Hardware topologies for decentralized large-scale MIMO detection using Newton method

Centralized Massive Multiple Input Multiple Output (MIMO) uplink detection techniques for baseband processing possess severe bottleneck in terms of interconnect bandwidth and computational complexity. This problem has been addressed in the current work by adapting the centralized Newton method for decentralized MIMO uplink detection leveraging several Base Station antenna clusters. The proposed decentralized Newton (DN) method achieves error-rate performance close to centralized Zero Forcing detector as compared to other decentralized techniques. Two hardware topologies, namely the ring and the star topologies, are proposed to assess and discuss the trade-off among interconnect bandwidth and throughput, in comparison with contemporary decentralized MIMO uplink detection techniques. As such the following findings are elaborated. On BS antenna cluster scaling for different MIMO system configurations, the ring topology provides high throughput at constant interconnect bandwidth, while the star topology provides lower latency with a deterministic variation in the hardware resource consumption. Due to strategic optimizations on the hardware implementation, additional user equipment can be allotted at a fractional increase in Field Programmable Gate Array resource consumption, improved energy efficiency, and increased transaction of bits per Joule. The ring topology can process additional subcarrier at a fractional increase in latency and improved system throughput

FPGA implementation for the multiplexed and pipelined building blocks of higher Radix-2k FFT

Fast Fourier transform (FFT) is one of the fundamental processing block used in many signal processing applications (i.e. for orthogonal frequency division multiplexing in wireless telecommunication). Therefore, every proposal to reduced latency, resources or accuracy errors of FFT implementation counts. This paper proposes the implementation of the butterfly processing elements (BPE) where the concept of the radix-r butterfly computation has been formulated as the combination of α radix-2 butterflies implemented in parallel. An efficient FFT implementation is feasible using our proposed multiplexed and pipelined BPE. Compared to a state-of-the-art reference based on pipelined and parallel structure FFTs, and FPGA based implementation reveals that the maximum throughput is improved by a factor of 1.3 for a 256-point FFT and reach a throughput of 2680 MSps on Virtex-7. The analysis extends to touch on key performance measurements metrics such as throughput, latency and resource utilization

Performance evaluation and implementation complexity analysis framework for ZF based linear massive MIMO detection

This paper discusses a framework for algorithm-architecture synergy for (1) performance evaluation and (2) FPGA implementation complexity analysis of linear massive MIMO detection techniques. Three low complexity implementation techniques of the zero-forcing (ZF) based linear detection are evaluated, namely, Neumann series expansion (NSE), Gauss–Seidel (GS) and a proposed recursive Gram matrix inversion update (RGMIU) techniques. The performance analysis framework is based on software-defined radio platform. By extrapolating the real data measured average error vector magnitude versus a number of served single-antenna user terminals, GS and RGMIU are showing no performance degradation with respect to ZF with direct matrix inversion. It is shown that under high load regime NSE and GS require more processing iterations at the expense of increased processing latency. We, therefore, consider a unified approach for field-programmable gate array based implementation complexity analysis and discuss the required baseband processing resources for real-time transmission. Due to the wide differences of NSE, GS and RGMIU in terms of performance, processing complexity and latency, practical deployment and real-time implementation insights are derived

Adversarial bandit approach for RIS-aided OFDM communication

To assist sixth-generation wireless systems in the management of a wide variety of services, ranging from mission-critical services to safety-critical tasks, key physical layer technologies such as reconfigurable intelligent surfaces (RISs) are proposed. Even though RISs are already used in various scenarios to enable the implementation of smart radio environments, they still face challenges with regard to real-time operation. Specifically, high dimensional fully passive RISs typically need costly system overhead for channel estimation. This paper, however, investigates a semi-passive RIS that requires a very low number of active elements, wherein only two pilots are required per channel coherence time. While in its infant stage, the application of deep learning (DL) tools shows promise in enabling feasible solutions. We propose two low-training overhead and energy-efficient adversarial bandit-based schemes with outstanding performance gains when compared to DL-based reflection beamforming reference methods. The resulting deep learning models are discussed using state-of-the-art model quality prediction trends

