Adaptive Radio Resource Management for a Cellular System with Fixed Relay Nodes Fredrik Boye, Member, IEEE, Peter Rost, Student Member, IEEE, and Gerhard Fettweis, Senior Member, IEEE Technische Universit¨at Dresden, Vodafone Chair Mobile Communications Systems, Dresden, Germany EMail:
[email protected], {rost, fettweis}@ifn.et.tu-dresden.de
Abstract— Future mobile communications systems demand for higher data rates and service quality compared to state-of-the-art systems. One way to achieve this ambitious goal is to use relaying which improves channel conditions by adding one or more intermediate nodes to support communication pairs. As relays do not rely on a wired backhaul, they can be flexibly deployed while keeping infrastructure costs lower than for additional base stations. Furthermore, relaying offers a variety of possible protocols - one promising concept is the cooperative transmission of different access points. Besides, the concurrent usage of different strategies is likely to be realized in next generation mobile communications systems, such that depending on the actual channel conditions the most beneficial strategy can be chosen. In this paper we present an adaptive and simple approach of partitioning the radio resources among different single-path and multi-path protocols. Our numerical analysis uses system level simulations of a 4G mobile communications system with relay enhanced cells and multiple antenna transmission.
I. I NTRODUCTION Scarce and cost demanding spectrum in future mobile communications systems as well as the need for high data rates and service quality require an improved spectral efficiency compared to currently deployed systems. Relaying [1], [2] is among the likely candidates to address these demands in future cellular systems. In relaying based systems the direct link between two communication partners, i. e., between base station (BS) and user terminal (UT), is split up by additional radio access points which are called relay nodes (RNs). Multihop networks provide many benefits: large scale diversity through cooperative transmission of multiple access points, reduced pathloss and shadowing, and the improved routing and resource management flexibility [3]. A usual and from a cost-benefit point of view reasonable assumption is the half-duplex constraint, i. e., relay nodes can either transmit or receive on a particular time-frequency resource [4]. This practical limitation is one of the main challenges in relaying based systems as it implies a tradeoff between in-band resources spent to serve relay nodes and the improved channel conditions and thus higher spectral efficiency. This work concentrates on single-path relaying (also Part of this work has been performed in the framework of the IST project IST-4-027756 WINNER II, which is partly funded by the European Union. The authors would like to acknowledge the contributions of their colleagues in WINNER II, although the views expressed are those of the authors and do not necessarily represent the project.
called “conventional relaying”) and multi-path relaying (“cooperative relaying”) as they offer higher resource management flexibility compared to analog relaying. In this work, we assume two-hop transmissions which implies a relay deployment providing good channel conditions between base stations and fixed relay nodes. Our analysis further takes advantage of multiple antenna transmissions which show promising results for link level setups [5], [6]. In the sequel of this work we investigate whether those performance benefits experienced on link level translate to system level. The paper is organized as follows: Section II introduces the considered system model which is based on the results developed within the European research project WINNER [7]. Afterwards, Section III presents the different transmission strategies used in our analysis as well as the distribution of the radio resource management. Then, Section IV analyzes this system using numerical results for the 5%-ile and average user throughput. Finally, we conclude this paper in Section V. II. S YSTEM M ODEL AND N OTATION We consider a cellular network using the specification developed within the WINNER-project [7], in particular we investigate a base urban scenario where sites are uniformly deployed with a distance of 1000m between each other. At each site three base stations are used where each base station serves a sector of 120 degrees using four antennas. Relay nodes are equipped with two omni-directional antennas while user terminals have one antenna. Each base station underlies a transmit power constraint of 46 dBm, relay nodes do transmit with a maximum power of 37 dBm. Furthermore, the noise figure at user terminals is defined to be 7 dB and at each receiver the noise power spectral density is specified to be −174 dBm/Hz. We further assume that user terminals are uniformly placed with a density of about 86 users/km2 . The analyzed system is based on time division duplex OFDMA where 15 OFDMA symbols (each of duration 20.48 µs including guard interval) are grouped to one frame. Frames are alternatingly used for uplink and downlink. Eight pairs of one uplink and downlink frame are grouped to build one superframe which includes a preamble for different signaling and synchronization issues. In our analysis we concentrate on a comparison of relaying versus conventional cellular systems and thus assume perfect synchronization and signaling. Furthermore, the system operates at a carrier frequency of 3.95 GHz and uses a total bandwidth of B=100 MHz. Each
center band g edge band 2
cell 3
center band g edge band 3
center band g
frequency
Fig. 1.
