IEEE COMMUNICATIONS LETTERS, VOL. 18, NO. 6, JUNE 2014
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Spatial Group Based Random Access for M2M Communications Han Seung Jang, Su Min Kim, Kab Seok Ko, Jiyoung Cha, and Dan Keun Sung
Abstract—We expect that the number of machine-to-machine (M2M) devices will rapidly increase in the near future due to a growing demand for a wide range of M2M applications such as e-health, public safety, surveillance, remote maintenance and control, and smart metering. Therefore, the future cellular networks should accommodate a large number of M2M devices and their random access (RA) requests at a specific time instant. In this letter, we propose a novel RA scheme to effectively increase the number of available preambles for the future M2M communication environment. The proposed scheme provides additional preambles by spatially partitioning a cell coverage into multiple group regions and reducing cyclic shift size in RA preambles. As a result, the proposed RA scheme can accommodate a significantly larger number of machine nodes with much lower collision probability and shorter random access delay, compared to a conventional RA scheme. Index Terms—M2M, random access, group, preamble, LTE.
I. I NTRODUCTION
T
RADITIONALLY, cellular networks have mainly supported human-to-human (H2H) and machine-to-human (M2H) communications such as voice calls, video streaming, and web surfing. However, machine-to-machine (M2M) communication has recently received big attention as a new target service of cellular networks. M2M communications enable many devices to spontaneously communicate with the network or each other in a wide range of M2M applications such as e-health, public safety, surveillance, remote maintenance and control, and smart metering. In case of smart metering, the number of smart meters is expected to be 35,670/cell with a radius of 2 km in urban London [1]. This massive number of devices/sensors may affect the traditional operation of networks and severely deplete the limited radio resources. Accordingly, cellular systems (e.g., 3GPP LTE and WiMax) should bear a huge burden to support M2M services. In general, many M2M devices stay out of connection to save energy consumption except for communicating with the network and transmit a small amount of data. Thus, cellular systems should focus on how to deal with a massive number of connection requests to initiate the network connections beManuscript received February 12, 2014; revised April 14, 2014; accepted April 18, 2014. Date of publication April 29, 2014; date of current version June 6, 2014. This work was supported by Grant No. EEWS-2014-N01140044 from Climate Change Research Hub Project of the KAIST EEWS Research Center (EEWS: Energy, Environment, Water and Sustainability). The associate editor coordinating the review of this letter and approving it for publication was C.-H. Lee. H. S. Jang, K. S. Ko, J. Cha, and D. K. Sung are with the Department of Electrical Engineering, KAIST, Daejeon 305 701, Korea (e-mail: hsjang@ cnr.kaist.ac.kr;
[email protected];
[email protected];
[email protected]. ac.kr). S. M. Kim is with the ACCESS Linneaus Centre, School of Electrical Engineering, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden (e-mail:
[email protected]). Digital Object Identifier 10.1109/LCOMM.2014.2320970
fore data transmission, rather than data traffic from numerous devices. A network connection is initiated through a random access (RA) procedure [2]. In the first step of the procedure, many devices access the eNodeB based on contention using given limited RA resources, which may result in severe RA overload. Therefore, efficient RA and overload control schemes are required for M2M communications. There have been several studies on the efficient RA schemes. Hasan [3] reviewed several RA overload control mechanisms to avoid congestion. Ko et al. [4] proposed a new RA scheme based on fixed timing alignment (TA) information to reduce collision probability. Kwon et al. [5] proposed efficient resource allocation schemes for spatial multi-group based RA in multicell networks. The scope of the previous studies was limited to efficient use of given limited RA resources such as RA preambles. On the contrary, we focus on a novel RA method to effectively increase the number of available preambles by relaxing a constraint on the number of preambles generated per root index. In the future, a massive number of machine nodes may require a much larger number of RA preambles to initially access the network or request uplink resource without collisions. Thus, it is important to increase the number of available RA preambles on a physical random access channel (PRACH) in a cell, which is the main parameter for reducing the RA collision probability and delay. In this letter, we propose a novel RA scheme to effectively increase the number of RA preambles through spatial grouping in a cell. The proposed spatial group based RA (SGRA) scheme can significantly reduce the collision probability and RA delay, and save radio resource used in PRACHs thanks to an effectively increased number of preambles at the additional cost of broadcasting group information and group identification of machine nodes.
