two micro-base stations, can find out, in two Microcells (Picocell), Microcell 1 is higher relative to the load of Microcell 2, if take the mode by arranging identical fixing bias (formula (6)) for two Microcells to unload macrocell user, may overload situations be there is in Microcell 1, even if the channel condition of its user is fine or Signal to Interference plus Noise Ratio (SINR) is very high, the available resources distributing to user are also very limited, and the resource of Microcell 2 is not fully utilized.
For this phenomenon, upgrade dynamically according to the user distribution situation of Microcell is the bias of Microcell setting at every turn, the Microcell bias making load high is lower than the Microcell of low load, specific to Fig. 1, the user UE1 finally making way for edge, two Microcells is in outside the spreading range of Microcell 1, and be in the covering spreading range of Microcell 2, namely user UE1 can access in Microcell 2, Radio Resource idle in this Microcell is utilized to carry out transfer of data, while the throughput of this Microcell is improved, decrease the load of neighbouring overload Microcell.
The bias arranging cell i according to the existing user distribution situation in community is:
bias i k+1＝bias i k+a(N m-N i)(7)
In above formula:
N mrepresent the number of users in macrocell, N irepresent the number of users in the i of Microcell, coefficient a be one on the occasion of, can be used for adjusting the step-length of bias value, avoid bias to arrange excessive; Number of users difference according to Liang Ge community sets, and its value will meet to be made to adjust value between scope 0 to 20 at every turn.As: in the setting parameter of subsequent simulation test, in order to make bias ∈ [0,20dB], get a=3.
Analysis mode (7) can draw, when i macrocell, bias is always 0, and when i Microcell, bias is along with number of users N iincrease and reduce.
Suppose that channel keeps constant in correlation time in T, user is equal random distribution in community, and a user can only access a base station within a period of time.Algorithm flow as shown in Figure 2, is specially:
S10: initialization bias bias 0=c, base station set (L 1, L 2, L 3) and user's collection (K 1, K 2, K 3), if macrocell initial value c=0, if Microcell then c>0; Preferred as one, the span of bias c is [0,20dB]; Wherein:
Namely initialization base station set arranges position and the covering radius of macro base station and micro-base station in simulations;
Carry out arranging certain number of users to each macrocell and Microcell before community is selected at edge customer in the emulation of initialising subscriber set representations;
S20: according to biased initial value, first edge customer carries out Microcell selection according to formula (6), and the number of users N in its region is added up in each Microcell p;
S30: then adjust its bias according to formula (7), edge customer, according to the bias after adjustment, carries out reselecting of Microcell according to formula (6) and accesses;
S40: the number of users before and after each biased adjustment of Microcell statistics in CRE region (after representing that Microcell setting is biased, covering the scope of expansion), if number of users changes, then illustrates that load does not reach balanced, return S30; If number of users is constant, then carry out S50;
S50: terminate.
Table 1 simulation parameter set point
According to simulation parameter set point as shown in table 1, community of the present invention selection scheme is emulated, simulation result be analyzed as follows:
Suppose that 1 macro base station is distributed in (0,0) coordinate position, 30 users random distribution in coverage, the marginal position of two micro-base station distribution in macro base station coverage, coordinate is respectively (45,350) and (-45,350).Micro-base station is difference random distribution 4 and 10 users in its coverage.The user of community to be selected refers in particular to the edge customer need applying cell selection algorithm of the present invention herein, is namely randomly dispersed in the user of the fringe region between two picocell coverage area.
The Microcell 2 that Fig. 3 gives underloading adopts the throughput curve of algorithms of different.Can find out:
Adopt maximum with reference to Received signal strength (RSRP) Access Algorithm, edge customer has mostly accessed the larger macrocell of transmitting power, increasing therefore along with edge customer number, and the throughput of Microcell 2 is substantially constant;
Underloading Microcell (Microcell 2) due to intra-cell users less, when arranging bias according to formula (7), biased larger, therefore the mode that fixed bias value is set according to formula (6) is compared to, more user will access this community, thus effectively improves the throughput of this community;
And can find out, when edge customer number is less than 20, it is very fast that curve raises trend (throughput increase), when number of users continues to increase, curve tendency then tends towards stability, this is because: when Microcell number of users is too much, its bias reduces gradually, cause the number of users of new access by minimizing, therefore when edge customer number is increased to a certain degree, the throughput recruitment arranging fixed bias and dynamic bias community is basically identical, therefore can draw, dynamic bias value-based algorithm especially in other words be only applicable to edge customer number less time scene.In fact, along with the Microcell that more edge customer number access load is less, being biased between two Microcells also will be more and more less, therefore, arrange dynamic bias and can also prevent two Microcells overloads.Particularly:
When edge customer number is 10, after application choice algorithm, the number of users of each community is as shown in Figure 4, when adopting RSRP, fixed bias algorithm and dynamic bias algorithm, the number of users of three communities is respectively (38,11,6), (0,17,7) and (30,12,12).When edge customer carries out community selection, the load that bias effectively can balance each community is set dynamically, while the user of macrocell is unloaded to Microcell, achieves the load balance of two Microcells;
Adopt when edge customer number is 20 fixed bias value and dynamic bias value two kinds of latter two Microcell throughputs of algorithm more as shown in Figure 5, when dynamic bias is set, because Microcell 2 number of users is less, arrange biased according to formula (7), the biased of Microcell 2 is greater than Microcell 1, when user carries out cell reselection, makes edge customer more access Microcell 2, compare to and adopt fixed bias value-based algorithm, the throughput of Microcell 2 improves 9.63%;
As shown in Fig. 6-1 and 6-2, (in figure, community, right side represents the Microcell 1 that initial user number is many respectively to adopt the distribution of the community user after fixed bias value and dynamic bias value two kinds of algorithms, community, left side is the less Microcell 2 of initial user number), by relatively finding out, by arranging dynamic bias value, edge customer has accessed the Microcell 2 of underloading more.
How the rational load by macrocell is unloaded to that Microcell gets on is an emphasis of current heterogeneous network technologies research.Edge customer cell selection algorithm of the present invention can make edge cell user select the Serving cell of Microcell as oneself of underloading more, while achieving the lifting of this cell capacity, also macrocell is served to the effect of load balancing, can be seen by simulation analysis, compared with fixed bias CRE algorithm, the throughput of underloading Microcell improves 1.77 ~ 9.63%, achieves the load balancing between macrocell and Microcell and different Microcell.
Below explain embodiments of the invention by reference to the accompanying drawings, accompanying drawing is herein used to provide a further understanding of the present invention.Obviously; the foregoing is only the present invention's preferably embodiment; but protection scope of the present invention is not limited thereto; any be to one skilled in the art can expect easily, do not depart from change of the present invention or replacement in fact, be also all included within protection scope of the present invention.

