Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming

Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate. However, the hybrid precoder design is a challenging task requiring channel state information (CSI) feedback and solving a complex optimization problem. This paper proposes a novel RSSI-based unsupervised deep learning method to design the hybrid beamforming in massive MIMO systems. Furthermore, we propose i) a method to design the synchronization signal (SS) in initial access (IA); and ii) a method to design the codebook for the analog precoder. We also evaluate the system performance through a realistic channel model in various scenarios. We show that the proposed method not only greatly increases the spectral efficiency especially in frequency-division duplex (FDD) communication by using partial CSI feedback, but also has near-optimal sum-rate and outperforms other state-of-the-art full-CSI solutions.Comment: Submitted to IEEE Transactions on Wireless Communication

Learning Energy-Efficient Hardware Configurations for Massive MIMO Beamforming

Hybrid beamforming (HBF) and antenna selection are promising techniques for improving the energy efficiency~(EE) of massive multiple-input multiple-output~(mMIMO) systems. However, the transmitter architecture may contain several parameters that need to be optimized, such as the power allocated to the antennas and the connections between the antennas and the radio frequency chains. Therefore, finding the optimal transmitter architecture requires solving a non-convex mixed integer problem in a large search space. In this paper, we consider the problem of maximizing the EE of fully digital precoder~(FDP) and hybrid beamforming~(HBF) transmitters. First, we propose an energy model for different beamforming structures. Then, based on the proposed energy model, we develop an unsupervised deep learning method to maximize the EE by designing the transmitter configuration for FDP and HBF. The proposed deep neural networks can provide different trade-offs between spectral efficiency and energy consumption while adapting to different numbers of active users. Finally, to ensure that the proposed method can be implemented in practice, we investigate the ability of the model to be trained exclusively using imperfect channel state information~(CSI), both for the input to the deep learning model and for the calculation of the loss function. Simulation results show that the proposed solutions can outperform conventional methods in terms of EE while being trained with imperfect CSI. Furthermore, we show that the proposed solutions are less complex and more robust to noise than conventional methods.Comment: This preprint comprises 15 pages and features 15 figures. Copyright may be transferred without notic

RSSI-Based Hybrid Beamforming Design with Deep Learning

Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) systems because non-convex optimization problems have to be solved, introducing additional latency and energy consumption. In addition, the channel-state information (CSI) must be either estimated from pilot signals or fed back through dedicated channels, introducing a large signaling overhead. In this paper, a hybrid precoder is designed based only on received signal strength indicator (RSSI) feedback from each user. A deep learning method is proposed to perform the associated optimization with reasonable complexity. Results demonstrate that the obtained sum-rates are very close to the ones obtained with full-CSI optimal but complex solutions. Finally, the proposed solution allows to greatly increase the spectral efficiency of the system when compared to existing techniques, as minimal CSI feedback is required.Comment: Published in IEEE-ICC202

Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning

Cell-free massive MIMO (CF-mMIMO) systems represent a promising approach to increase the spectral efficiency of wireless communication systems. However, near-optimal beamforming solutions require a large amount of signaling exchange between access points (APs) and the network controller (NC). In this letter, we propose two unsupervised deep neural networks (DNN) architectures, fully and partially distributed, that can perform decentralized coordinated beamforming with zero or limited communication overhead between APs and NC, for both fully digital and hybrid precoding. The proposed DNNs achieve near-optimal sum-rate while also reducing computational complexity by 10-24x compared to conventional near-optimal solutions.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notic

Joint CFO and channel estimation in OFDM-based massive MIMO systems

Estimation of carrier frequency offset (CFO) is a challenging task in practical systems specifically in the uplink of multiuser systems where multiple CFOs are present in the received signal. Massive MIMO as a multiuser technique has recently attracted a great deal of attention among researchers. However, to the best of our knowledge, there is no study looking into the joint estimation of CFOs and wireless channel in orthogonal frequency division multiplexing (OFDM) based massive MIMO systems. Therefore, in this paper, we propose joint estimation of multiple CFOs and the users' channel responses based on the maximum likelihood (ML) criteria in such systems. We propose to use the zadoff-chu (ZC) training sequences to reduce the implementation complexity. Additionally, utilization of ZC sequences for training simplifies the multidimensional grid search problem of estimating multiple CFOs and converts it into a set of line search problems, i.e., one line search problem per user. Also this sequence has a low peak to average ratio (PAPR). Finally, we show the efficacy of our proposed algorithm through numerical simulations

Mitochondrial ATPase 6,8 Associated with Brain Tumours in Patients Compared to Adjacent Normal Brain Cells

