neighboring cell#2 are grouped together to use chunk S2 and S3.,. And each chunk or group of chunks are limited to be reassigned to the UEs belonging to the ...
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings.
Uplink Adaptive Resource Allocation Mitigating Inter-cell Interference Fluctuation for Future Cellular Systems Minghai Feng, Lan Chen, and Xiaoming She DoCoMo Beijing-Labs, Beijing, China Email: {feng, chen, she}@docomolabs-beijing.com.cn Abstract—This paper aims to address the problem of inter-cell interference fluctuation, which is inherent on the uplink direction of future cellular systems employing OFDMA or single carrier as multiple access technology. We present here a novel uplink adaptive resource allocation algorithm based on inter-cell interference measurement to achieve both multi-user diversity gain and the cancellation of inter-cell interference fluctuation. The phenomenon of inter-cell interference fluctuation in future cellular systems is explained and its negative effect on traditional resource allocation methods is analyzed. The proposed algorithm includes two independent sub-algorithms: 1) user grouping based resource allocation scheme, in which radio resource is limited to be reallocated within a group of users generating close inter-cell interference; 2) power compensation based resource allocation scheme, in which inter-cell interference fluctuation is compensated by adjusting transmitting power of UE. And performance evaluation results show that our algorithms smooth the inter-cell interference successfully and can improve system spectrum efficiency by 10-15% compared with existing traditional adaptive resource allocation schemes. Index Terms—Inter-cell interference management, uplink adaptive resource allocation, inter-cell interference fluctuation.
I. INTRODUCTION As a promising technology to address the demand of next generation wireless communication systems, Orthogonal Frequency Division Multiplexing (OFDM) is attractive mainly due to its dividing the total system bandwidth into a large number of parallel, flat faded and orthogonally overlapped sub-carriers to better combat frequency selective fading, reduce inter symbol interference (ISI), and improve spectrum efficiency. Thanks to these merits, OFDM has been recommended as strong physical layer technique candidate in such as 3GPP Long Term Evolution and IEEE 802 series standards and so on. In addition to the basic features, when Orthogonal Frequency Division Multiple Access (OFDMA) or DFT-S-OFDMA (one of single carrier techniques, suggested for uplink direction with low PAPR) schemes are employed, sub-carrier could be adaptively allocated among users, multi-user diversity gain [1][2] is further provided by exploiting the fact that different users experience different amount of fading at a particular instant of time and a deep faded sub-carrier for one UE is probably a less attenuated one for another UE. Therefore OFDMA or DFT-S-OFDMA combined with efficient resource allocation scheme can increase significantly system throughput. However, coming along with the application of OFDMA or
single carrier multiple access techniques is the specific property of none-white colour interference and inter-cell interference (ICI) fluctuation as mentioned in [3]-[5], that is the amount of perceived ICI in one cell is not flat across sub-carriers in frequency domain and fluctuates dramatically with the resource reallocation in the surrounding cells in time domain, compared with nearly white ICI in CDMA based system [6]-[9]. Since in such system, the ICI comes from a single user in each cell, there is insufficient averaging of interference on each single carrier or sub-carrier, leading to the significant ICI fluctuation across sub-carriers and across resource reallocations. These new emerging features will result in bursty or unpredictable ICI and corresponding SINR estimation error, finally challenge the efficiency of traditional resource allocation methods. Several resource allocation methods aiming at ICI mitigation have been studied in [5], including ICI randomization, cancellation and coordination, however the problem of interference fluctuation is not well solved. Centralized resource allocation scheme has been proposed in [10], but the implementation requires exchange of messages between base stations and the network controller at a very fine time scale and thus may not practical. The objective of this paper is to propose novel uplink adaptive resource allocation algorithm to achieve multi-user diversity gain and cancel the negative effect of ICI fluctuation as well, without much complexity increased. The remainder of the paper is organized as follows: section II shows the system model, section III describes the character of ICI fluctuation in future cellular systems. Section IV investigates the inefficiency of existing resource allocation schemes confronted with ICI fluctuation. In section V we present two novel uplink adaptive resource allocation algorithms based on ICI measurement to solve the problem. Performance comparison of our approaches with traditional resource allocation methods is shown in section VI, and section VII concludes the paper. II. SYSTEM MODEL The efficient utilization of spectrum requires adaptive resource allocation approach with the adaptability to varying wireless network conditions. When focusing on resource allocation among multi-user in a cellular system in the context of OFDM or single carrier air interface, SINR of each user on each sub-carrier or single carrier has been a major criterion.