Claims (7)

1. edge customer cell selection algorithm in a heterogeneous network, it is characterized in that, each edge customer is with reference to fixing bias, select the Microcell in its place macrocell, according to the change of the number of users of each Microcell before and after selection, dynamic conditioning is carried out to fixing bias, using the bias after adjustment as reference, each edge customer reselects the Microcell in its place macrocell, if with reference to the number of users difference of the Microcell that the bias before and after adjustment obtains, then the bias after adjustment is adjusted according to the number of users of each Microcell in macrocell further, until number of users is constant, load reaches balanced.
2. edge customer cell selection algorithm in heterogeneous network according to claim 1, is characterized in that, specifically comprise the following steps:
S100: initialization bias, base station set and user's collection, with biased initial value for reference, edge customer selects the Microcell in its place macrocell, and the number of users in its region is added up in each Microcell;
S200: adjust each Microcell bias according to the number of users between Microcell, with the bias after adjusting for reference, edge customer carries out reselecting of Microcell and accesses, and the number of users in its region is added up in each Microcell;
S300: before and after bias adjustment, if the number of users in the region of Microcell changes, then return S200; Until number of users is constant, load reaches balanced.
3. edge customer cell selection algorithm in heterogeneous network according to claim 2, is characterized in that, in the step s 100:
If macrocell; Then biased initial value c=0,
If Microcell is c>0 then;
And the span of biased initial value c is [0,20dB].
4. edge customer cell selection algorithm in heterogeneous network according to claim 2, is characterized in that, in the step s 100, edge customer selects the Microcell in its place macrocell according to fixing bias; And
In the step s 100, edge customer carries out reselecting of Microcell according to fixing bias and accesses.
5. edge customer cell selection algorithm in heterogeneous network according to claim 4, is characterized in that, edge customer selects specifically being referenced as of community:
i*＝argmax{RSRP ik+bias i}；
Wherein:
I* represents the Serving cell that user k will select;
RSRP ikrepresent that user k receives the reference signal power from cell i;
Bias irepresent the side-play amount of cell i; If bias i=0, expression i is macrocell; If bias i>0, then represent that i is Microcell.
6. edge customer cell selection algorithm in heterogeneous network according to claim 2, is characterized in that, in step s 200, adjusts each Microcell bias be specially according to the number of users between Microcell:
bias i k+1＝bias i k+a(N m-N i)；
Wherein:
N mrepresent the user in macrocell;
N ishow the number of users in the i of Microcell;
Bias i kan expression kth edge customer selects bias during cell i;
Bias i k+1represent bias during kth+1 edge customer selection cell i;
A represents step-length regulation coefficient, and a > 0.
7. edge customer cell selection algorithm in heterogeneous network according to claim 6, is characterized in that, the value of described step-length regulation coefficient a should meet:
Make bias i k+1=bias i k+ a (N m-N i) ∈ [0,20dB].
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CN103957561A (en) * 2014-04-28 2014-07-30 西安交通大学 Variable step size offset switching method on the basis of optimizing worst user performance

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CN109150259A (en) * 2018-09-06 2019-01-04 华北电力大学（保定） A kind of dynamic migration pilot distribution method based on Massive mimo system
CN109150259B (en) * 2018-09-06 2021-04-27 华北电力大学（保定） Dynamic migration pilot frequency distribution method based on Massive MIMO system

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