II. R ANDOM ACCESS P REAMBLES IN LTE In LTE system, Zadoff-Chu (ZC) sequences are used to Δ generate RA preambles defined as zr [n] = exp[−jπ · r · n · (n + 1)/NZC ] for n = 0, . . . , NZC − 1, where NZC is the sequence length and r ∈ {1, . . . , NZC − 1} is the root index [6]. The ZC sequences have an ideal cyclic autocorrelation property which means the magnitude of the cyclic correlation with the circularly shifted version of itself becomes ZC −1 zr [n]zr∗ [n + σ]| = NZC · a delta function, |crr [σ]| = | N n=0 δ[σ], where crr [σ] is the discrete cyclic autocorrelation function of zr [n] at lag σ and (·)∗ denotes the complex conjugate. With this property, we can observe how much the received sequences are shifted, compared to the reference ZC sequences. Another property is that the magnitude of the cyclic cross-correlation between any two ZC sequences with different root indices, r ZC −1 zr [n]zl∗ [n + σ]| = and l, is constant, i.e., |crl [σ]| = | N n=0 √ NZC , r = l.
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A. Proposed Preamble Generation, Transmission, and Detection Mechanism
Fig. 1. Cell model for the proposed spatial group based random access.
In principle, multiple RA preambles can be generated from the ZC sequence by cyclically shifting the sequence by a factor of cyclic shift, NCS . The sequence shifted by i ∈ {0, . . . , NZC /NCS − 1} is zr,i [n] = zr [(n + NCS × i) mod NZC ] and this sequence is the i-th preamble (PAi ). The number of available preambles per root index (ZC sequence), NZC /NCS , depends on NCS , which highly relies on a cell radius. NCS should be appropriately set to be greater than both of the maximum round-trip delays between the eNodeB and a cell edge node, and the maximum delay spread since the received preambles from any non-synchronized machine nodes should be detected in correct preamble detection zones. Hence, the lower bound of NCS is obtained by NCS ≥ ((20/3)d + τds )NZC /TSEQ + ng [7], where d is the cell radius (km), τds is the maximum delay spread (μs), NZC and TSEQ denote the length and duration (μs) of ZC sequences, respectively, and ng is the number of additional guard samples. Let us deΔ
fine NCS (d) = ((20/3)d + τds )NZC /TSEQ + ng . Note that NCS (d) increases as the cell radius d increases and, thus, the Δ root number of available preambles per root index, NPA (d) = NZC /NCS (d), decreases as d increases. Conventionally, an conv preambles in a single cell, eNodeB serves UEs with fixed NPA conv root conv e.g., NPA = 64 in an LTE system. If NPA (d) < NPA , the conv cell needs to use more than one root index to provide NPA Δ
conv root preambles. We define K = NPA /NPA (d) as the required conv number of root indices for generating fixed NPA preambles in the conventional RA scheme when the cell radius is d.
III. P ROPOSED S PATIAL G ROUP BASED R ANDOM ACCESS (SGRA) S CHEME The proposed SGRA scheme consists of a spatial grouping method and a modified preamble detection mechanism in the first step of the RA procedure to obtain additional preambles. We assume to utilize the same number of root indices, K, as that in the conventional RA scheme. Fig. 1 illustrates a cell model for the proposed SGRA scheme. In this model, the entire cell area with radius d is spatially partitioned into multiple K spatial group regions and the cell uses total K root indices, {r1 , . . . , rK }. The first group has a circular shape with group coverage distance (CD) d1 and the other groups have doughnut shapes with group CDs d2 , . . ., and dK . The k-th group has its own group root index rk and a reduced group cyclic shift of NCS (dk ) with group CD dk instead of d and therefore root utilizes NPA (dk )(= NZC /NCS (dk )) available preambles. root root Our key idea comes from the fact that NPA (dk ) > NPA (d) since NCS (dk ) < NCS (d) for k = 1, . . . , K. To correctly detect preambles employing the reduced group cyclic shifts, we propose a modified preamble detection mechanism based on shifted reference ZC sequences.