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This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings.
SINR i j =
Pi j ∗ H i j N
∑I
n int ra
n =1
M
+∑I
(1)
m int er
+ Nj
m =1
j
Where SINRi is the signal to noise and interference ratio of j
j
user i on sub-carrier j , Pi is the allocated power, H i is the n
channel gain of user i on sub-carrier j , I int ra is intra cell m
interference from user n , I int er is inter-cell interference from user m , and N j is the thermal noise on sub-carrier j . While actually intra-cell interference in such system is small because of intra-cell orthogonality that is supported by sub-carrier orthogonality, and the dominant part of interference comes from inter-cell interference. Then formula (1) can be rewritten as
SINR
j i
≈
Pi j ∗ H i j M
∑I
m int er
+ N
(2) j
m =1
Base on this calculated SINR, radio resource, i.e., sub-carrier, power and bit is allocated among users. Besides network conditions, user requirements and QoS constraints should also be considered, but they are not the focus in this paper. III. UPLINK INTER-CELL INTERFERENCE CONSIDERATION Uplink inter-cell interference in future cellular systems employing OFDMA or single carrier as multiple access techniques have the character of none-white color across sub-carrier and fluctuation across resource reallocation, which has essential influence on resource allocation method. And we first describe and analyze these two items in detail as following. A. None-white color inter-cell interference across sub-carrier It has been well recognized that the inter-cell interference could be viewed in the form of AWGN (Additional White Gaussian Noise) in a CDMA based cellular system, where frequency band is shared among users by using orthogonal code in each cell, given a frequency re-use factor of 1, there will be a large number of users in the adjacent cells that generate co-channel interference. Hereby the inter-cell interference as a sum of all the signals will average the difference and finally have a nearly flat spectrum. Compared with a CDMA based cellular system, in an OFDM based cellular system, one sub-carrier or frequency band is engaged by only one user in each cell. So inter-cell interference consists of only the signals emitted from the very user occupying the same sub-carrier or sub-band in the other cells. This small number of co-channel interferences results in that the inter-cell interference perceived by the base station from other cells might not be perfectly white.
Fig. 1 None-white colour inter-cell interference
First, the interfering signals undergo time dispersion, and do not have a flat spectrum, and furthermore on different sub-carrier uplink interfering signals may come from different user and experience different path-loss, shadowing and fast fading, hereby the resulting spectrum from each of these uneven signals will contain fluctuation. Without sufficient averaging, it is therefore likely that the total interference spectrum observed by the base station would not be flat. In order to validate the phenomenon of none-white colour inter-cell interference, a system level simulation work is done depending on the parameters listed in the table 1, in which 5 user per sector and 300 total useful sub-carriers are assumed. In figure 1, we display the inter-cell interference received on each sub-carrier from the simulation, and according to this result, the fluctuation of inter-cell interference across sub-carrier is obvious, the “frequency selectivity” of interference may be raised by small scale fading of the same user or by different user with different large scale fading users, combined with insufficient averaging as depicted in the figure. B. Inter-cell interference fluctuation across resource allocation As we can see from the above section, the small number of co-channel interference components leads to the none-white colour inter-cell interference in spectrum and additionally because one pattern of none-white colour inter-cell interference is determined by one corresponding pattern of resource allocation in the surrounding cells, the perceived inter-cell interference will vary with resource reallocation in the surrounding cells.` Hence besides the none-white colour inter-cell interference across sub-carrier, there is inter-cell interference fluctuation across resource reallocation. One more simple simulation work is done based on the parameters in table 1, and we here give a record of suffered inter-cell interference on one sub-carrier with periodical randomly sub-carrier allocation and fixed uplink transmitting power in all of the cells, as shown in figure 2. In the figure, the x-axis denotes the index of simulation time in TTI, and period of resource reallocation is 3 TTI, the y-axis denotes the quantity of suffered interference on one sub-carrier.
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings.
