- Research Article
- Open Access
A Comparison of Scheduling Strategies for MIMO Broadcast Channel with Limited Feedback on OFDM Systems
© Ermanna Conte et al. 2010
- Received: 14 October 2009
- Accepted: 22 March 2010
- Published: 4 May 2010
We consider a multiuser downlink transmission from a base station with multiple antennas (MIMO) to mobile terminals (users) with a single antenna, using orthogonal frequency division multiplexing (OFDM). Channel conditions are reported by a feedback from users with limited rate, and the base station schedules transmissions and beamforms signals to users. We show that an important set of schedulers using a general utility function can be reduced to a scheduler maximizing the weighted sum rate of the system. For this case we then focus on scheduling methods with many users and OFDM subcarriers. Various scheduling strategies are compared in terms of achieved throughput and computational complexity and a good tradeoff is identified in greedy and semiorthogonal user selection algorithms. In the greedy selection algorithm, users are selected one by one as long as the throughput increases, while in the semiorthogonal approach users are selected based on the channel correlation. An extension of these approaches from a flat-fading channel to OFDM is considered and simplifications that may be useful for a large number of subcarriers are presented. Results are reported for a typical cellular transmission of the long-term evolution (LTE) of 3GPP.
- Orthogonal Frequency Division Multiplex
- Channel State Information
- Orthogonal Frequency Division Multiplex System
- Orthogonal Frequency Division Multiplex Symbol
- Resource Block
Next generation wireless cellular systems are expected to support high-quality multimedia services; this motivates the interest in multiantenna (MIMO) systems, where both spatial diversity and multiplexing can be used to increase the achievable throughput. In fact, it has been shown that the downlink capacity of a MIMO system with perfect channel state information (CSI) scales as a linear function of the number of transmit antennas . Although nonlinear dirty paper coding scheme achieves the system capacity, it has a high computational cost , and simpler solutions have been investigated. Linear beamforming has been shown  to achieve a large part of dirty paper coding capacity; in particular, zero forcing beamforming matched to an opportunistic scheduling is widely used .
However, benefits of MIMO are obtained only by a proper scheduling of transmissions, which opportunistically exploits channel conditions in order to increase throughput, while ensuring quality of service (QoS). Several scheduling techniques have been proposed for MIMO single carrier systems on flat fading channels based on various approaches, including clique search , maximization of the Frobenius norm of the composite channel matrix [5, 6], user channel orthogonality [7–9], single bit feedback , waterfilling , tree search , evolutionary algorithms , and greedy scheduling  extended to the case of limited feedback in . In some cases, joint optimization of scheduling and power allocation is performed [4–6, 10, 11, 13], while in other cases only scheduling is considered [7–9, 14]. Moreover, QoS-oriented multiuser scheduling and beamforming have been investigated in , in order to conciliate the request of high throughput with low packet delays. An overview of research on cross-layer scheduling for multiuser MIMO single-carrier systems is given in . A similar problem to multiuser MIMO scheduling can be found in other transmission systems, such as multicarrier code- or frequency-division multiple access .
In frequency selective channels, single carrier modulation is often replaced by orthogonal frequency division multiplexing (OFDM) due to its efficiency in overcoming multipath fading. In fact, the combination of MIMO and OFDM seems to be the technology of future wireless cellular systems, as it has been proposed for downlink in the long term evolution (LTE) release of 3GPP standard [19, 20]. When MIMO OFDM is considered, scheduling becomes more complex, as the number of resources to be allocated, that is, the number of subcarriers, increases and only suboptimal approaches are viable . Complexity is further increased in a frequency division duplexing system, where CSI is provided to the base station by each user (mobile terminal) through a feedback channel. In fact, due to the limited feedback rate, only a partial CSI is available at the base station and additional processing is required to compensate the channel uncertainty. Some of the scheduling techniques considered for single carrier transmissions can be extended to OFDM. For example, in  a scheduling algorithm has been proposed for MIMO OFDM systems which extends method  for single-carrier systems: the set of scheduled users on each subcarrier is built in a greedy fashion, by adding one user at a time with the aim of maximizing a weighted sum rate (WSR). In  this approach has been further simplified to avoid the need of computing a new beamforming matrix upon the insertion of a new candidate in the set of scheduled users. A further simplification of the scheduling is achieved by computing an estimate on their signal to interference ratio which is then used to exclude users that would not contribute to the WSR, by introducing a threshold to their signal to noise plus interference ratio.
