In frequency division duplex mode, the downlink channel state information (CSI) should be sent to the base station through feedback links so that the potential gains of a massive multiple-input multiple-output can be exhibited. However, such a transmission is hindered by excessive feedback overhead. In this letter, we use deep learning technology to develop CsiNet, a novel CSI sensing and recovery mechanism that learns to effectively use channel structure from training samples. CsiNet learns a transformation from CSI to a near-optimal number of representations (or codewords) and an inverse transformation from codewords to CSI. We perform experiments to demonstrate that CsiNet can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing (CS)-based methods. Even at excessively low compression regions where CS-based methods cannot work, CsiNet retains effective beamforming gain.Index Terms-Massive MIMO, FDD, compressed sensing, deep learning, conventional neural network.
Human mitochondrial NAD(P) + -dependent malic enzyme (m-NAD(P)-ME) has a dimer of dimers quaternary structure with two independent allosteric sites in each monomer. Here, we reveal the different effects of nucleotide ligands on the quaternary structure regulation and functional role of the human m-NAD(P)-ME exosite. In this study, size distribution analysis was utilized to investigate the monomer-dimer-tetramer equilibrium of m-NAD(P)-ME in the presence of different ligands, and the monomer-dimer ( K d,12 ) and dimer-tetramer ( K d,24 ) dissociation constants were determined with these ligands. With NAD + , the enzyme formed more tetramers, and its K d,24 (0.06 µM) was 6-fold lower than the apoenzyme K d,24 (0.34 µM). When ATP was present, the enzyme displayed more dimers, and its K d,24 (2.74 µM) was 8-fold higher than the apoenzyme. Similar to the apoenzyme, the ADP-bound enzyme was present as a tetramer with a small amount of dimers and monomers. These results indicate that NAD + promotes association of the dimeric enzyme into tetramers, whereas ATP stimulates dissociation of the tetrameric enzyme into dimers, and ADP has little effect on the tetrameric stability of the enzyme. A series of exosite mutants were created using site-directed mutagenesis. Size distribution analysis and kinetic studies of these mutants with NAD + or ATP indicated that Arg197, Asn482 and Arg556 are essential for the ATP binding and ATP-induced dissociation of human m-NAD(P)-ME. In summary, the present results demonstrate that nucleotides perform discrete functions regulating the quaternary structure and catalysis of m-NAD(P)-ME. Such regulation by the binding of different nucleotides may be critically associated with the physiological concentrations of these ligands.
The use of low-resolution analog-to-digital converters (ADCs) can significantly reduce power consumption and hardware cost. However, their resulting severe nonlinear distortion makes reliable data transmission challenging. For orthogonal frequency division multiplexing (OFDM) transmission, the orthogonality among subcarriers is destroyed. This invalidates conventional OFDM receivers relying heavily on this orthogonality. In this study, we move on to quantized OFDM (Q-OFDM) prototyping implementation based on our previous achievement in optimal Q-OFDM detection. First, we propose a novel Q-OFDM channel estimator by extending the generalized Turbo (GTurbo) framework formerly applied for optimal detection. Specifically, we integrate a type of robust linear OFDM channel estimator into the original GTurbo framework and derive its corresponding extrinsic information to guarantee its convergence. We also propose feasible schemes for automatic gain control, noise power estimation, and synchronization. Combined with the proposed inference algorithms, we develop an efficient Q-OFDM receiver architecture. Furthermore, we construct a proofof-concept prototyping system and conduct over-the-air (OTA) experiments to examine its feasibility and reliability. This is the first work that focuses on both algorithm design and system implementation in the field of low-resolution quantization communication. The results of the numerical simulation and OTA experiment demonstrate that reliable data transmission can be achieved.
In frequency division duplex mode of massive multiple-input multiple-output systems, the downlink channel state information (CSI) must be sent to the base station (BS) through a feedback link. However, transmitting CSI to the BS is costly due to the bandwidth limitation of the feedback link. Deep learning (DL) has recently achieved remarkable success in CSI feedback. Realizing high-performance and low-complexity CSI feedback is a challenge in DL based communication. We develop a DL based CSI feedback network in this study to complete the feedback of CSI effectively. However, this network cannot be effectively applied to the mobile terminal because of the excessive numbers of parameters. Therefore, we further propose a new lightweight CSI feedback network based on the developed network. Simulation results show that the proposed CSI network exhibits better reconstruction performance than that of other CsiNet-related works. Moreover, the lightweight network maintains a few parameters and parameter complexity while ensuring satisfactory reconstruction performance. These findings suggest the feasibility and potential of the proposed techniques.
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