Fig. 2 Inter-cell interference variation across resource allocation
From the result, we can clearly see that, the suffered inter-cell interference is fluctuating dramatically with the resource reallocation in the adjacent cells. The maximum fluctuation is about 7 dB and the average fluctuation is about 4 dB. IV. NEGATIVE EFFECT OF INTER-CELL INTERFERENCE FLUCTUATION ON RESOURCE ALLOCATION The aforementioned new emerging character of inter-cell interference fluctuation will finally challenge the efficiency of channel dependent resource allocation and link adaptation such as adaptive modulation and coding (AMC). Since inter-cell interference is not white in spectrum any more, so besides sub-carrier fading, sub-carrier suffered interference should also be taken into account in resource allocation in order to exploit the channel and obtain the multi-user diversity gain as mentioned in section II. However, different from the reliable estimation of the sub-carrier fading, the accurate estimation of inter-cell interference may be unavailable, because the fluctuation across resource allocation makes inter-cell interference bursty and unpredictable, which is determined by the pattern of resource reallocation in the neighbouring cells. We illustrate such an example in Fig. 3. Figure 3(a) illustrates the observed uplink inter-cell interference at the channel quality indicator (CQI) measurement timing for the channel dependent resource allocation while the perceived inter-cell interference at the timing of transmitting the actual scheduled packet is depicted in Fig. 3(b), respectively. From the comparison between Fig. 3(a) and 3(b), we can see large fluctuation of inter-cell interference, which may cause severe degradation both in multi-user diversity effect based on channel-dependent resource allocation and MCS selection accuracy in AMC. As a consequence, in a multi-cell environment the objective of adaptive resource allocation schemes involves also how to manage resource in an cooperative and efficient manner to minimize the effect of inter-cell interference. V. NOVEL ADAPTIVE RESOURCE ALLOCATION SCHEMES We present here two novel uplink adaptive resource allocation algorithms based on inter-cell interference measurement to achieve both multi-user diversity gain and the cancellation of the effect of inter-cell interference fluctuation. One is inter-cell interference and user grouping based adaptive resource allocation and the other is inter-cell interference and power compensation based adaptive resource allocation.
Fig.3 Example of inter-cell interference variation in frequency domain
Scheme 1: User grouping based adaptive resource allocation. The main idea of this algorithm is at time of adaptive resource reallocation, resource block (sub-channel or chunk) is limited to be reassigned to a group of users who will generate close inter-cell interference to neighboring cells as the previously served user. A. Algorithm description The main procedures of our proposed inter-cell interference measurement based adaptive resource allocation method are as follows: (1). Each UE measures the long term path-loss between the UE and its strongest (most interfered) neighboring Node B by utilizing the downlink common reference signal. (2). UE feeds back the measured path-loss and the corresponding cell ID of that neighboring cell to its serving cell. (3). Serving cell calculates the uplink inter-cell interference from each UE to the neighboring cell based on the feedback information: long-term path loss and UE power head room. (4). UEs having similar inter-cell interference to the same neighboring cell are classified into one group. For each UE group, the same sub-set of the whole uplink spectrum (we call it chunk hereafter) is pre-allocated. (5). Frequency and time domain channel-dependent scheduling such as Proportional fairness (PF) algorithm are applied within each group of UEs for respective chunk. Figure 4 depicts an overview of our proposed group based scheduling method assuming 6 UEs and 6 chunks. In this case, UE #2, #3, and #6, which have the same inter-cell interference quantized value, i.e., close ICI, to the adjacent neighboring cell #3 are grouped together to use chunk S4 to S6, and UE #4 and #5, which have the same quantized inter-cell interference to the neighboring cell#2 are grouped together to use chunk S2 and S3., And each chunk or group of chunks are limited to be reassigned to the UEs belonging to the same UE group.
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings.
