Shreeram Sigdel

Simplified Fair Scheduling and Antenna Selection Algorithms for Multiuser MIMO Orthogonal Space-Division Multiplexing Downlink

We consider the downlink of a multiuser multiple-input multiple-output (MIMO) system, where the base station and the mobile receivers are equipped with multiple antennas. We propose simplified algorithms for channel-aware multiuser scheduling in conjunction with receive antenna selection for two downlink multiuser orthogonal space-division multiplexing techniques: block diagonalization and successive optimization. The algorithms greedily maximize the weighted sum rate. The algorithms add the best user at a time from the set of users that are not selected yet to the set of selected users until the desired number of users has been selected. To apply the proportional fairness criterion, simplified user scheduling metrics are proposed for block diagonalization and successive optimization. Two receive antenna selection algorithms are also proposed, which further enhance the power gain of the equivalent single-user channel after orthogonal precoding by selecting a subset of the receive antennas that contributes the most toward the total power gain of the channel. A user grouping technique is used to further lower the complexity of the selection algorithms. We compare various multiuser MIMO scheduling strategies that are applied to block diagonalization and successive optimization transmission techniques through simulation. Simulation results demonstrate the effectiveness of the proposed algorithms in ensuring throughput fairness among users. Results also show that when the number of users is large, the proposed scheduling algorithms perform close to the exhaustive search algorithms and previously proposed greedy scheduling algorithms, but with much lower complexity.

Fair Scheduling and Antenna Selection Algorithms for Multiuser MIMO Orthogonal Space Division Multiplexing Downlink

We consider the downlink of a multiuser MIMO system where base station as well as the mobiles are equipped with multiple antennas. We propose simplified algorithms for channel aware multiuser scheduling in conjunction with receive antenna selection (RAS) for two downlink multiuser orthogonal space- division multiplexing techniques: Block diagonalization (BD) and successive optimization (SO). The algorithms greedily maximize the weighted sum rate. To apply the proportional fairness criterion, simplified user scheduling metrics have been proposed for both BD and SO. Two RAS algorithms are proposed, which further enhance the power gain of the equivalent single user channel after orthogonal precoding by selecting a subset of the receive antennas that contributes the most towards the total power gain of the channel. A user grouping technique is used to further lower the complexity of the selection algorithms. We compare various multiuser MIMO scheduling strategies applied to BD and SO transmission techniques through simulation. Simulation results demonstrate the effectiveness of the proposed algorithms in delivering long term fairness in data rates among users. Results also show that the proposed scheduling algorithms perform close to the exhaustive search based algorithms and previously proposed lower complexity scheduling algorithms with much less complexity for a large number of users.

Antenna and User Subset Selection in Downlink Multiuser Orthogonal Space-Division Multiplexing

Block diagonalization (BD) and successive optimization (SO) are two suboptimal but more practical (compared to dirty paper coding (DPC)) orthogonal linear precoding techniques for the downlink of multiuser MIMO systems. Since the numbers of users supported by BD or SO for a given number of transmit antennas are limited, BD or SO should be combined with scheduling so that a subset of users is selected at a given time slot while meeting the dimensionality requirements of these techniques. On the other hand, receive antenna selection (RAS) is a promising hardware complexity reduction technique. In this paper, we consider user scheduling in conjunction with receive antenna selection. Since exhaustive search is computationally prohibitive, we propose simplified and suboptimal user scheduling algorithms for both BD and SO. For BD, we propose capacity and Frobenius-norm based suboptimal algorithms with the objective of sum rate maximization. Starting from an empty set, each step of proposed algorithms adds the best user from the set of users not selected yet until the desired number of users have been selected. Proposed receive antenna selection works in conjunction with user scheduling to further enhance the sum rate of BD. For SO, a Frobenius-norm based low complexity algorithm is proposed, which maximizes the ratio of the squared Frobenius norm of the equivalent channel (projected to the joint null space of the previously selected users) to the sum of the squared Frobenius norms of the previously selected users’ preprocessed channels. Simulation results demonstrate that the proposed algorithms achieve sum rates close to exhaustive search algorithms with much reduced complexity. We also show that in addition to reduced hardware complexity at the receiver, antenna selection enhances multiuser diversity gain that is achieved with user scheduling.

