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.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we propound a solution named Cognitive Sub-Small Cell for
Sojourners (CSCS) in allusion to a broadly representative small cell scenario,
where users can be categorized into two groups: sojourners and inhabitants.
CSCS contributes to save energy, enhance the number of concurrently supportable
users and enshield inhabitants. We consider two design issues in CSCS: i)
determining the number of transmit antennas on sub-small cell APs; ii)
controlling downlink inter-sub-small cell interference. For issue i), we
excogitate an algorithm helped by the probability distribution of the number of
concurrent sojourners. For issue ii), we propose an interference control scheme
named BDBF: Block Diagonalization (BD) Precoding based on uncertain channel
state information in conjunction with auxiliary optimal Beamformer (BF). In the
simulation, we delve into the issue: how the factors impact the number of
transmit antennas on sub-small cell APs. Moreover, we verify a significant
conclusion: Using BDBF gains more capacity than using optimal BF alone within a
bearably large radius of uncertainty region.
"In multiuser MIMO downlink channels , , there also exist several methods adjusting the transmission modes at the transmitter to improve the performance of systems , . However, note that multiuser systems are fundamentally different from the IC which includes multiple transmitter-receiver pair. "
[Show abstract][Hide abstract] ABSTRACT: In this paper, we study $K$ -user multiple-input multiple-output (MIMO) interference channels (IC) in the presence of antenna correlations. Although interference alignment (IA) achieves the maximum multiplexing gain, i.e., degrees of freedom (DOF), the actual performance is degraded due to ill-conditioned channels caused by the antenna correlations. To enhance the overall system performance, we thus consider a transmission mode selection scheme which adaptively determines the number of data streams at each transmitter-receiver pair in a distributed fashion using local channel state information (CSI). A filter update process is exploited in order to estimate the performance regarding each transmission mode successively by considering the actual channel conditions as well as signal-to-noise ratio (SNR). Simulation results show that our mode selection scheme enhances the sum rate performance compared to the conventional full data stream transmissions in spatially correlated MIMO IC.
IEEE Transactions on Signal Processing 08/2012; 60(8):4475-4479. DOI:10.1109/TSP.2012.2198471 · 2.79 Impact Factor
"A general approach to deal with multiple-antenna receivers is to treat each antenna as a separate user as in . In this regard, user scheduling is investigated in connection with receive antenna selection (RAS) , or with eigenmode selection , . On the one hand such approaches efficiently exploit spatial multiplexing gains, but on the other hand they require more feedback information to be exchanged. "
[Show abstract][Hide abstract] ABSTRACT: The block diagonalization (BD) scheme is a low-complexity suboptimal precoding technique for multiuser multiple input-multiple output (MIMO) downlink channels, which completely precancels the multiuser interference. Accordingly, the precoder of each user lies in the null space of other users' channel matrices. In this paper, we propose an iterative algorithm using QR decompositions (QRDs) to compute the precoders. Specifically, to avoid dealing with a large concatenated matrix, we apply the QRD to a sequence of matrices of lower dimensions. One problem of BD schemes is that the number of users that can be simultaneously supported is limited due to zero interference constraints. When the number of users is large, a set of users must be selected, and selection algorithms should be designed to exploit the multiuser diversity gain. Finding the optimal set of users requires an exhaustive search, which has too high computational complexity to be practically useful. Based on the iterative precoder design, this paper proposes a low-complexity user selection algorithm using a greedy method, in which the precoders of selected users are recursively updated after each selection step. The selection metric of the proposed scheduling algorithm relies on the product of the squared row norms of the effective channel matrices, which is related to the eigenvalues by the Hadamard and Schur inequalities. An asymptotic analysis is provided to show that the proposed algorithm can achieve the optimal sum rate scaling of the MIMO broadcast channel. The numerical results show that the proposed algorithm achieves a good trade-off between sum rate performance and computational complexity. When users suffer different channel conditions, providing fairness among users is of critical importance. To address this problem, we also propose two fair scheduling (FS) algorithms, one imposing fairness in the approximation of the data rate, and another directly imposing fairness in the product of the squared row norms of the effective channel matrices.
IEEE Transactions on Signal Processing 07/2012; 60(7):3726-3739. DOI:10.1109/TSP.2012.2192433 · 2.79 Impact Factor
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