The eNodeB broadcasts group parameters such as a group root index set, {r1 , . . . , rK }, and a group CD set, {d1 , . . . , dK }, when the total K root indices are utilized in the cell coverage area. After receiving the parameters, each machine node1 determines its own group based on its distance information between the eNodeB and itself, which can be obtained by various distance estimation methods. Then, each machine node chooses its group root index rk and calculates its group cyclic shift NCS (dk ) with its group CD dk . Thereafter, a machine node in the k-th group randomly selects a preamble among root NPA (dk ) preambles and transmits it on a PRACH time slot. Note that machine nodes within the same group region contend with each other because they generate preambles using the same group root index. For preamble detection at the eNodeB, there exist K shifted reference ZC sequences zrk [n + τBk ] ∈ {zr1 [n + τB1 ], . . . , zrK [n + τBK ]} with K root indices and they are used to calculate cyclic correlation values of the received preambles. τBk denotes the round-trip delay between the eNodeB and the inner boundary of k-th group region, i.e., τB1 = 0, τBk = ((20 × k−1 i=1 di )/3) × (NZC /TSEQ ) − 0.5 , k = 2, . . . , K. Among all shifted reference ZC sequences, only zrk [n + τBk ] has a cyclic auto-correlation property with the preambles generated by group root index rk , which yields a delta function signal. If the magnitude of the delta function signal is greater than or equal to a detection threshold γ, the received preamble is detected in one of preamble detection zones. However, if τBk is not considered in reference ZC sequences, the eNodeB will detect preambles in wrong preamble detection zones. To resolve this problem, we propose a shifted reference ZC sequence, zrk [n + τBk ], which is shifted by τBk from the original reference ZC sequence, zrk [n]. The cyclic correlation root of preamble i ∈ [0, NPA (dk ) − 1] from machine node m in the k-th group region with the proposed shifted reference NZC −1 zrk [n + ZC sequence is expressed as |csft rk rk [σ]| = | n=0 τBk ]a∗ [n + σ]zr∗k [n − tm + σ]| = α[σ]δ[σ −χm ], where tm = NCS (dk ) × i + τBk + τkm denotes the received time instance, χm = tm − τBk = NCS (dk ) × i + τkm denotes the detection time instance, τkm is the round trip delay between the inner boundary of the k-th group and machine node m, a[n] is the attenuation factor, and α[σ] is the magnitude of a delta function. Note that the round trip delay between the eNodeB and machine node m in the k-th group region, τBm , is split into τBk and τkm , i.e, τBm = τBk + τkm . As an example, when d1 = 2.0 km, d2 = 1.1 km, and d3 = 0.9 km, Fig. 2 shows the magnitude of the cyclic correlation value of preamble 0 from machine node m located 4 km away from eNodeB in the conventional RA scheme and the proposed SGRA scheme. In the conventional RA scheme, the received time instance tm and the detection time instance χm are the same as ((20 × 4)/3) × (839/800) − 0.5 = 28 using the original reference ZC sequence and PA0 is correctly detected in the PA0 zone due to a large value of NCS (d).
1 In this letter, we assume fixed or low-speed machine nodes served by the eNodeB. For high-speed machine nodes, we need to consider the cyclic shift restrictions as stated in [7] but it is beyond the scope of this letter.
JANG et al.: SPATIAL GROUP BASED RANDOM ACCESS FOR M2M COMMUNICATIONS
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TABLE I S IMULATION PARAMETERS AND VALUES
Fig. 2. Preamble detection of the conventional RA scheme and the proposed SGRA scheme in 4 km cell-radius case.