Genetic and Greedy User Scheduling for Multiuser MIMO Systems with Successive Zero-Forcing

In this paper we consider efficient and low complex- ity scheduling algorithms for multiuser multiple-input multiple- output (MIMO) systems. The optimal user scheduling involves an exhaustive search, which becomes very complex for realistic num- bers of users and transmit antennas. Among various suboptimal but low complexity algorithms, greedy algorithms with heuristic scheduling metrics have been shown to achieve performance close to an exhaustive search. Meanwhile, genetic algorithms (GAs) are a rapid, though suboptimal, option of performing a utility (in this case scheduling) metric optimization. In this paper, we propose and analyze the performance and complexity of greedy and genetic scheduling algorithms for multiuser MIMO systems with successive zero-forcing precoding. We demonstrate that at lower K, the genetic algorithm performs better than the greedy algorithm, where K denotes the total number of users requesting service. For large K, however, the greedy algorithm outperforms the genetic algorithm. The greedy algorithm also achieves similar sum-rate growth (with K) as the exhaustive search. A detailed complexity analysis shows that the order of complexity of the genetic algorithm is higher than that of the greedy algorithm by a factor equal to K 2 ,w hereK0 denotes the maximum number

User Scheduling for Network MIMO Systems with Successive Zero-Forcing Precoding

In this paper we consider simplified greedy user scheduling algorithms for clustered network multiuser multiple-input multiple-output (MIMO) systems with successive zero-forcing (SZF) precoding. The optimal user scheduling involves an exhaustive search, which is very complex. Among various suboptimal but lower complexity algorithms, greedy algorithms with heuristic scheduling metrics have been shown to achieve performance close to the exhaustive search. In this paper, we propose two simplified algorithms: interference-aware greedy user scheduling (IA-GUS) and interference-whitening greedy user scheduling (IW-GUS). IA-GUS schedules the users based on the information on interference power of the selected users in surrounding cooperating clusters, and IW-GUS schedules the users based on the information on whitened channels of all users requesting the service in the cluster. IA-GUS has lower complexity and performs better than IW- GUS. Simplified metrics for proportionally fair (PF) scheduling are also proposed. Performance of the proposed algorithms is analyzed and compared with dirty paper coding (DPC), block diagonalization (BD) and systems without network coordination. Simulation results demonstrate that the proposed algorithms for SZF achieve much higher outage capacity compared to BD with previously proposed algorithms.

Greedy and Genetic User Scheduling Algorithms for Multiuser MIMO Systems with Block Diagonalization

In this paper, we consider efficient and low complex- ity scheduling algorithms for multiuser multiple-input multiple- output (MIMO) systems. Due to the dimensionality constraint imposed by linear precoding techniques like block diagonalization (BD), user scheduling is required. Optimal user scheduling involves an exhaustive search, which becomes very complex for realistic numbers of users and transmit antennas. Hence, various suboptimal but low complexity algorithms have been considered in the literature. Among them, greedy algorithms with heuristic scheduling metrics have been shown to achieve performance close to an exhaustive search. Meanwhile, genetic algorithms (GAs) are a rapid, though suboptimal, option of performing a utility (i.e. scheduling) metric optimization. In this paper, we propose and analyze the performance and complexity of greedy and genetic scheduling algorithms for multiuser MIMO systems with BD precoding. We demonstrate that except at low SNR with a smaller number of transmit antennas, the genetic algorithm outperforms the greedy algorithm. A detailed complexity analysis shows that the order of complexity of the genetic algorithm is higher than that of the greedy algorithm by a factor equal to K0 ,w here K0 denotes the maximum number of simultaneously supported multiple-antenna users.

Efficient User Selection and Ordering Algorithms for Successive Zero-Forcing Precoding for Multiuser MIMO Downlink

In this paper we consider user scheduling problem for linearly preceded multiuser multiple-input multiple-output (MIMO) downlink, where base station as well as the mobile receivers are equipped with multiple antennas. Optimal precoding involves dirty paper coding (DPC) technique, and it is highly nonlinear and complex. On the other hand, complete inter-user interference cancellation using linear zero-forcing or block diagonalization precoding are suboptimal. Hence, we consider successive zero-forcing precoding, which achieves improved system throughput compared to block diagonalization by allowing users to work under limited interference. Due to the dimensionality constraint of linear precoding techniques user scheduling is required. The optimal user scheduling involves exhaustive search, which becomes very complex for realistic numbers of users and transmit antennas. In addition, for successive zero-forcing precoding the order in which users are precoded successively is important for sum rate maximization, which further increases the complexity of exhaustive search. In this paper we develop a low complexity greedy user scheduling algorithm for successive zero-forcing precoding, which incorporates various user ordering techniques. Simplified heuristic scheduling metrics are proposed, which are shown to perform close to the exhaustive search method. A suboptimal user ordering technique that is similar to the order, in which the proposed greedy user selection selects users, is proposed. Further simplification of regular greedy scheduling algorithm is obtained with the proposed intermediate user grouping technique. The proposed algorithm is of low complexity, but provides performance close to the highly complex exhaustive search algorithm.

