Optimal Power Allocation and User Loading for Multiuser MISO Channels with Regularized Channel Inversion

Muharar, Rusdha; Zakhour, Randa; Evans, Jamie

doi:10.1109/tcomm.2013.111613.130478

Cited by 21 publications

(29 citation statements)

References 27 publications

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“…Regarding the parameters ofΣ j [n], ϕ r,j is a positive scalar and Z r,j ∈ C N ×N is a Hermitian nonnegative definite matrix, which can be optimized, and provide some new insights in case of presence of both delayed CSI and pilot contamination. Similar analysis has been presented in [39], where different regularization parameters for RZF precoding were derived under different scenarios with large number of antennas. Thus, it is an interesting topic of future work to explore and determine the corresponding parameters and shed light on their behavior.…”

Section: Asymptotic Performance Analysismentioning

confidence: 74%

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Deterministic Equivalent Performance Analysis of Time-Varying Massive MIMO Systems

Papazafeiropoulos

Ratnarajah

2015

IEEE Trans. Wireless Commun.

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Abstract-Delayed channel state information at the transmitter (CSIT) due to time variation of the channel, coming from the users' relative movement with regard to the BS antennas, is an inevitable degrading performance factor in practical systems. Despite its importance, little attention has been paid to the literature of multi-cellular multiple-input massive multiple-output (MIMO) system by investigating only the maximal ratio combining (MRC) receiver and the maximum ratio transmission (MRT) precoder. Hence, the contribution of this work is designated by the performance analysis/comparison of/with more sophisticated linear techniques, i.e., a minimum-mean-square-error (MMSE) detector for the uplink and a regularized zero-forcing (RZF) precoder for the downlink are assessed. In particular, we derive the deterministic equivalents of the signal-to-interference-plus-noise ratios (SINRs), which capture the effect of delayed CSIT, and make the use of lengthy Monte Carlo simulations unnecessary. Furthermore, prediction of the current CSIT after applying a Wiener filter allows to evaluate the mitigation capabilities of MMSE and RZF. Numerical results depict that the proposed achievable SINRs (MMSE/RZF) are more efficient than simpler solutions (MRC/MRT) in delayed CSIT conditions, and yield a higher prediction at no special computational cost due to their deterministic nature. Nevertheless, it is shown that massive MIMO are preferable even in time-varying channel conditions.

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Section: Asymptotic Performance Analysismentioning

confidence: 74%

“…Theorem 3: The downlink deterministic equivalent SINR at user m in cell j, applying a RZF precoder and accounting for delayed CSI, is given by (39) …”

Section: B Downlink Transmissionmentioning

confidence: 99%

Deterministic Equivalent Performance Analysis of Time-Varying Massive MIMO Systems

Papazafeiropoulos

Ratnarajah

2015

IEEE Trans. Wireless Commun.

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“…As mentioned earlier, the concept of regularization is most beneficial with equal number of transmit and receive antennas [5,24,27,28]. Nevertheless, in Fig.…”

Section: Numerical Resultsmentioning

confidence: 92%

“…So far, this problem has been treated in two distinguishable ways: In the first category of literature like [8,9], the regularization parameter has been derived only for the case of digital feedback where it has been further assumed that the perfect CSI and the channel estimation error are not independent of each other. In the second category of literature like [5,[24][25][26][27], the regularization parameter has been derived under the assumption of asymptotically large number of transmit and receive antennas. However, the proposed regularization parameter in this work is derived in a distinct manner from the previously mentioned works such that it is amenable to our generalized CSI mismatch model.…”

Section: Contributionsmentioning

confidence: 99%

“…Similar to [5,[27][28][29], we consider g as the scaling factor that ensures transmit power constraint i.e., E J s J We also define the nominal SNR as ρ ¼ P=σ 2 . Note that although the concept of regularization is most beneficial for the case of equal number of transmit and receive antennas [5,24,27,28], without loss of generality, we assume M rN.…”

Section: System Modelmentioning

confidence: 99%

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Channel inversion and regularization revisited