Serving cell
Group #1 Group #2
Neighboring cell#1
UE#5
Group #3 UE#6
UE#1
UE#3 UE#4
UE#2
Close ICI
Close ICI Neighboring cell #3
Neighboring cell#2 Group 1 Group 2
S1
S2
S3
Group 3
S4
S5
S6
f
Frequency allocation in serving cells Fig. 4 Overview of proposed User grouping based resource allocation
And the following formula is defined when it comes to determine “similar inter-cell interference” or the size of the “user group” in detail. (3) φ s = {i , j } = arg ( I int er ( i , B ) − I int er ( j , B ) ≤ Th ) Where I int er (i, B ) denotes inter-cell interference on from user i to cell B in uplink direction. Th is the threshold parameter, which actually determine the size of the user group and at the same time limit the extent of inter-cell interference fluctuation. Obviously it is this threshold parameters that balance between the multi-user diversity and the cancellation of inter-cell interference fluctuation, the bigger this threshold, the more multi-user diversity and the more interference fluctuation; the smaller this value, the less multi-user diversity and less interference fluctuation. B. Algorithm complexity and overhead consideration The measurement of inter-cell interference generated by UE to its adjacent cells acts as a vital role in the algorithm and meanwhile raise the dominant part of the algorithm complexity. The inter-cell interference experiences large scale fading (i.e. path-loss and shadowing) and small scale fading. However, in fact the significant difference of large scale fading of different users accounts for the large inter-cell interference fluctuation, which has been demonstrated in figure 1 and 3. Therefore, long term inter-cell interference instead of short term inter-cell interference measurement makes more sense when considering both the algorithm complexity and acceptable performance gain. In case of this, since the long term inter-cell pilot channel is always measured for handover issue, so no more overhead of this algorithm is needed, and the interference is calculated as below in case of that I int er (k , B, s ) = Pd (k , s ) × I int er ( B, k , s0 ) / Pc ( B, s 0 ) (4) Where Pd ( k , s ) denotes transmitting power from user k on chunk s , I int er ( B, k , s0 ) denotes long term downlink inter-cell
interference on pilot channel from base station B to user k and Pc ( B, s0 ) denotes the transmitting power on downlink pilot channel. In conclusion, by this scheme, the generated inter-cell interference to other cells will keep nearly the same no matter how resource is managed in the cell of interest. And vice versa, perceived inter-cell interference in the cell of interest maintains also constant through resource reallocation in other cells. The cancellation of inter-cell interference fluctuation is derived at a little cost of multi-user diversity. And the reliable interference estimation is achieved through resource allocation. Scheme 2: Power compensation based resource allocation. Besides the user grouping and inter-cell interference measurement based adaptive resource allocation scheme, we put forward another scheme as power compensation and inter-cell interference measurement based resource allocation method to solve the problem. The main idea of this scheme is compensating inter-cell interference fluctuation by adjusting user’s transmitting power in resource reallocation. The algorithm includes the following main procedures (1). Each UE measures the long term path-loss between the UE and its strongest (most interfered) neighboring Node B by utilizing the downlink common reference signal. (2). UE feeds back the measured path-loss and the corresponding cell ID of that neighboring cell to its serving cell. (3). Serving cell calculates the uplink inter-cell interference from each UE to the neighboring cell based on the feedback information: long-term path loss and UE power head room. (4). UEs having similar inter-cell interference to the same neighboring cell are classified into one group. For each UE group, the same sub-set of the whole uplink spectrum (we call it chunk hereafter) is pre-allocated. (5). Calculate the transmitting power Pt according to the below formula for the users in one user group, if the user uses one chunk s in their corresponding chunk group
PT (k ) = P( j ) ×
PLint er ( j, B ) PLint er (k , B )
(5)
Where j is the user who used chunk s previously (before
PLint er ( j, B ) denotes the measured long term path-loss between the UE j and its resource
reallocation);
strongest (most interfered) neighboring BTS P( j ) denotes the transmitting power of user j .
B;
(6). Use the calculated power PT (k ) combined with other intra cell channel information to estimate SINR of this user.
SINR(k , s) = PT (k ) × H int ra (k , B, s) / I ( B, s) Where H int ra ( k , B, s ) denotes the channel gain on chunk s from user k to base station B and I ( B , s ) denotes the suffered interference on chunk s .
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings.
(7). Based on this power limited SINR( k , s ) , multi-user scheduling such as Max C/I or Proportional fairness (PF) algorithm are applied within each group of UEs for respective chunk. Actually, deducing from formula (5), we can derive the generated inter-cell interference after power adjustment as following
Iinter (k, B) = PT (k ) × PLinter (k, B)
= P( j) ×
PLinter ( j, B) × PLinter (k, B) PLinter (k, B)
PL (k , B) = Iinter ( j, B) × inter PLinter (k , B)
(6)
= Iinter ( j, B) ×1 = Iinter ( j, B) Therefore after adjusting transmitting power as formula (5), the generated inter-cell interference to the neighboring cells by the new severed user will not be higher than the previously severed user. Compared with user grouping based resource allocation scheme, this method can also cancel the negative effect of inter-cell interference fluctuation by compensating transmitting power of UE. And combined with multi-user scheduling, the priority of one user is controlled via allocating different power on different chunk. VI. NUMERICAL RESULTS In this section, performance evaluation of the proposed schemes is presented. The simulation results including the inter-cell interference fluctuation cancellation, spectrum efficiency improvement and performance of cell edge users are shown and analyzed. A. Simulation parameters The basic parameters adopted in the simulator are listed in table 1. B. Cell layout and user distribution A quasi-static system level simulator is built for this purpose, where snapshot like simulation are used. Each snapshot spans a relatively short time, within each snapshot, the location of the mobiles is quasi fixed (no path-loss fading) but small-scale fading is taken into account, and a number of snapshots is modeled to collect the statistic results. Users are uniform random distributed among the cells, and one user distribution case in one snapshot is shown in figure 6. C. Inter-cell interference fluctuation cancellation Figure 7 shows the received inter-cell interference across traditional resource allocation and the user grouping based and power compensation based resource allocation schemes. It is obvious that the interferences before and after once resource allocation is smoothed successfully by our proposed methods.