In this paper, we first show that any scheduler maximizing a wide class of utility functions can be reduced to a scheduler maximizing the weighted sum rate, where the weights are suitably chosen according to the utility function. Then we revise scheduling techniques proposed in the literature that maximize the weighted sum rate for a multiuser MIMO OFDM system with limited feedback and compare them in a LTE 3GPP scenario in terms of (i) computational complexity, (ii) memory requirements, and (iii) achievable throughput.
The rest of the paper is organized as follows. In Section 2 we describe the downlink MIMO OFDM system model. In Section 3 a general scheduling method is derived and algorithms [14, 21] are revised. Sections 4 and 5 present, respectively, the user selection and user preselection strategies of . In Section 6 the complexity of the various strategies is investigated. In Section 7 simulation results are illustrated and Section 8 outlines main conclusions.
Bold upper and lower letters denote matrices and vectors, respectively; denotes Hermitian operation (transpose complex conjugate), while denotes transpose; is the vector norm, and stands for expectation.
where the expectation is taken only with respect to , and is the th entry of .
2.1. Feedback Information
with suitable weights that take into account fairness and QoS constraints.
2.2. Exhaustive Search Scheduling
At each slot, we aim at scheduling the set of streams that maximizes WSR.
This problem can be solved by considering all possible sets and evaluating the WSR achieved by each candidate set. Unfortunately, this exhaustive search (ES) scheduling has a high computational cost, which becomes infeasible for an increasing number of users and subcarriers. Simpler and suboptimal scheduling methods are investigated in Section 4.
In order to balance the opportunistic use of channel resources with fairness among users, we consider a multiuser scheduler. We first consider in this section general criteria for the choice of weights of the WSR and we derive the optimum maximum utility scheduler weights for a general utility function. Then we specialize the result for the maximum sum rate scheduler and the proportional fair scheduler.
3.1. General Multiuser Scheduling
where is a fairness parameter to be chosen according to the desired scheduling policy. For example, for we obtain the proportional fair scheduler (PFS). For we obtain the utility function of the maximum sum rate scheduler. When , (13) becomes the utility function of the max-min scheduler.
where is the set of all possible streams. Note that for , (16) boils down to the maximum utility scheduler of .
3.2. Maximum Sum Rate Scheduling
3.3. Proportional Fair Scheduling
The multiuser multicarrier proportional fair scheduling (MMPFS) algorithm  is an extension to the OFDM multiuser scenario of the PFS algorithm.
and MPFS (18) coincides with the maximization of the WSR (15) with weights (16), and .
Now that we have established that maximizing the weighted sum rate is equivalent to the maximization of a wide set of utility functions, we focus on methods that allow to achieve this goal. In the following we investigate suboptimal solutions to problem (15) for a small number of users , when the probability of having a fully loaded system is small. In fact, in this scenario power distribution has an important role in selecting the optimal user set. In Section 4.3 we will consider the case of a high number of users , and in this case a simplification of scheduling is possible. For ease of notation we drop both slot ( ) and OFDM symbol ( ) index in the remaining of the paper.
4.1. Multicarrier Greedy (MG)
In , a greedy scheduling algorithm in a single-carrier flat-fading system has been proposed, where users are selected one by one as long as the throughput increases and it has been then extended to an OFDM system in  and denoted here multicarrier greedy (MG).
where is given by (6) while is the th column of the beamforming matrix for users scheduled at step . Note that total power has been divided by in order to obtain the per stream power .
4.2. Projection-Based Greedy (PBG)
the power is redistributed among all streams;
beamforming of streams already scheduled on the same resource block is modified.
By using (26) and (28), there is no need to determine a new beamformer in correspondence of each candidate stream; instead, only the basis needs to be updated at each step, and this requires only few vector multiplications. Note that the computation of is based on the projection of the candidate vector on the basis, as from the acronym PBG. Once all streams have been scheduled, a beamformer is computed to perform transmission.
4.3. Greedy Scheduling Strategies in the High Scenario
Scheduling can then be simplified by operating independently on each resource block.
4.4. Multicarrier Semiorthogonal User Selection Algorithm (MSUS)
where is a design parameter that sets the maximum correlation allowed between the quantized channel vectors of the selected users. We note that in MSUS we apply single carrier SUS in parallel, one for each resource block. Also in this case the number of steps is random as the algorithm ends when set is empty. Once users have been scheduled, the total power is equally distributed among the scheduled streams according to (4).
From (23) we obtain that this condition is satisfied only if the SNIR is high enough to compensate for losses incurred by the insertion of a new scheduled stream, that is, the power redistribution and the beamforming modification, as described by conditions (1) and (2) of Section 4.2. This observation suggests a further simplification of the PBG algorithm, by a-priori excluding the streams whose SNIR is below a certain threshold. Indeed, as for each candidate stream the SNIR (26) must be evaluated, by excluding streams that could never be inserted, the scheduling procedure can be fastened .
