Hadi Sarieddeen

Modulation Classification via Subspace Detection in MIMO Systems

The problem of efficient modulation classification (MC) in multiple-input multiple-output systems is considered. Per-layer likelihood-based MC is proposed by employing subspace decomposition to partially decouple the transmitted streams. When detecting the modulation type of the stream of interest, a dense constellation is assumed on all remaining streams. The proposed classifier outperforms existing MC schemes at a lower complexity cost, and can be efficiently implemented in the context of joint MC and subspace data detection.

Likelihood-based modulation classification for MU-MIMO systems

The problem of optimal likelihood based modulation classification (MC) for optimal detection in 2×2 multiuser MIMO (MU-MIMO) receivers is considered. The optimal Log-MAP classifier is computationally exhaustive, and its sub-optimal Max-Log-MAP version poses remarkable degradation in performance. Between these two extremes, we propose four computationally efficient methods for MC, by taking special subset constellation points for Euclidean distance computations that constitute the decision metric. Compared to the Max-Log-MAP classifier, the proposed schemes achieved a frame error rate (FER) gain of 0.5dB with uncorrelated channels, while the gains reach 2dB under highly correlated channel conditions.

On the blind classification of parametric quadrature amplitude modulations

Automatic modulation classification (MC) for parametric quadrature amplitude modulation (QAM) formats is investigated. The generic θ-QAM is considered, which includes popular constellations, such as square QAM (SQAM) and triangular QAM (TQAM). Both feature-based and optimal likelihood-based classifiers are tested, where in the first we use high order cyclic cumulants (CCs) of the received signals as features for MC, and in the latter we exploit several likelihood functions. Simulations demonstrate that, unlike likelihood-based MC schemes, feature-based MC schemes are not generally suitable to discriminate between different θ-QAMs, unless the true values of θ are perfectly chosen.

Proactive channel allocation for multimedia applications over CSMA/CA-based CRNs

Aiming at an efficient use of the channels available in the wireless spectrum, cognitive radio networks (CRNs) allow users to adapt to the environment by reusing the existing spectrum. A secondary (unlicensed) user (SU) is allowed to operate in the licensed band of the primary user (PU) after sensing the spectrum and making sure that it would not interfere with it. Normally, a SU transmits over spectrum holes, and when a PU wants to switch on to her channel, the SU gets hanged and her transmission stops. Such a network alters the seamless communication and raises challenges regarding the transmission of delay-sensitive traffic like multimedia. In this paper, a new algorithm that adds a proactive behavior for channel allocation at the medium access control (MAC) layer is proposed: Before hopping into a licensed channel (LC), the SU reserves her unlicensed channel (UC) for a token time (TT), such that if a PU reclaims her LC within this time, the SU can hop back into her previous channel seamlessly. The value of TT was varied, and the effect of this variation showed favorable results on delay and jitter.

Efficient near optimal joint modulation classification and detection for MU-MIMO systems

Optimum data detection schemes for dual layer multi-user multiple-input multiple-output (MU-MIMO) systems are studied. A joint maximum likelihood (ML) modulation classification (MC) of the co-scheduled user and data detection receiver is developed. By expanding the max-log-maximum-a-posteriori MC approach to include distances of counter ML hypothesis symbols, the decision metric for MC is shown to be an accumulation over a set of tones of Euclidean distance computations also used by the ML detector for bit log-likelihood ratio soft decision generation. With a small complexity overhead, the proposed approach achieves near-optimal performance. An efficient hardware architecture is presented for the proposed approach.

High Order Multi-User MIMO Subspace Detection

An efficient high order multi-user multiple-input multiple-output (MU-MIMO) subspace detector is proposed. The detector employs joint modulation classification (MC) and subspace detection (SD), by which the modulation type of the interferer is estimated, while multiple decoupled streams are individually detected. The algorithmic contributions are on two levels. First, the preprocessing channel matrix decomposition overhead is reduced, using special layer ordering followed by permutation-robust QR Decomposition and elementary matrix operations. Second, a hierarchical MC scheme is proposed, comprising feature-based and near-optimal likelihood-based classifiers, as well as a classifier that always assumes the interfering modulation type to be a fixed high order quadrature amplitude modulation. An efficient hardware architecture that realizes the proposed algorithms is presented. Simulations demonstrate that depending on the channel condition, one of the proposed schemes can achieve near interference-aware performance with a minimum complexity overhead.

Low-complexity joint modulation classification and detection in MU-MIMO

In this paper, dual-layer multi-user multiple-input multiple-output systems are studied. Building on the low-complexity layered orthogonal lattice detector (LC-LORD), an efficient sub-optimal joint modulation classification (MC) of the co-scheduled user and data detection receiver is developed. By adjusting the Max-Log-Maximum-a-Posteriori MC approach to the limitations of LC-LORD, and expanding it to include distances of counter maximum likelihood hypothesis symbols, the decision metric for MC is shown to be an accumulation over a set of tones of Euclidean distance computations also used by the LC-LORD detector for bit log-likelihood ratio soft decision generation. Simulations demonstrate that with a small complexity overhead, the proposed approaches achieve near interference-aware performance. An efficient hardware implementation scheme is presented.

Efficient near-optimal 8×8 MIMO detector

In this paper, a low-complexity near-optimal detector for 8-layer MIMO systems is proposed. The detector employs subspace detection schemes, which decompose a spacially multiplexed MIMO channel into multiple decoupled streams to be detected separately. Several existing subspace detection algorithms are studied, all of which require a significant overhead for channel matrix decomposition. We propose computationally efficient schemes based on special layer ordering, followed by permutation-robust QR Decomposition (PR-QRD) using the modified Gram-Schmidt orthogonalization procedure, and elementary matrix operations. A hardware architecture is proposed, which allows building an 8-layer detector from 4-layer and 2-layer constituent detector blocks. Simulations demonstrate that using the proposed scheme, the QRD overhead is reduced by 30%, without incurring any performance degradation.

Enhanced low-complexity layer-ordering for MIMO sphere detectors

In this paper, optimum soft-output (SO) multiple-input multiple-output (MIMO) sphere detectors (SDs) are studied. Noting that ordering the channel matrix columns plays an important role in reducing the tree-search complexity of a SD, we propose an optimized layer-ordering scheme based on the minimum cumulative residual criterion. The proposed scheme is studied in the context of a 4 × 4 MIMO system, and a low-complexity dataflow architecture is proposed. The implementation employs a permutation-robust QR decomposition (PR-QRD) scheme, based on the modified Gram-Schmidt orthogonalization procedure. Simulations demonstrate that using the proposed scheme, the node count of a SO MIMO SD is reduced by one order of magnitude, while the QRD overhead is reduced by more than 25% in computations and 36% in time, without incurring any performance degradation.

Efficient subspace detection for high-order MIMO systems

In this paper, low-complexity multiple-input multiple-output (MIMO) subspace detection schemes are studied, which decompose a channel into multiple decoupled streams to be detected disjointly. Existing schemes require a number of matrix decomposition operations equal to the number of detected streams, which is computationally complex, especially in high-order MIMO systems. We propose two computationally efficient detection algorithms, based on a preprocessing stage that consists of special layer ordering, followed by permutation-robust QR decomposition (QRD) and elementary matrix operations. The algorithms are illustrated in the context of a 4-layer MIMO system, and their complexity is studied. Simulations demonstrate that using the proposed scheme, the QRD overhead is reduced by almost 50% for very high order MIMO, without incurring any performance degradation.