Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMIMO Operations in Molecular Communications: Theory, Prototypes, and Open Challenges
94
0
0.0
(
0
)
Added by
Bonhong Koo
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The Internet of Bio-nano Things is a significant development for next generation communication technologies. Because conventional wireless communication technologies face challenges in realizing new applications (e.g., in-body area networks for health monitoring) and necessitate the substitution of information carriers, researchers have shifted their interest to molecular communications (MC). Although remarkable progress has been made in this field over the last decade, advances have been far from acceptable for the achievement of its application objectives. A crucial problem of MC is the low data rate and high error rate inherent in particle dynamics specifications, in contrast to wave-based conventional communications. Therefore, it is important to investigate the resources by which MC can obtain additional information paths and provide strategies to exploit these resources. This study aims to examine techniques involving resource aggregation and exploitation to provide prospective directions for future progress in MC. In particular, we focus on state-of-the-art studies on multiple-input multiple-output (MIMO) systems. We discuss the possible advantages of applying MIMO to various MC system models. Furthermore, we survey various studies that aimed to achieve MIMO gains for the respective models, from theoretical background to prototypes. Finally, we conclude this study by summarizing the challenges that need to be addressed.
rate research
Read More
Significant inter-symbol interference (ISI) challenges the achievement of reliable, high data-rate molecular communication via diffusion. In this paper, a hybrid modulation based on pulse position and concentration is proposed to mitigate ISI. By exploiting the time dimension, molecular concentration and position modulation (MCPM) increases the achievable data rate over conventional concentration and position-based modulations. In addition, unlike multi-molecule schemes, this hybrid scheme employs a single-molecule type and so simplifies transceiver implementations. In the paper, the optimal sequence detector of the proposed modulation is provided as well as a reduced complexity detector (two-stage, position-concentration detector, TPCD). A tractable cost function based on the TPCD detector is proposed and employed to optimize the design of the hybrid modulation scheme. In addition, the approximate probability of error for the MCPM-TPCD system is derived and is shown to be tight with respect to simulated performance. Numerically, MCPM shows improved performance over standard concentration and pulse position-based schemes in the low transmission power and high bit-rate regime. Furthermore, MCPM offers increased robustness against synchronization errors.
Terahertz (THz) communications are regarded as a pillar technology for the sixth generation (6G) wireless systems, by offering multi-ten-GHz bandwidth. To overcome the short transmission distance and huge propagation loss, ultra-massive (UM) MIMO systems that employ sub-millimeter wavelength antennas array are proposed to enable an enticingly high array gain. In the UM-MIMO systems, hybrid beamforming stands out for its great potential in promisingly high data rate and reduced power consumption. In this paper, challenges and features of the THz hybrid beamforming design are investigated, in light of the distinctive THz peculiarities. Specifically, we demonstrate that the spatial degree-of-freedom (SDoF) is less than 5, which is caused by the extreme sparsity of the THz channel. The blockage problem caused by the huge reflection and scattering losses, as high as 15 dB or over, is studied. Moreover, we analyze the challenges led by the array containing 1024 or more antennas, including the requirement for intelligent subarray architecture, strict energy efficiency, and propagation characterization based on spherical-wave propagation mechanisms. Owning up to hundreds of GHz bandwidth, beam squint effect could cause over 5~dB array gain loss, when the fractional bandwidth exceeds 10%. Inspired by these facts, three novel THz-specific hybrid beamforming architectures are presented, including widely-spaced multi-subarray, dynamic array-of-subarrays, and true-time-delay-based architectures. We also demonstrate the potential data rate, power consumption, and array gain capabilities for THz communications. As a roadmap of THz hybrid beamforming design, multiple open problems and potential research directions are elaborated.
Terahertz (THz) communications have been envisioned as a promising enabler to provide ultra-high data transmission for sixth generation (6G) wireless networks. To tackle the blockage vulnerability brought by severe path attenuation and poor diffraction of THz waves, an intelligent reflecting surface (IRS) is put forward to smartly control the incident THz waves by adjusting the phase shifts. In this paper, we firstly design an efficient hardware structure of graphene-based IRS with phase response up to 306.82 degrees. Subsequently, to characterize the capacity of the IRS-enabled THz multiple-input multiple-output (MIMO) system, an adaptive gradient descent (A-GD) algorithm is developed by dynamically updating the step size during the iterative process, which is determined by the second-order Taylor expansion formulation. In contrast with conventional gradient descent (C-GD) algorithm with fixed step size, the A-GD algorithm evidently improves the achievable rate performance. However, both A-GD algorithm and C-GD algorithm inherit the unacceptable complexity. Then a low complexity alternating optimization (AO) algorithm is proposed by alternately optimizing the precoding matrix by a column-by-column (CBC) algorithm and the phase shift matrix of the IRS by a linear search algorithm. Ultimately, the numerical results demonstrate the effectiveness of the designed hardware structure and the considered algorithms.
In diffusion-based communication, as for molecular systems, the achievable data rate is low due to the stochastic nature of diffusion which exhibits a severe inter-symbol-interference (ISI). Multiple-Input Multiple-Output (MIMO) multiplexing improves the data rate at the expense of an inter-link interference (ILI). This paper investigates training-based channel estimation schemes for diffusive MIMO (D-MIMO) systems and corresponding equalization methods. Maximum likelihood and least-squares estimators of mean channel are derived, and the training sequence is designed to minimize the mean square error (MSE). Numerical validations in terms of MSE are compared with Cramer-Rao bound derived herein. Equalization is based on decision feedback equalizer (DFE) structure as this is effective in mitigating diffusive ISI/ILI. Zero-forcing, minimum MSE and least-squares criteria have been paired to DFE, and their performances are evaluated in terms of bit error probability. Since D-MIMO systems are severely affected by the ILI because of short transmitters inter-distance, D-MIMO time interleaving is exploited as countermeasure to mitigate the ILI with remarkable performance improvements. The feasibility of a block-type communication including training and data equalization is explored for D-MIMO, and system-level performances are numerically derived.
In a practical massive MIMO (multiple-input multiple-output) system, the number of antennas at a base station (BS) is constrained by the space and cost factors, which limits the throughput gain promised by theoretical analysis. This paper thus studies the feasibility of adopting the intelligent reflecting surface (IRS) to further improve the beamforming gain of the uplink communications in a massive MIMO system. Under such a novel system, the central question lies in whether the IRS is able to enhance the network throughput as expected, if the channel estimation overhead is taken into account. In this paper, we first show that the favorable propagation property for the conventional massive MIMO system without IRS, i.e., the channels of arbitrary two users are orthogonal, no longer holds for the IRS-assisted massive MIMO system, due to its special channel property that each IRS element reflects the signals from all the users to the BS via the same channel. As a result, the maximal-ratio combining (MRC) receive beamforming strategy leads to strong inter-user interference and thus even lower user rates than those of the massive MIMO system without IRS. To tackle this challenge, we propose a novel strategy for zero-forcing (ZF) beamforming design at the BS and reflection coefficients design at the IRS to efficiently null the inter-user interference. Under our proposed strategy, it is rigorously shown that even if the channel estimation overhead is considered, the IRS-assisted massive MIMO system can always achieve higher throughput compared to its counterpart without IRS, despite the fact that the favorable propagation property no longer holds.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