Note that the idea of preselecting users has been first introduced in , by letting users feeding back their CSI and rate request only if the quality of their channel is above a threshold. On the other hand, we use preselection as a technique to simplify scheduling rather than reducing the feedback rate. Moreover, in our case the preselection is not based only on the channel quality but also on the correlation with other users' channels.
5.1. Preselection PBG (PPBG)
Then by considering only streams satisfying (35), we decrease the number of comparisons and SINR updates at each step of PBG. In the high scenario the preselection technique is not feasible; in fact, as illustrated in Appendix , for , and therefore (35) is verified by all streams.
We further note that is an increasing function of ; hence, streams whose CQI is below the threshold at step can be neglected also in the next steps.
5.2. Simplified Preselection PBG (S-PPBG)
Within PBG methods, we note that this approach becomes optimal when the scheduling objective coincides with the maximization of the SR. However, for the maximization of the WSR, S-PPBG is in general suboptimal.
We analyze the worst case complexity of the various approaches, in terms of both computational complexity and memory requirement.
6.1. Computational Complexity
We first observe that all considered algorithms select one stream per step, until at most streams are allocated on each resource block, thus in general . At step , streams are considered for insertion in . Furthermore, at each step, the per stream power is adapted, due to the insertion of a candidate stream in .
6.1.1. MG Complexity
where denotes the resource block of the stream selected at step . The first term in (39) accounts for the selection of the stream with maximum CQI. The remaining terms account for step through , with (a) update of SNIR estimate of the already scheduled streams, (b) computation of a new beamformer for each of the candidate streams on subcarrier , (c) evaluation of , (d) update of the SNIR estimates, and (e) evaluation of the WSR. Lastly, the algorithm determines the stream which maximizes the WSR at step and checks condition (21).
since now and no power update is necessary at each step.
6.1.2. PBG Complexity
In fact, the PBG algorithm for each candidate stream on resource block (a) performs the projection of channel vector on the orthogonal basis and (b) updates the SNIR estimate. At each step, the basis is also updated according to the channel vector of last scheduled stream. At the end, the beamforming matrix is computed according to the set of scheduled streams.
since scheduling can be performed in parallel on all resource blocks.
6.1.3. PPBG Complexity
6.1.4. S-PPBG Complexity
6.2. Asymptotic Complexity Analysis
where , and .
6.3. Memory Occupation
as PBG stores (a) the value , (b) total rate provided by each candidate stream ( CLS), and (c) orthogonal basis ( CLS).
with respect to PBG it needs to store also ( CLS as worst case).
as MSUS stores (a) correlations of candidate streams and last inserted stream ( CLS), (b) the value of ( CLS), and (c) the set of total rates of each candidate ( CLS as worst case).
We compare the scheduling algorithms in terms of average sum rate (SR) and complexity requirements. All users are uniformly distributed in a cell of radius 500 m, as in ; we consider an average of 15 dB per resource block at the cell border and path loss is included in the channel model. We assume also a realistic MIMO channel with time, frequency, and spatial correlation among the elements of . The channel is modeled as slowly time-variant, frequency selective Rayleigh fading as from the spatial channel model (SCM) . According to the LTE release, we set transmission bandwidth to 2.5 MHz, divided into resource blocks and centered at the carrier frequency of 2 GHz. The base station is equipped with antennas spaced by 10 wavelength. Scheduling and beamforming are performed once a slot, and each slot is composed of 7 adjacent OFDM symbols. CSI feedback is performed with a variable number of bits using an optimized codebook, as detailed in .
7.1. Performance Comparison
We note also in Figure 3 that preselection applied to PBG provides slightly better performance, despite the fact that it considers a lower number of candidate sets. In fact, preselection aims at excluding from scheduling streams that would not increase the WSR and prevents the scheduler from inserting them for fairness reasons.
7.2. Complexity Comparison
In the high scenario, simulations confirm the analysis; in fact, for we have , , , and . We underline that in the high regime S-PPBG complexity is higher than that of PBG because of the required power distribution; indeed simplification of preselection does not compensate the need of redistributing the total power. On the other hand, we note that the high complexity required by MG is mainly due to the evaluations of the beamformer at each step.
Memory requirements, investigated in Section 6, do not prefigure large differences between different methods; for required memory locations are 35890 for MG, 29682 for PBG, 29730 for S-PPBG, and 33841 for MSUS. Hence, the simplified techniques achieve a reduction of memory requirement with respect to existing algorithms.