Partitioning of the overall bandwidth in edge band and center band.
b
↓
↓
OFDM symbol consists of 2048 subcarriers and taking guard bands into account, 1840 subcarriers are used for the actual data transmission. Besides, we concentrate on the investigation of our system’s downlink as it particularly must provide very high data rates due to the asymmetric service demands of multimedia transmissions and alike services and use the channel models defined by WINNER in [8]. Furthermore, we assume that each node has perfect knowledge of its backward and forward channels and use a LQ precoding approach for the broadcast access. Consider a system with M transmit and N receive antennas (M ≥ N ). Let H = [hij ] ∈ CN ×M be the channel matrix where hij indicates the complex channel gain between receive antenna i ∈ [1; N ] and transmit antenna j ∈ [1; M ]. This channel can be decomposed into a lower triangular matrix L ∈ CN ×N and an unitary matrix Q ∈ CN ×M . The data symbols d ∈ CN ×1 are mapped to the antenna output x ∈ CN ×1 by multiplication with the conjugate complex of Q. Therefore, the received signal becomes y = Hx + n = LQQT d + n = Ld + n, where n ∈ CN ×1 denotes the noise vector with covariance σ 2 . Assuming one receive antenna per terminal, the user with index k receives only the signal at antenna i and interference from users l > k is forced to zero. Using dirty paper coding [9] the receive signal of user k is also not interfered by transmissions to users l < k, which implies a single user channel. This precoding approach referred to as Zero-Forcing Dirty Paper Coding by [10] does not achieve capacity but requires less computation resources compared to the capacity achieving approach presented in [11]. Besides, it was shown in [10] that using a LQ precoding approach combined with dirty paper coding (called Zero-Forcing Dirty Paper Coding in [10]) achieves rates close to (sum-rate) capacity. Using the forward channel knowledge each transmitter can estimate the ratio of useful signal power to noise and interference power (SINR) at the respective receiver. Based on this knowledge it selects a specific modulation and coding scheme (MCS) used for this transmission. Table I gives an overview over all possible schemes (using a convolutional code). We further use a partial frequency reuse scheme (PFR) as presented in [12] which is adapted to relay enhanced cells (REC) to improve the tradeoff between the reduced interference from neighboring cells and the resource reuse in each cell. Consider Fig. 1 which illustrates the basic resource division: The total bandwidth B is divided in an edge band
cell 2
↓
TABLE I M ODULATION AND CODING SCHEMES USED IN THE ANALYZED SYSTEM .
edge band 1
cell 1
↓
Code rate 1/3 2/3 2/3 2/3 2/3 8/9 8/9
450 m
Modulation BPSK BPSK QPSK 16-QAM 64-QAM 64-QAM 256-QAM
350 m
MCS number 1 2 3 4 5 6 7
b
1000 m Fig. 2. Detail of the system showing two sites with 12 fixed relay nodes each. At every site 3 base stations with sectorized antennas are used. The use of the different frequency bands at the cell center and the cell edge is indicated.