Fig. 6 Cell layout and a case of user distribution
Table 1, simulation parameters Parameters Assumption Cellular layout 19cell, 3sectors/cell Antenna horizontal 70 deg (-3 dB) with 20 dB pattern front-to-back ratio Site to site distance 500 m Propagation model 128.1 + 37.6log10(R), R(km) Slow fading variation 8 dB Carrier frequency 2000 MHz UE distribution Uniform random distribution System bandwidth 5MHz Thermal noise -174dBm/Hz Fading channel model Pedestrian B User speed 3 km/h Scheduling scheme Max C/I Useful sub-carrier 300 FFT size 512 Modulation QPSK, 16QAM User grouping threshold 3dB Traffic model Full buffer Physical layer technique DFT-S-OFDMA D. Spectrum efficiency improvement The improvement of spectrum efficiency of our algorithms is evaluated in this part compared with traditional Max C/I based resource allocation and an inter-cell interference coordination method as proposed in [11]. The available average sector throughputs by using the above algorithms are presented in figure 8. From the results, we can see that the spectrum efficiency is about 15-17% increased by user grouping and power compensation based scheme, depending on short term inter-cell interference measurement, compared with Max C/I based scheme, and about 25% improvement compared with inter-cell interference coordination scheme. Furthermore, when depending on long term inter-cell interference measurement, the user grouping and power compensation based scheme can still improve sector throughput about 10-11% compared with Max C/I based scheme, and 18% improvement compared with inter-cell interference coordination scheme.
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings.
8.4 8.2
Sector throughput (Mbps)
7.8 7.4 7 6.6 6.2 power com pens ation & long term ICI meas ure 5.8
us er grouping & long term ICI measure us er grouping & s hort term ICI measure
5.4
power com pens ation and s hort term ICI meas ure traditional Max C/I res ource allocation
5
ICI coordination method 4.6
12
13
14
15
16
17
num. of user per sector
18
19
20
Fig. 8 Spectrum efficiency improvement 900
Fig. 7 Inter-cell interference smoothing
E. Cell edge user throughput Besides the average sector throughput, we also give the cell edge user throughput evaluation. Figure 9 shows the average user throughput with different distance from user to the base station. According to the results, compared with Max C/I based scheme, at cell central area, the performance is close to each other, since users at this area suffers either low inter-cell interference or low interference fluctuation. While as the distance to base station increases, the performance improvement of our schemes becomes obvious. And when it comes to the inter-cell interference coordination scheme, it has a little better performance at cell edge area than our schemes but at a huge loss of performance at cell central area. Actually our schemes are compatible with the interference coordination method, so a combined scheme will further enhance the performance of cell edge users. VII. CONCLUSION In this paper we investigate the relationship between resource allocation and inter-cell interference in future cellular systems using OFDMA or DFT-S-OFDMA, analyze the phenomenon of inter-cell interference fluctuation across sub-carrier and resource reallocation. Two novel algorithms are proposed as user grouping based and power compensation based adaptive resource allocation with a limitation to reallocation of sub-carrier and power respectively. And the gain derived from cancellation of inter-cell interference fluctuation is expected to be more than the loss of multi-user diversity. Finally performance evaluation work verified that our scheme can improve spectrum efficiency by 10-15% compared with existing resource allocation method.
Average user throughput(kbps)
800 700 600 500 400 User grouping based algorithm 300
Power compensation based algorithm ICI coordination algorithm
200
Traditional Max C/I algorithm 100
0
50
100
150 200 Distance to base station(m)
250
300
Fig. 9 Cell edge user throughput improvement
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