Razavi

Ratnarajah

2016

Signal Processing

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a b s t r a c tIn multiuser multiple-input-single-output (MISO) downlink, linear precoders like channel inversion (CI) and regularized CI (RCI) are more desirable than their nonlinear counterparts due to their reduced complexity. To achieve the full benefits of linear precoding, the availability of perfect channel state information (CSI) at base stations (BSs) is necessary. Since in practice, having access to perfect CSI is not pragmatic, in this paper, we evaluate the performance of CI and RCI under a generalized, imperfect CSI model where the variance of the channel estimation error depends on the signal-to-noise ratio (SNR) and thus covers digital and analog feedbacks as two special cases. Then, based on this imperfect CSI model, we quantify the asymptotic mean loss in sum rate and the achievable degrees of freedom (DoFs) by deriving the received signal-to-interference-plus-noise ratio (SINR) of each user. For example, it is shown that the achievable DoF is directly related to the SNR exponent of the channel estimation error variance. Also, two asymptotic gaps to capacity for the analog feedback are derived: mean loss in sum rate and power loss. In addition, we propose an adaptive RCI technique by deriving an appropriate regularization parameter as a function of the error variance and without imposing any restrictions on the number of users or antennas. It is shown that in the presence of CSI mismatch, while the comparative improvement of the standard RCI to CI becomes negligible, the adaptive RCI compensates this degraded performance of the standard RCI without introducing any extra computational complexity.

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Clustered linear precoding for downlink network MIMO systems with partial CSI

Sadeghzadeh

Bahrami

Tran

2016

Wireless Communications

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We propose two novel clustered linear precoding schemes applicable to network multi-input multi-output systems using only partial channel state information to enhance the sum-rate of the system. Using a channel model that decomposes a multi-input multi-output channel matrix into transmit and receive steering vectors and assuming that only transmit steering vectors are available at the base transmit stations, we, first, propose a regularized channel inversion precoding scheme to enhance the sum-rate assuming only single-antenna users are available in the system. Next, because of the limitation of regularized channel inversion to handle users with multiple receive antennas, a novel block diagonalization method is proposed. We construct the precoding matrices that jointly eliminate inter-cell interference and maximize the sumrate for a given input covariance matrix. Assuming total power constraint and per-base-station power constraints, optimal power allocation schemes are further developed to optimize the sum-rate. We analytically show that the sum-rate increases linearly with the number of users when only single-antenna users are present in the system. Numerical results show that at low signal-to-noise ratios, the block diagonalization precoding outperforms the regularized channel inversion in terms of the bit error rate; while at high signal-to-noise ratios, the regularized channel inversion provides a better performance.Clustered linear precoding for downlink network MIMO systems or a part of the channel matrix (e.g., [27,28]). In this paper, however, we use a special multiplicative model that decomposes the channel matrix into a known and an unknown. We then use this model to derive the proposed partial CSI-based precoders. Related WorkAmong precoding techniques for downlink multiuser systems, regularized channel inversion and block diagonalization (BD) with full CSI have received lots of attention. The recent attempts on the design of regularized channel inversion precoding have mainly focused on maximizing the sum-rate of single-cell and multi-cell systems [29][30][31][32].In [29], optimal power allocation for the case of multiuser multi-input single-output is proposed, and it is shown that, for large number of transmit antennas, the power allocation converges to a waterfilling scheme. In [30], the throughput of a multi-cell downlink network with imperfect CSI is investigated and in [31] and [32], secrecy sum-rate and ergodic secrecy sum-rate of the multiuser MIMO have been studied. In [5] and [9], the BD technique with full CSI, originally proposed for single-cell multi-user MIMO systems [10][11][12], is deployed to design precoder matrices for network MIMO to mitigate the ICI between user equipments (UEs). More recently, the complexity reduction of BD techniques have been investigated in [33][34][35]. In [33] and [34], the authors propose the complexity reduction of BD using a common channel inversion method, QR decomposition, and lattice reduction, while in [35], a generalized zero-forcing precoder is prop...

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Optimal Power Allocation and User Loading for Multiuser MISO Channels with Regularized Channel Inversion

Cited by 21 publications

References 27 publications

Deterministic Equivalent Performance Analysis of Time-Varying Massive MIMO Systems

Deterministic Equivalent Performance Analysis of Time-Varying Massive MIMO Systems

Channel inversion and regularization revisited

Clustered linear precoding for downlink network MIMO systems with partial CSI

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