with frequency reuse freuse = 1/3 (the degree how resources are reused, i. e., one resource group is used in freuse of the overall area) and the remaining resources are used as center band in each cell (frequency reuse 1). User terminals are assigned to either edge or center band by estimating their SINR which is assumed to be perfectly known at the respective transmitters. If the SINR of specific user terminals is below the threshold level γedge = 3 dB, it is served using edge band resources and otherwise using center band resources. This threshold was found using a numerical analysis and reflects the SINR thresholds of the used MCS schemes, hence using other MCS schemes will affect the choice of γedge . Twelve fixed relay nodes are deployed in two tiers around each site (four per cell) as it is illustrated in Fig. 2. The six relay nodes of the inner tier are deployed at radius r1 = 350 m, the outer relays at r2 = 450 m and both tiers are rotated such that two relays are not in line with the assigned base station. Note that the deployment is likely to be more irregular in a real environment and we limit the number of relays to four per cell with regard to financial aspects. We use for our analysis a snapshot based simulation approach where users are independently placed for each snapshot and no mobility is considered. For a specific snapshot the throughput of a user terminal at position (x, y) served on subcarrier c in time slot k is defined by θ(c, k, x, y). After assigning a set of subcarriers Cp to the a user pPat (xp , yp ), we compute its throughput as θ(k, xp , yp ) = c∈Cp θ(c, k, xp , yp ). For the performance evaluation in Section IV we use as mean of comparison the expected throughput at a particular location which is defined by θ(xp , yp ) = Ek (θ(k, xp , yp )) where E (·) denotes the expectation operator.
UT
BS
(a) Conventional System (Protocol A) feederlink
BS
userlink
RN
UT
(b) Single-path relaying (Protocol B)
RN1
UT1
RN2
UT2
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(c) Single-path relaying using alternatingly transmitting relays (Protocol C)
BS
RN
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(d) Cooperative relaying (Protocol D) Fig. 3. Different forms of serving user terminals in relay enhanced systems
III. R ADIO R ESOURCE M ANAGEMENT In our system each user terminal is served either directly by its assigned base station (see Fig. 3(a)), by a relay node (see Fig. 3(b) and 3(c)) or cooperatively by both (see Fig. 3(d)). In the following, these protocols, the node assignment and resource management are described in more detail. Our aim is to provide homogeneous data rates over the entire cell. A. Considered Relaying Protocols The first protocol B illustrated in Fig. 3(b) regards singlepath relaying where the BS sends the user data to the assigned relay node which forwards the data in the consecutive frame; the user terminal then only uses the forwarded data and ignores the first phase transmission by its assigned base station. Beside its simple protocol structure, single-path relaying has the advantage that resources can be independently scheduled by each relay node. Each relay node can independently manage its own micro cell where in each micro cell the same frequency resources are used. This parallel usage of resources has the potential to overcome the bottleneck due to the usage of in-band feederlink resources and the orthogonality constraint. To reduce the interrelay interference when using relay micro-cells we divide all relay nodes in two groups which are alternatingly either receive data from the base station or serve user terminals. The idea of alternatingly transmitting relay nodes as illustrated by protocol C in Fig. 3(c) was already investigated in [13] for single communication pairs. In one frame only one of both relay groups is transmitting while the other group is receiving data from the base station. One special case of this protocol comes up if a user is located in between two relays of different groups. In this case the relays serve this user alternatingly as analyzed in [14] for a single communication pair.
