A Novel Construction of Multi-group Decodable Space-Time Block Codes

Complex Orthogonal Design (COD) codes are known to have the lowest detection complexity among Space-Time Block Codes (STBCs). However, the rate of square COD codes decreases exponentially with the number of transmit antennas. The Quasi-Orthogonal Design (QOD) codes emerged to provide a compromise between rate and complexity as they offer higher rates compared to COD codes at the expense of an increase of decoding complexity through partially relaxing the orthogonality conditions. The QOD codes were then generalized with the so called g-symbol and g-group decodable STBCs where the number of orthogonal groups of symbols is no longer restricted to two as in the QOD case. However, the adopted approach for the construction of such codes is based on sufficient but not necessary conditions which may limit the achievable rates for any number of orthogonal groups. In this paper, we limit ourselves to the case of Unitary Weight (UW)-g-group decodable STBCs for 2^a transmit antennas where the weight matrices are required to be single thread matrices with non-zero entries in {1,-1,j,-j} and address the problem of finding the highest achievable rate for any number of orthogonal groups. This special type of weight matrices guarantees full symbol-wise diversity and subsumes a wide range of existing codes in the literature. We show that in this case an exhaustive search can be applied to find the maximum achievable rates for UW-g-group decodable STBCs with g>1. For this purpose, we extend our previously proposed approach for constructing UW-2-group decodable STBCs based on necessary and sufficient conditions to the case of UW-g-group decodable STBCs in a recursive manner.Comment: 12 pages, and 5 tables, accepted for publication in IEEE transactions on communication

A New Low-Complexity Decodable Rate-5/4 STBC for Four Transmit Antennas with Nonvanishing Determinants

The use of Space-Time Block Codes (STBCs) increases significantly the optimal detection complexity at the receiver unless the low-complexity decodability property is taken into consideration in the STBC design. In this paper we propose a new low-complexity decodable rate-5/4 full-diversity 4 x 4 STBC. We provide an analytical proof that the proposed code has the Non-Vanishing-Determinant (NVD) property, a property that can be exploited through the use of adaptive modulation which changes the transmission rate according to the wireless channel quality. We compare the proposed code to the best existing low-complexity decodable rate-5/4 full-diversity 4 x 4 STBC in terms of performance over quasi-static Rayleigh fading channels, worst- case complexity, average complexity, and Peak-to-Average Power Ratio (PAPR). Our code is found to provide better performance, lower average decoding complexity, and lower PAPR at the expense of a slight increase in worst-case decoding complexity.Comment: 5 pages, 2 figures and 1 table; IEEE Global Telecommunications Conference (GLOBECOM 2011), 201

A New Low-Complexity Decodable Rate-1 Full-Diversity 4 x 4 STBC with Nonvanishing Determinants

Space-time coding techniques have become common-place in wireless communication standards as they provide an effective way to mitigate the fading phenomena inherent in wireless channels. However, the use of Space-Time Block Codes (STBCs) increases significantly the optimal detection complexity at the receiver unless the low complexity decodability property is taken into consideration in the STBC design. In this letter we propose a new low-complexity decodable rate-1 full-diversity 4 x 4 STBC. We provide an analytical proof that the proposed code has the Non-Vanishing-Determinant (NVD) property, a property that can be exploited through the use of adaptive modulation which changes the transmission rate according to the wireless channel quality. We compare the proposed code to existing low-complexity decodable rate-1 full-diversity 4 x 4 STBCs in terms of performance over quasi-static Rayleigh fading channels, detection complexity and Peak-to-Average Power Ratio (PAPR). Our code is found to provide the best performance and the smallest PAPR which is that of the used QAM constellation at the expense of a slight increase in detection complexity w.r.t. certain previous codes but this will only penalize the proposed code for high-order QAM constellations.Comment: 5 pages, 3 figures, and 1 table; IEEE Transactions on Wireless Communications, Vol. 10, No. 8, AUGUST 201

A New Family of Low-Complexity Decodable STBCs for Four Transmit Antennas

In this paper we propose a new construction method for rate-1 Fast-Group-Decodable (FGD) Space-Time-Block Codes (STBC)s for 2^a transmit antennas. We focus on the case of a=2 and we show that the new FGD rate-1 code has the lowest worst-case decoding complexity among existing comparable STBCs. The coding gain of the new rate-1 code is then optimized through constellation stretching and proved to be constant irrespective of the underlying QAM constellation prior to normalization. In a second step, we propose a new rate-2 STBC that multiplexes two of our rate-1 codes by the means of a unitary matrix. A compromise between rate and complexity is then obtained through puncturing our rate-2 code giving rise to a new rate-3/2 code. The proposed codes are compared to existing codes in the literature and simulation results show that our rate-3/2 code has a lower average decoding complexity while our rate-2 code maintains its lower average decoding complexity in the low SNR region at the expense of a small performance loss.Comment: 5 pages, 4 figures and 1 table. Accepted for publication in IEEE International Conference on Communications (ICC 2012), 201

La Littérature Algérienne et l’Épistémè Soufie : le cas de Mohamed Dib et de Rachid Boudjedra

The Algerian Literature and The Sufi Episteme: the case of Mohamed Dib and Rachid Boudjedra Comprendre l’écriture algérienne, c’est faire surgir en elle le sens profond qu’elle recèle et qui l’apparente à une expérience mystique, celle de soufis. En interrogeant deux auteurs classiques algériens. Mohamed Dib et Rachid Boudjedra, nous avons essayé de montrer par un questionnement du sens, que leur littérature est générée par une épistémè soufie

Association of myasthenia gravis and Behçet's disease: A case report

Myasthenia gravis is a disease of neuromuscular junction due to auto-immune destruction of the acetylcholine receptors. Behçet's disease, on the other hand, is a multisystemic vascular-inflammatory disease. Both conditions are not common in the general population although their association has not been reported in the literature. We wanted to present our patient who developed clinical course of myasthenia gravis following discontinuation of medications due to complications of corticosteroid for Behçet's disease. It was observed that clinical findings of myasthenia gravis recovered following restarting steroid treatment and he did not experience attacks of both conditions. Although Myasthenia gravis and Behçet's disease are distinct entities clinically as well as in terms of pathogenesis, they share common physiopathological features and their treatment is based on their common features

Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions

The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, the current communication systems, which were designed on the basis of conventional communication theories, signficantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learningbased communication methods are presented along with the research opportunities and challenges. In particular, novel communication frameworks of non-orthogonal multiple access (NOMA), massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated. We vision that the appealing deep learning-based wireless physical layer frameworks will bring a new direction in communication theories and that this work will move us forward along this road.Comment: Submitted a possible publication to IEEE Wireless Communications Magazin

Compressive Sampled CSI Feedback Method Based on Deep Learning for FDD Massive MIMO Systems

Accurate downlink channel state information (CSI) is required to be fed back to the base station (BS) in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems in order to achieve maximum antenna diversity and multiplexing. However, downlink CSI feedback overhead scales with the number of transceiver antennas, a major hurdle for practical deployment of FDD massive MIMO systems. To solve this problem, we propose a compressive sampled CSI feedback method based on deep learning (SampleDL). In SampleDL, the massive MIMO channel matrix is sampled uniformly in time/frequency dimension before being fed into neural networks (NNs), which will reduce the computational resource/time at user equipment (UE) as well as enhance the CSI recovery accuracy at the BS. Both theoretical analysis and normalized mean square errors (NMSE) results confirm the advantages of the proposed method in terms of time complexity and recovery accuracy. Besides, a suitable CSI feedback period is explored by link level simulations, which aims to further reduce the overhead of CSI feedback without degrading the communication quality