On the contrary, in the proposed SGRA scheme, PA0 is incorrectly detected in the PA2 zone due to the reduced value of NCS (d3 ) if the original reference ZC sequence is used. Based on the proposed shifted reference sequence, zr3 [n + τB3 ], the detection time instance of the preamble χm is adjusted as tm − τB3 = ((20 × 4)/3) × (839/800) − 0.5 − ((20 × 3.1)/3) × (839/800) − 0.5 = 6 and, therefore, PA0 is correctly detected in the corresponding PA0 zone. B. Collision Probability If more than one machine nodes in the same group region transmit the same preamble on the same RA time slot in the first step of the RA procedure, they receive the same uplink grant and TA information in RA response (RAR) message in the second step. Then, they transmit their desired messages on the same uplink resource, which causes a collision in the third step. If a collision occurs, eNodeB does not send a feedback message in the fourth step. Hence, the machine nodes finally recognize the collision and defer the subsequent RA [2]. Let p denote the collision probability. The total RA arrival rate in the i-th PRACH slot is expressed as [8]: λT [i] = λ · M · TRACH + η · p · λT [i − 1], where λ denotes the RA arrival rate of a device (sec−1 ), M denotes the number of machine nodes in a single cell, TRACH denotes the period of PRACH time slot, and η · p · λT [i − 1] represents the reattempted RA arrival rate from the previous slot due to collisions. η denotes the Q i i reducing factor expressed as η = Q−1 i=1 p / i=1 p , where Q is the maximum number of RA trials. In steady state, we can drop the slot index i, and then λT is expressed as λT = (λ · M · TRACH )/(1 − η · p). As a result, p = 1 − (1 − 1/NPA )λT where NPA is the number of preambles. Assuming η ≈ 1, p = 1 − exp[W (ln(1 − (1/NPA )) · λ · M · TRACH )] where W (x) denotes the Lambert W function. C. Optimal Group Coverage Distances Now, we find optimal group CDs, d∗ = [d∗1 , . . . , d∗K ] to minimize the sum of collision probabilities, when d is the overall cell radius and K is the required number of root indices. The collision probability of the k-th group, pk (d), root is expressed as 1 − exp[W (ln(1 − 1/NPA (dk )) · λ · Mk (d) · TRACH )], where Mk (d) is the number of machine nodes
in the k-th group region. The optimization problem is formulated as minimize d
subject to
K
pk (d)
k=1
|pk (d) − pj (d)| ≤ ε, k, j = 1, . . . , K, k = j, d1 + · · · + dK = d, dk ≥ dmin , k = 1, . . . , K
where ε is the fairness parameter and dmin is the minimum root conv group coverage distance satisfying NPA (dk ) ≤ NPA . We solve the optimization problem using a Matlab optimization tool since this is a combinatorial problem which is NPcomplete. In the following, the optimum group CDs found are given in Table I with ε = 0.005 and dmin = 0.65. IV. P ERFORMANCE E VALUATION In this section, we evaluate the performance of the proposed SGRA scheme in terms of collision probability, acceptable number of machine nodes, and average RA delay. In addition, we address a robustness problem due to group mismatching. Table I lists a set of simulation parameters. The smallest PRACH resource, 1.08 MHz bandwidth and a 10 ms period of time slot, is selected among possible PRACH configurations in LTE [2]. We assume that there are no miss detections and false alarms during preamble detection. For preamble allocation between H2H and M2M communications, we assume to separate the set of all available preambles into two subsets [1], where one for M2M and the other for H2H, to avoid interference M2M between both groups. M2M utilizes NPA preambles and conv M2M H2H utilizes NPA − NPA preambles in the conventional RA scheme. In the proposed SGRA scheme, the cell utilizes to root conv M2M tal K k=1 NPA (dk ) preambles and allocates NPA − NPA preambles to H2H and the remainder to M2M. M machine nodes following a Poisson distribution with arrival rate λ access the network and they are uniformly deployed in a cell. We consider two scenarios according to the cell radius, 2 km for an urban scenario and 4 km for a suburban scenario. Fig. 3 shows the collision probabilities of the conventional RA scheme and the proposed SGRA scheme in 2 km and 4 km cells. When 1/λ = 3 min, the proposed SGRA scheme yields collision probabilities of about 3% and 2% in 2 km and 4 km
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Fig. 3. Collision probabilities vs. random access arrival rate when d = 2 km and d = 4 km.