The last protocol D shown in Fig. 3(d) regards cooperative transmission of the base station and relay node assigned to a user terminal. In the first phase the base station and relay node exchange channel state information and the base station additionally transmits the user data to the relay. Then both nodes perform a distributed LQ precoding as suggested in [15] where the transmit antennas build a virtual antenna array and the compound channel matrix is used for the precoding (as described in Section II). In our analysis only the user data overhead is considered which is reasonable due to the sufficiently high coherence bandwidth and time. Furthermore, at maximum four users are cooperatively served which assures that the BS can solely serve them in case of transmission errors on the feederlink. A joint relay transmission is also possible but not considered in our system due to significantly higher control overhead as well as the coordination complexity. B. Protocol Assignment The previously presented protocols are assigned to each user solely on its experienced SINR from each radio access point which allows for a low signaling overhead. Let γ(x, y, r(x,y) ) be the SINR at (x, y) for radio access point r(x,y) ∈ R(x,y) where R(x,y) is the set of all radio access points accessible from position (x, y). Based on these SINR estimations, the user is assigned to a base station and relay node with the highest SINR value. If the SINR towards the assigned base station is higher than towards the assigned relay node, the user is served using protocol A. In case the difference between both SINR values is lower than γcoop = 20 dB the user is served using protocol D. It reflects the fact that a cooperative transmission is only beneficial if the SINR difference is relatively low. Protocol C is used if the SINR towards the relay node is at least γmicro = 13 dB higher than towards the assigned base station. Finally, protocol B is used if the user is neither served cooperatively nor satisfies the condition to be served in a relay micro cell. The chosen threshold values result from a numerical analysis and showed the best performance for the chosen setup. Depending on the actual SINR values protocols A, B and D can be scheduled both in edge and center band whereas protocol C is obviously only used within the center band. C. Radio Resource Management The radio resource management consists of three parts: at first the protocol is chosen as previously explained, then the resources for each protocol group are scheduled according to the user distribution and selected protocols, and finally resources for each user within one protocol group are assigned. To ensure a sufficiently homogeneous service quality it is important to consider previous user statistics. Hence, for each protocol p ∈ P = {A, B, C, D} the expected throughput θp is calculated based on the forward channel state information P and according to θp = (x,y)∈Ap θ(x,y)/|Ap |, where Ap is the set of all user locations (x, y) where protocol p is used.
bp,k in percent
100
rs
rs rs rs rs
rs
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radio access point can be done independently which reduces
rs rs rs rs rs rs sr srthe signaling overhead, improves the radio resource managesr sr sr sr sr rs ment flexibility and allows for a distributed ARQ protocol.
75
Nevertheless, for protocol D the individual user scheduling must be coordinated between the base station and relay node. We assume a fully loaded system and no Quality of Service considerations. Each user is therefore scheduled an equal part of the resources assigned to the corresponding radio access point. Of course more advanced scheduling approaches like frequency adaptive scheduling can be applied.
bB,k rs t u
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bA,k
25 t u
0
0
t tu u tu tu
t u
t u
tt tu ut tu u ut ut ut t tu tu tu tu uu tu
10 k
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Fig. 4. The plot shows the distribution of the available bandwidth B between the two used protocols for a setup where direct transmissions and single-path conventional relaying is used.