Locating the Binding Sites of Pb(II) Ion with Human and Bovine Serum Albumins

Lead is a potent environmental toxin that has accumulated above its natural level as a result of human activity. Pb cation shows major affinity towards protein complexation and it has been used as modulator of protein-membrane interactions. We located the binding sites of Pb(II) with human serum (HSA) and bovine serum albumins (BSA) at physiological conditions, using constant protein concentration and various Pb contents. FTIR, UV-visible, CD, fluorescence and X-ray photoelectron spectroscopic (XPS) methods were used to analyse Pb binding sites, the binding constant and the effect of metal ion complexation on HSA and BSA stability and conformations. Structural analysis showed that Pb binds strongly to HSA and BSA via hydrophilic contacts with overall binding constants of KPb-HSA = 8.2 (±0.8)×104 M−1 and KPb-BSA = 7.5 (±0.7)×104 M−1. The number of bound Pb cation per protein is 0.7 per HSA and BSA complexes. XPS located the binding sites of Pb cation with protein N and O atoms. Pb complexation alters protein conformation by a major reduction of α-helix from 57% (free HSA) to 48% (metal-complex) and 63% (free BSA) to 52% (metal-complex) inducing a partial protein destabilization

Biogenic and Synthetic Polyamines Bind Cationic Dendrimers

Biogenic polyamines are essential for cell growth and differentiation, while polyamine analogues exert antitumor activity in multiple experimental model systems, including breast and lung cancer. Dendrimers are widely used for drug delivery in vitro and in vivo. We report the bindings of biogenic polyamines, spermine (spm), and spermidine (spmd), and their synthetic analogues, 3,7,11,15-tetrazaheptadecane.4HCl (BE-333) and 3,7,11,15,19-pentazahenicosane.5HCl (BE-3333) to dendrimers of different compositions, mPEG-PAMAM (G3), mPEG-PAMAM (G4) and PAMAM (G4). FTIR and UV-visible spectroscopic methods as well as molecular modeling were used to analyze polyamine binding mode, the binding constant and the effects of polyamine complexation on dendrimer stability and conformation. Structural analysis showed that polyamines bound dendrimers through both hydrophobic and hydrophilic contacts with overall binding constants of Kspm-mPEG-G3 = 7.6×104 M−1, Kspm-mPEG-PAMAM-G4 = 4.6×104 M−1, Kspm-PAMAM-G4 = 6.6×104 M−1, Kspmd-mPEG-G3 = 1.0×105 M−1, Kspmd-mPEG-PAMAM-G4 = 5.5×104 M−1, Kspmd-PAMAM-G4 = 9.2×104 M−1, KBE-333-mPEG-G3 = 4.2×104 M−1, KBe-333-mPEG-PAMAM-G4 = 3.2×104 M−1, KBE-333-PAMAM-G4 = 3.6×104 M−1, KBE-3333-mPEG-G3 = 2.2×104 M−1, KBe-3333-mPEG-PAMAM-G4 = 2.4×104 M−1, KBE-3333-PAMAM-G4 = 2.3×104 M−1. Biogenic polyamines showed stronger affinity toward dendrimers than those of synthetic polyamines, while weaker interaction was observed as polyamine cationic charges increased. The free binding energies calculated from docking studies were: −3.2 (spermine), −3.5 (spermidine) and −3.03 (BE-3333) kcal/mol, with the following order of binding affinity: spermidine-PAMAM-G-4>spermine-PAMMAM-G4>BE-3333-PAMAM-G4 consistent with spectroscopic data. Our results suggest that dendrimers can act as carrier vehicles for delivering antitumor polyamine analogues to target tissues

Classification and Detection of Cancer in Histopathologic Scans of Lymph Node Sections Using Convolutional Neural Network