This paper has provided an overview of scheduling problems for multiuser downlink MIMO OFDM systems. We first have shown that scheduling according to a wide class of utility functions can be reduced to a scheduling problem aiming at maximizing the weighted sum rate of the system, under a proper choice of the weighting function. Then we have compared scheduling algorithm having as objective the maximization of the weighted sum rate, including greedy algorithms, based on throughput maximization and algorithms based on the semiorthogonality among MIMO channels. Extensions to a OFDM scenario of algorithms originally devised for flat-fading single-carrier systems have been investigated. The comparison has been carried out both in terms of computational complexity and in terms of achievable throughput.
Several insights on the performance of the state of the art scheduling algorithms can be highlighted from the numerical results. Firstly, the MG approach achieves an average sum rate which is very close to the maximum value achieved by ES, over a wide range of cell loads. When compared against MSUS, the proposed MG technique has a gain of about 50% in terms of average sum rate in most network conditions. Moreover, MG requires a significantly lower complexity than that of ES and only 30% additional CMUXs than MSUS. Hence, we believe that MG provides a good trade-off between performance and complexity.
Lastly, limitations in the feedback rate have a severe impact on the performance of all scheduling approaches. Indeed we have seen that all schedulers yield an average sum rate that increases linearly with the number of bits used to feedback the CSI with an increase of about 1 bit/s/Hz for each additional feedback bit.
A. Proof of (15)
where indicates that user is scheduled, that is, if and otherwise.
Therefore, by inserting (A.5) into (A.4) we obtain (15).
B. Proof of (36)
where is the generic candidate stream.
The authors thank the editor and the reviewers for their comments on the manuscript.
- Tse D, Viswanath P: Fundamentals of Wireless Communication. Cambridge University Press, Cambridge, UK; 2005.View ArticleMATHGoogle Scholar
- Caire G, Shamai S: On the achievable throughput of a multiantenna Gaussian broadcast channel. IEEE Transactions on Information Theory 2003, 49(7):1691-1706. 10.1109/TIT.2003.813523MathSciNetView ArticleMATHGoogle Scholar
- Yoo T, Goldsmith A: On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming. IEEE Journal on Selected Areas in Communications 2006, 24(3):528-541.View ArticleGoogle Scholar
- Yoo T, Goldsmith A: Sum-rate optimal multi-antenna downlink beamforming strategy based on clique search. Proceedings of IEEE Global Telecommunications Conference (GLOBECOM '05), November-December 2005, St. Louis, Mo, USA 3: 1510-1514.Google Scholar
- Shen Z, Chen R, Andrews JG, Heath RW Jr., Evans BL: Low complexity user selection algorithms for multiuser MIMO systems with block diagonalization. IEEE Transactions on Signal Processing 2006, 54(9):3658-3663.View ArticleGoogle Scholar
- Wang J, Love DJ, Zoltowski MD: User selection for the MIMO broadcast channel with a fadiness constraint. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '07), April 2007, Honolulu, Hawaii, USA 3: 9-12.Google Scholar
- Viswanathan H, Kumaran K: Rate scheduling in multiple antenna downlink wireless systems. Proceedings of Allerton Conference on Communication, Control, and Computing, October 2001, Allerton, Ill, USAGoogle Scholar
- Swannack C, Uysal-Biyikoglu E, Wornell GW: Low complexity multiuser scheduling for maximizing throughput in the MIMO broadcast channel. Proceedings of Allerton Conference on Communication, Control, and Computing, October 2004, Allerton, Ill, USAGoogle Scholar
- Yoo T, Jindal N, Goldsmith A: Multi-antenna downlink channels with limited feedback and user selection. IEEE Journal on Selected Areas in Communications 2007, 25(7):1478-1491.View ArticleGoogle Scholar
- Diaz J, Simeone O, Bar-Ness Y: Sum-rate of MIMO broadcast channels with one bit feedback. Proceedings of IEEE International Symposium on Information Theory (ISIT '06), July 2006, Seattle, Wash, USA 1944-1948.Google Scholar
- Kobayashi M, Caire G: An iterative water-filling algorithm for maximum weighted sum-rate of Gaussian MIMO-BC. IEEE Journal on Selected Areas in Communications 2006, 24(8):1640-1646.View ArticleGoogle Scholar