Let bp,k be the portion of the overall bandwidth B which is used by protocol p in superframes [(k −1)·K +1; k ·K] where K is the update frequency of the bandwidth assignment. The initial value bp,1 is chosen based on the estimation of SINR. If the SINRs are not fully known, the bandwidth is equally assigned to the protocols. Every K superframes, the mean throughput θp for each protocol is updated and the bandwidth assignment is adapted accordingly such that a protocol with a high user throughput gets less resources in the following K superframes and a protocol with a low throughput gets more resources. Consider the relative throughput portion of a protocol p P of the overall throughput, i. e., θp / p′ ∈P θp′ . Using this measure we can calculate the relative difference dp from this reference value for each protocol. If all protocols achieve exactly the same throughput, dp equals zero for each protocol. Otherwise the amount of bandwidth for each protocol is changed recursively. Is bp,k the currently assigned portion of the overall bandwidth, then bp,k+1 = bp,k − dp will be used in the following period, which leads to more available resources for the users experiencing the lowest data rates. Additionally a limitation is done to assure bp ∈ [0; 1]. While we assume enough data available at the BSs at all times, prior to userlink transmissions from RNs required data always has to be transferred to the RN first. This feederlink scheduling also bases on the forward channel information, i. e., for each relay the expected throughput on the feederlink is calculated. Taking the expected user throughput for each relay node as the sum over all assigned user throughput values we get the necessary amount of feederlink resources. The corresponding bandwidth is reserved and taken from the bandwidth bp,k assigned to each protocol. We reduce the scheduled feederlink resources if a certain user data threshold is exceeded to reduce delay and to improve the data throughput towards the user terminal. Except for protocol D the user scheduling at each individual
IV. N UMERICAL RESULTS
We finally present in this section numerical results for the previously described protocols using the system setup of the typical urban scenario described in Section II. The results of three different setups are compared: 1) a conventional system where only protocol A is used, 2) a single-path relaying system where protocols A and B are used, and 3) a combined cooperative relaying system where all four protocols are used. To ensure a sufficiently realistic modeling we consider the performance obtained for one site surrounded by six interfering sites. First of all, Fig. 4 shows the development of the partial bandwidth assignment for the single-path relaying system and protocols A and B over the number of transmitted superframes. To show its ability of fast convergence we assume that in this example bA,1 = bB,1 = 0.5, i. e., equal resource assignment to both protocols. Even with this suboptimal initialization of the resource assignment the algorithm converges at about k = 15 which implies that after this point in time the average throughput of users for each protocol normalized by their portion of the overall traffic is equal. Hence, when applying a fairness scheduling approach for each individual protocol we achieve homogeneous data rates. The three steps of resource assignment – protocol selection, protocol resource scheduling and then assigning to each user the necessary resources – simplify the overall resource scheduling as each protocol uses standard user scheduling/resource assignment algorithms which were well investigated in the past. Our measures to compare the three system setups are • the minimal expected throughput value achieved by at least 95% of all users given by the 5%-ile θ5% and defined by Pr {θ(·, ·) ≤ θ5% } = 0.05, • the average throughput θ = Ex,y [θ(x, y)], • the standard deviation of the throughput defined as r h 2 i σθ = Ex,y θ(x, y) − θ .
The 5%-ile is of particular interest, as it has the advantage in comparison to the average user throughput that high data rates of a small group of users do not outweigh the poor performance experienced by a large group of users. Now consider Fig. 5 which shows the cumulative distribution function (CDF) Pr {θ(·, ·) ≤ θ} of the expected user
100
Pr {θ(·, ·) ≤ θ}
Conventional system
rs
sr t u c b
rsb u t c
c u t rsb
Single-path relaying rs
-1
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Combined cooperative relaying 10-2 10-1
Fig. 5.
rs rs
t u
bc
t u bc
t u
100 Throughput θ in MBit/s t u
101
bc
rs
t for the throughput in our considered system. u Logarithmic CDF
Name
rs
Conventional system Single-path relaying rs rs cooperative relaying Combined
t u
t u
θ5%
θ
σθ
0.84 1.29 2.44
8.68 6.87 8.02
7.72 5.58 5.72
TABLE II
N UMERICAL RESULTS FOR LINK THROUGHPUT. A LL VALUES ARE GIVEN IN
t u
t u
concerning the attained throughput, as each protocol can be used where it is most applicable. V. C ONCLUSIONS AND FURTHER WORK
t u
rs
t rs b c c u u rsb c t u t rsb
MB IT / S .