Simplified Antenna Selection and User Scheduling for Orthogonal Space-Division Multiplexing

In this paper, we study simplified algorithms for user scheduling in conjunction with receive antenna selection (RAS) for two downlink multiuser orthogonal space-division multiplexing techniques: block diagonalization (BD) and successive optimization (SO). Proposed algorithms select user and antenna subsets that maximize total power gain of the equivalent channel through mutual null-space projections of the active user channels. With this approach, algorithms add the best user at a time from the set of users not selected yet to the set of selected users until the desired number of users have been selected. To reduce the complexity further, users whose channel correlation to the signal subspace of the previously selected users is under some specified threshold are grouped at each step of the algorithm such that next user is selected from that group. This avoids the search through all K - i remaining users at the ith step of the algorithm. Two RAS algorithms are proposed, which further enhance the power gain of the equivalent channel by selecting a subset that contributes most towards the sum rate. For SO, user selection accounts for the interference of the previously selected users and maximizes the ratio of the total power of the equivalent channel to the approximate interference power on the desired signal subspace. Proposed algorithms perform close to the previously proposed algorithms with much less complexity.

Greedy and progressive user scheduling for CoMP wireless networks

In this work, we propose progressive user scheduling (PUS) and distributed successive zero-forcing (SZF) precoding for clustered coordinated multi-point transmission/reception (CoMP) systems. With joint transmission (JT) across a cluster of cells, centralized processing of precoding and user scheduling requires significant bandwidth resources for information exchange among coordinated base stations (BTSs) and the central controller, and channel state information (CSI) feedback from all users in the cluster to the central controller. In this work, we propose a greedy progressive user scheduling (PUS) and distributed SZF precoding to reduce CSI feedback and information exchange. With PUS, each BTS schedules and precodes its users in the coverage area of its cell separately from other BTSs in a cluster, but takes limited amount of feedback information available from other BTSs into account for user scheduling. Once the users are scheduled in that cell, BTS passes precoder information of the scheduled users to the next BTS. The second BTS schedules users in its cell taking the information from the first BTS into account. This process continues until the last BTS in the cluster schedules its users. With PUS, users in a cell do not completely estimate CSI from other cells in the cluster, which may significantly reduce the CSI feedback. Also, central controller is not necessarily required, but it may be utilized for limited amount of control signalling, e.g. an order in which the BTSs should schedule their users. Due to its distributed implementation, the proposed technique enables the use of various existing single-cell multiuser MIMO techniques that have been already standardized or proposed in the literature. In this work, we investigate the performance of greedy PUS and compare it with the greedy user selection for JT. It is shown that PUS can achieve sum rate close to the JT technique.

Simplified transmit covariance optimization and user ordering algorithm for successive zero-forcing precoding

In this paper we consider transmit covariance matrix optimization and user ordering problem for successive zero-forcing (SZF) precoding for multiuser multiple-input multiple-output (MIMO) downlink, where base station as well as the mobile receivers are equipped with multiple antennas. With SZF, an optimization of sum rate maximizing transmit covariance matrices is necessary. A dirty paper coding (DPC) based transmit covariance matrix optimization algorithm has been recently proposed in [1]. The algorithm involves three steps: the dual multiple access channel (MAC) covariance optimization using sum power iterative water-filling [2], the MAC to broadcast channel (BC) covariance transformation [3] for DPC, and transformation of the DPC downlink covariance matrices to SZF matrices. Hence, that algorithm is computationally very complex, and may not be realizable in practice. In this paper, we propose a suboptimal but much simplified algorithm, which employs an iterative procedure similar to the MAC covariance optimization technique of [2], but does not involve multiple levels of covariance matrix transformations. Additionally, the optimized algorithm requires the optimization to be applied to all possible user orders. Hence, we propose a heuristic user ordering algorithm based on previously proposed user selection algorithms, so that the exhaustive search through all possible user orders is avoided without significant performance penalty. Simulation results show that proposed algorithm performs very close to the algorithm of [1] in low SNR regime.