- Fuchs M, Galdo GD, Haardt M: Low-complexity space-time-frequency scheduling for MIMO systems with SDMA. IEEE Transactions on Vehicular Technology 2007, 56(5):2775-2784.View ArticleGoogle Scholar
- Jiang M, Rubio F, Wang Y, Gomez J, Yuan D: User selection for maximum sum-rate in multi-user and MISO system with evolutionary algorithm. Proceedings of the 1st International Workshop on Cross Layer Design (IWCLD '07), September 2007, Jinan, China 74-77.Google Scholar
- Dimic G, Sidiropoulos ND: On downlink beamforming with greedy user selection: performance analysis and a simple new algorithm. IEEE Transactions on Signal Processing 2005, 53(10):3857-3868.MathSciNetView ArticleGoogle Scholar
- Trivellato M, Boccardi F, Tosato F: User selection schemes for MIMO broadcast channels with limited feedback. Proceedings of the 65th IEEE Vehicular Technology Conference (VTC '07), April 2007, Dublin, Ireland 2089-2093.Google Scholar
- Alexiou A, Reis J, Gameiro A: QoS-based multiuser scheduling in MIMO systems. Proceedings of the 16th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC '05), September 2005, Berlin, Germany 2: 837-841.Google Scholar
- Anton-Haro C, Svedman P, Bengtsson M, Alexiou A, Gameiro A: Cross-layer scheduling for multi-user MIMO systems. IEEE Communications Magazine 2006, 44(9):39-45.View ArticleGoogle Scholar
- Trifonov P, Costa E, Filippi A, Schulz E: Adaptive coding in MC-CDMA/FDMA systems with adaptive sub-band allocation. European Transactions on Telecommunications 2004, 15(3):207-214. 10.1002/ett.967View ArticleGoogle Scholar
- Overview of the 3GPP long term evolution physical layer Motorola, January 2003, http://www.freescale.com/files/wireless_comm/doc/white_paper/3GPPEVOLUTIONWP.pdf
- Ekstrom H, Furuskar A, Karlsson J, et al.: Technical solutions for the 3G long-term evolution. IEEE Communications Magazine 2006, 44(3):38-45.View ArticleGoogle Scholar
- Benvenuto N, Conte E, Tomasin S, Trivellato M: Joint low-rate feedback and channel quantization for the MIMO broadcast channel. Proceedings of Tyrrhenian International Workshop on Digital Communication, September 2007, Ischia Island, ItalyGoogle Scholar
- Conte E, Tomasin S, Benvenuto N: Scheduling strategies for multiuser MIMO OFDM systems with limited feedback. Proceedings of the 19th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC '08), September 2008, Cannes, FranceGoogle Scholar
- Hanzo L, Münster M, Chei B, Keller T: OFDM and MC-CDMA for Broadband Multi-User Communications, WLANS and Broadcasting. John Wiley & Sons, New York, NY, USA; 2003.View ArticleGoogle Scholar
- Jindal N: Finite rate feedback MIMO broadcast channels. Proceedings of Workshop on Information Theory and Its Applications (ITA '06), February 2006, San Diego, Calif, USAGoogle Scholar
- Svedman P, Wilson SK, Cimini LJ Jr., Ottersten B: Opportunistic beamforming and scheduling for OFDMA systems. IEEE Transactions on Communications 2007, 55(5):941-952.View ArticleGoogle Scholar
- Kountouris M, Gesbert D: Memory-based opportunistic multi-user beamforming. Proceedings of IEEE International Symposium on Information Theory (ISIT '05), September 2005, Adelaide, SC, USA 1426-1430.Google Scholar
- Jalali A, Padovani R, Pankaj R: Data throughput of CDMA-HDR a high efficiency-high data rate personal communication wireless system. Proceedings of the 51st Vehicular Technology Conference (VTC '00), May 2000, Tokyo, Japan 3: 1854-1858.Google Scholar
- Gesbert D, Alouini M-S: How much feedback is multi-user diversity really worth? Proceedings of IEEE International Conference on Communications, June 2004, Paris, France 1: 234-238.Google Scholar
- SCM micro cell and urban canyon model Motorola, January 2003, http://www.3gpp.org/ftp/tsg_ran/WG1_RL1/3GPP_3GPP2_SCM/ConfCall-10-20030130/
- Salo J, Galdo GD, Salmi J, et al.: Matlab implementation of the 3GPP spatial channel model. January 2005., (3GPP TR 25.996):http://www.tkk.fi/Units/Radio/scm/Google Scholar
- Benvenuto N, Conte E, Tomasin S, Trivellato M: Predictive channel quantization and beamformer design for MIMO-BC with limited feedback. Proceedings of the 50th Annual IEEE Global Telecommunications Conference (GLOBECOM '07), November 2007, Washington, DC, USA 3607-3611.Google Scholar
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