throughput θ(·, ·), i. e., the probability that a user has a throughput at least as large as θ. Furthermore, Table II shows tthe u u t t for all three systems and the previously mentioned u results evaluation measures. Our numerical evaluation shows that both relaying setups significantly improve the 5%-ile and therefore provide a more homogeneous data rate support. This is also reflected by the lower standard deviation achieved by both protocols. The main reason for this performance improvement are the improved channel conditions through the usage of relay nodes as well as the improved frequency reuse through the usage of relay micro-cells. In comparison to the 5%-ile, the average throughput is lower for the relay system which is mainly due to the applied resource scheduling approach for each individual protocol guaranteeing a minimum number of resources for each user. The application of cooperative relaying further improves the performance compared to single-path relaying. Consider a user located half-way between base station and relay node. Such a user could be served by both nodes individually, but using a cooperative relaying we are able to reduce intra-cell interference, to gain on large scale spatial diversity as well as to profit from a antenna gain. This is reflected by the significantly improved 5%-ile and average user throughput (and hence cell throughput) provided by cooperative relaying. It can be seen that using different relaying protocols and the proposed radio resource management lead to improvements
This work pursued a system level analysis of a multiple antenna based relaying system and focused on possible relay protocols and their effects on the radio resource management. Using an adaptive approach for each individual protocol and then using a standard scheduler for each of them, assures a flexible radio resource management and homogeneous data rates in a relay based system as well as a facile integration of relaying into existing networks. It shows that using cooperative transmissions significantly improves the performance compared to single-path relaying as well as a conventional system. Future work will include a more detailed analysis of the cooperative transmissions and an evaluation of the cost-benefit-tradeoff implied by the additional costs for fixed relay nodes and the improved performance as well as reduced infrastructure costs since less base stations are necessary to achieve the same system performance. R EFERENCES [1] E. van der Meulen, “Transmission of information in a t-terminal discrete memoryless channel,” Dept. of Statistics, Univ. of California, Berkeley (CA), Tech. Rep., 1968. [2] T. Cover and A. E. Gamal, “Capacity theorems for the relay channel,” IEEE Transactions on Information Theory, vol. 25, no. 5, pp. 572–584, September 1979. [3] J. Laneman, D. Tse, and G. Wornell, “Cooperative diversity in wireless networks: Efficient protocols and outage behavior,” IEEE Transactions on Information Theory, vol. 50, no. 12, pp. 3062–3080, December 2004. [4] A. Host-Madsen, “On the capacity of wireless relaying,” in IEEE Vehicular Technology Conference (VTC), vol. 3, Vancouver (BC), Canada, September 2002, pp. 1333–1337. [5] I. Hammerstr¨om, M. Kuhn, and A. Wittneben, “Distributed MIMO for cellular networks with multihop transmission protocols,” in Asilomar Conference on Signals, Systems, and Computers 2006, Pacific Grove, CA,, 2006. [6] Y. Fan and J. Thompson, “MIMO configurations for relay channels: Theory and practice,” IEEE Transactions on Wireless Communications, vol. vol. 6, pp. pp. 1774–1786, 2007. [7] WINNER, “Ist-winner,” http://www.ist-winner.org. [8] IST-4-027756 WINNER II, “D1.1.1 WINNER II Interim channel models,” November 2006. [9] M. Costa, “Writing on dirty paper,” IEEE Transactions on Information Theory, vol. IT-29, no. 3, pp. 439–441, May 1983. [10] G. Caire and S. S. (Shitz), “On the achievable throughput of a multiantenna gaussian broadcast channel,” IEEE Transactions on Information Theory, vol. 49, no. 7, pp. 1691–1706, July 2003. [11] W. Yu and T. Lan, “Transmitter optimization for the multi-antenna downlink with per-antenna power constraints,” IEEE Transactions on Signal Processing, vol. 55, no. 6, pp. 2646–2660, June 2007. [12] Y. Xiang, J. Luo, and C. Hartmann, “Inter-cell interference mitigation through flexible resource reuse in ofdma based communication networks,” in 13th European Wireless Conference EW2007, 2007. [13] T. Oechtering and H. Boche, “Capacity of a gaussian FIR linear relay network,” in International Conference on Wireless Networks, Communications and Mobile Computing, Maui (Hawaii), USA, June 2005. [14] P. Rost and G. Fettweis, Cognitive Wireless Networks: Concepts, Methodologies and Visions. Springer, 2007, ch. Scalable Cooperation in Multi-Terminal Half-Duplex Relay Networks, pp. 179–197. [15] K. Karakayali, G. Foschini, R. Valenzuela, and R. Yates, “On the maximum common rate achievable in a coordinated network,” in IEEE International Conference on Communications, vol. 9, Istanbul, Turkey, June 2006, pp. 4333–4338.