Abstract | Background: Brain cancer is considered one of the most prevalent types of cancer in the world. Primary brain tumours consist of two types. Studies provide some deficiencies in mitochondrial functions that could cause different genetic. Objective: This study aimed to determine the association of ATPase 6,8 alterations of brain tumour cells in comparison with the adjacent healthy tissue cells. Methods: A group of patients was examined, and their disease was identified during precise examinations. These persons were sampled for their affected brain tissues, and these were compared with their adjacent healthy cells. Besides, the populations of 300 healthy controls were selected as the control. The DNA of the brain tumour cells was extracted and analysed using sequencing methods. Result: After the sequence analysis, T8473C, G8584A, A8701G, A8730G and A8860G variants were found—all of them had been reported in other diseases. Also, they were observed in patients with brain tumours, as compared with the adjacent normal tissues. Discussion: The A8860G variant was one of the most prevalent polymorphisms between all these alterations in brain cancer. It seems that the ATPase 6 subunit is more prone to brain cancer. The analysis shows that amongst all the five variants determined in this research, the T8473C, G8584A and A8730G variants—with the p value<0.05—were considered to affect brain tumours

RSSI-Based Hybrid Beamforming Design with Deep Learning

Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) systems because non-convex optimization problems have to be solved, introducing additional latency and energy consumption. In addition, the channel-state information (CSI) must be either estimated from pilot signals or fed back through dedicated channels, introducing a large signaling overhead. In this paper, a hybrid precoder is designed based only on received signal strength indicator (RSSI) feedback from each user. A deep learning method is proposed to perform the associated optimization with reasonable complexity. Results demonstrate that the obtained sum-rates are very close to the ones obtained with full-CSI optimal but complex solutions. Finally, the proposed solution allows to greatly increase the spectral efficiency of the system when compared to existing techniques, as minimal CSI feedback is required

Mitochondrial Polymorphisms, in The D-Loop Area, Are Associated with Brain Tumors

Objective This study was carried out to evaluate the relationship between mtDNA D-loop variations and the pathogenesis of a brain tumor. Materials and Methods In this experimental study, 25 specimens of brain tumor tissue with their adjacent tissues from patients and 454 blood samples from different ethnic groups of the Iranian population, as the control group, were analysed by the polymerase chain reaction (PCR)-sequencing method. Results Thirty-six variations of the D-loop area were observed in brain tumor tissues as well as the adjacent normal tissues. A significant difference of A750G (P=0.046), T15936C (P=0.013), C15884G (P=0.013), C16069T (P=0.049), T16126C (P=0.006), C16186T (P=0.022), T16189C (P=0.041), C16193T (P=0.045), C16223T (P=0.001), T16224C (P=0.013), C16234T (P=0.013), G16274A (P=0.009), T16311C (P=0.038), C16327T (P=0.045), C16355T (P=0.003), T16362C (P=0.006), G16384A (P=0.042), G16392A (P=0.013), G16394A (P=0.013), and G16477A (P=0.013) variants was found between the patients and the controls. Conclusion The results indicated individuals with C16069T [odds ratio (OR): 2.048], T16126C (OR: 2.226), C16186T (OR: 3.586), G16274A (OR: 4.831), C16355T (OR: 7.322), and T16362C (OR: 6.682) variants with an OR more than one are probably associated with a brain tumor. However, given the multifactorial nature of cancer, more investigation needs to be done to confirm this association

Chemotherapy Could Induce Antibiotic Resistance in E. Faecalis in Patients with Colorectal Cancer

Colorectal cancer is one of the most common cancers in Iran. There are many effective methods of treatment of it. As a conventional treatment, chemotherapy has become a part of treatment scheme for patients with colorectal cancer. Enterococci are intestinal commensals. They are opportunistic pathogens which cause millions of human and animal infections annually. The aim of this study was to investigate the side effects of chemotherapy of sufferers from colon cancer on the antibiotic resistance of microflora. Methods: In this study, participants were divided into three groups: Group A: 300 colorectal cancer patients before the start of the cancer chemotherapy, group B: 300 healthy people living with patients at least for recent 12 months and group C includes 300 patients with colorectal cancer after six weeks chemotherapy. RNA was extracted from the stool of all the participants of the study. Following the RNA extraction from stool samples, cDNA libraries were constructed. Eight virulent genes (vanA, vanB, gelE, esp, asa1, aggA, efaA and enlA) of E. faecalis were evaluated by real-time qPCR. Results: The results were showed the expression level of the virulent genes in the group of the patients after chemotherapy was significantly higher than the two groups of B and C (P<0.05). Although the expression of these genes in the group of patients before chemotherapy was higher than that of the control group, this increase was not significant (P>0.05). Conclusions: It seems that chemotherapy could change the balance of mRNA expression of microflora such as antibiotic resistance genes. These could be responsible for infections arisen after ending the chemotherapy of cancer