Cancer has been considered one of the major threats to the lives and health of people. The substantial clinical practices show that earlier diagnosis and detection of cancer can provide adaptable treatment methods, increase survivability, and enhance life quality. Moreover, rapid advancements in science, technology, and Computer-Aided Diagnosis systems also provide additional information for robust analysis and examination of medical images. Image processing and machine learning presented promising low-cost approaches for classifying and detecting different cancerous diseases. However, these traditional techniques need extensive pre-processing and laborious manual features extraction methods. Thus, in this paper, we presented a Convolutional Neural Network based method for the classification and detection of metastatic cancer in histopathologic images of lymph node sections. A diagnostic method of cancer in histopathologic images is time consuming and tedious for pathologists because a large tissue area has been examined, and tiny metastasis can be easily ignored. Thus the developed deep learning method can help pathologists in examining the histopathologic scans and assist in decision-making to analyze the disease and cancer staging, which will give consequential opinions in clinical diagnosis. We performed the necessary pre-processing and data augmentation steps to enhance the results and avoid overfitting. The method utilizes low dimensional representations and performs automated, categorical feature extraction and classification, which attain high accuracy for diagnosis of cancer. The method is applied to PatchCamelyon (PCam) data set. Experimental results show good performance with an accuracy rate of 0.94 for the medical image classification and detection task

The effects of Poly(ethylene glycol) on the Solution Structure of Human Serum Albumin,

Protein physical and chemical properties can be altered by polymer interaction. The presence of several high affinity binding sites on human serum albumin (HSA) makes it a possible target for many organic and polymer molecules. This study was designed to examine the interaction of HSA with poly(ethylene glycol) (PEG) in aqueous solution at physiological conditions. Fourier transform infrared, ultraviolet‐visible, and CD spectroscopic methods were used to determine the polymer binding mode, the binding constant, and the effects of polymer complexation on protein secondary structure. The spectroscopic results showed that PEG is located along the polypeptide chains through H‐bonding interactions with an overall affinity constant of K = 4.12 × 105M-1. The protein secondary structure showed no alterations at low PEG concentration (0.1 mM), whereas at high polymer content (1 mM), a reduction of α‐helix from 59 (free HSA) to 53% and an increase of β‐turn from 11 (free HSA) to 22% occurred in the PEG-HSA complexes (infrared data). The CDSSTR program (CD data) also showed no major alterations of the protein secondary structure at low PEG concentrations (0.1 and 0.5 mM), while at high polymer content (1 mM), a major reduction of α‐helix from 69 (free HSA) to 58% and an increase of β‐turn from 7 (free HSA) to 18% was observed

The Effects of Drug Complexation on the Stability and Conformation of Human Serum Albumin: Protein Unfolding

We report different analytical methods used to study the effects of 3\u27-azido-3\u27-deoxythymidine, aspirin, taxol, cisplatin, atrazine, 2,4-dichlorophenoxyacetic, biogenic, polyamines, chlorophyll, chlorophyllin, poly(ethylene glycol), vanadyl cation, vanadate anion, cobalt-hexamine cation, and As2O3, on the stability and secondary structure of human serum albumin (HSA) in aqueous solution, using capillary electrophoresis. Fourier transform infrared, ultraviolet visible, and circular dichroism (CD) spectroscopic methods. The concentrations of HSA used were 4% to 2% or 0.6 to 0.3 mM, while different ligand concentrations were 1μM to 1 mM. Structural data showed drugs are mostly located along the polypeptide chains with both specific and nonspecific interactions. The stability of drug-protein complexes were in the order KVO 2+ 1.2×108 M -1\u3eKAZT 1.9×106 M -1\u3eKPEG 4.1×105 M -1\u3eKatrazine 3.5×104 M -1\u3eKchlorophyll 2.9×104 M -1\u3eK 2,4-D2.5×104 M-1\u3eKspermine 1.7×104 M -1\u3eKtaxol 1.43×104 M -1\u3eKCo3+\u3e1.1×104 M -1\u3eKaspirin 1.04×104i-1\u3eKchlorophyllin 7.0×103 M -1×KVO3 -6.0×103 M -1\u3eKspermidine 5.4 ×103 M -1\u3eKputrescine 3.9×103 M -1\u3eKAs2O3, 2.2×103 M -1\u3eKcisplatin 1.2×102 M -1. The protein conformation was altered (infrared and CD results) with major reduction of α-helix from 60 to 55% (free HSA) to 40 to 40% and increase of β-structure from 22 to 15% (free HSA) to 33 to 23% in the drug-protein complexes. The alterations of protein secondary, structure are attributed to partial, unfolding of HSA on drug complexation