Fig. 4. Average random access delay vs. random access arrival rate when d = 2 km and d = 4 km.
TABLE II ACCEPTABLE N UMBER OF S MART M ETERS W ITH D IFFERENT TARGET C OLLISION P ROBABILITIES W HEN 1/λ = 5 min
cells, respectively, while the conventional RA scheme has a collision probability of 39% in both 2 km and 4 km cells. The proposed SGRA scheme in 4 km cell yields lower collision probabilities than those of 2 km cell becuase it can utilize one more root index which generates 33 more preambles. In conclusion, the proposed SGRA scheme can significantly reduce the collision probabilities, compared to the conventional RA scheme thanks to an effectively increased number of preambles for M2M communications. Table II shows the acceptable number of smart meters with different target collision probabilities assuming the period of metering report, 1/λ = 5 min [1]. With a 3% collision probability, the proposed SGRA scheme can accommodate 44,500 smart meters within 2 km cell, 78,200 smart meters within 4 km cell, while the conventional RA scheme can accommodate only 4,900 smart meters. This implies that the proposed SGRA scheme can sufficiently accommodate the number of smart meters (e.g., 35,670) required in the urban London scenario [1] under a 3% collision probability. Fig. 4 shows the average RA delay. The proposed SGRA scheme yields almost fixed low-average RA delays regardless of the cell radius, while the conventional RA scheme shows an exponential growth of the average RA delay with the arrival rate. If a machine node mistakenly selects its group and sends a preamble of a different group due to distance estimation error, it cannot receive an RAR message from the eNodeB. To avoid continued failures, we assume that the machine node replaces its group with the nearest neighbor group after NF consecutive failures in receiving the RAR message. In Fig. 5, it is shown that the proposed SGRA scheme is still robust against the group mismatching caused by the distance estimation error, although the performance is slightly degraded. V. C ONCLUSION In this letter, we emphasized the necessity of a new RA scheme to effectively increase RA resource in M2M
Fig. 5. Effect of distance estimation error when d = 2 km, 1/λ = 5 min, and ˆ = D + De , where D is the NF = 3. The estimated distance is expressed as D true distance and the estimation error is modeled as De ∼ N (0, σe2 ).
communications and proposed an SGRA scheme. Since the SGRA scheme can provide additional RA preambles, it significantly reduces the collision probability and average RA delay, and save resource used in PRACHs. As a result, the SGRA scheme can accommodate a significantly larger number of nodes with lower collision probabilities and shorter average RA delay. R EFERENCES [1] “Study on RAN Improvements for Machine-Type Communications,” Sophia-Antipolis, France, TR 37.868 V11.0.0, Sep. 2011. [2] “Medium Access Control (MAC) Protocol Specification,” SophiaAntipolis, France, TR 36.321 V11.3.0, Jun. 2013. [3] M. Hasan, E. Hossain, and D. Niyato, “Random access for machineto-machine communication in LTE-advanced networks: Issues and approaches,” IEEE Commun. Mag., vol. 51, no. 6, pp. 86–93, Jun. 2013. [4] K. S. Ko et al., “A novel random access for fixed-location machine-tomachine communications in OFDMA based systems,” IEEE Commun. Lett., vol. 16, no. 9, pp. 1428–1431, Sep. 2012. [5] T. Kwon and J.-W. Choi, “Multi-group random access resource allocation for M2M devices in multicell systems,” IEEE Commun. Lett., vol. 16, no. 6, pp. 834–837, Jun. 2012. [6] D. Chu, “Polyphase codes with good periodic correlation properties,” IEEE Trans. Inf. Theory, vol. 18, no. 4, pp. 531–532, Jul. 1972. [7] S. Sesia, I. Toufik, and M. Baker, LTE—The UMTS Long Term Evolution From Theory to Practice. Hoboken, NJ, USA: Wiley, 2009. [8] Y.-J. Choi, S. Park, and S. Bahk, “Multichannel random access in OFDMA wireless networks,” IEEE J. Sel. Areas Commun., vol. 24, no. 3, pp. 603– 613, Mar. 2006.
