Multiple-input Multiple-output Setting Research Articles

Pilot Randomization to Protect MIMO Secret Key Generation Systems Against Injection Attacks

In this paper, we investigate the problem of secret key generation under an injection attack, which refers to tampering of pilot signals over the air so that part of the shared randomness observed at the legitimate parties is controlled by the adversary. It has been shown that to launch such an attack, an adversary only needs one extra antenna, compared to the legitimate parties, in a single input single output (SISO) network. In this work, we generalize this result for the multiple input multiple output (MIMO) case. Furthermore, we propose pilot randomization as a means to protect against injection attacks by reducing them to jamming attacks that constitute a less serious threat. Finally, we derive a closed-form expression for the secret key rate of the investigated MIMO setting.

MIMO Channel Estimation Using Score-Based Generative Models

Channel estimation is a critical task in multiple-input multiple-output (MIMO) digital communications that substantially effects end-to-end system performance. In this work, we introduce a novel approach for channel estimation using deep score-based generative models. A model is trained to estimate the gradient of the logarithm of a distribution and is used to iteratively refine estimates given measurements of a signal. We introduce a framework for training score-based generative models for wireless MIMO channels and performing channel estimation based on posterior sampling at test time. We derive theoretical robustness guarantees for channel estimation with posterior sampling in single-input single-output scenarios, and experimentally verify performance in the MIMO setting. Our results in simulated channels show competitive in-distribution performance, and robust out-of-distribution performance, with gains of up to 5 dB in end-to-end coded communication performance compared to supervised deep learning methods. Simulations on the number of pilots show that high fidelity channel estimation with 25% pilot density is possible for MIMO channel sizes of up to 64 × 256. Complexity analysis reveals that model size can efficiently trade performance for estimation latency, and that the proposed approach is competitive with compressed sensing in terms of floating-point operation (FLOP) count.

Dual-Band Wearable MIMO Antenna for WiFi Sensing Applications

Multiple input multiple output (MIMO) technology combined with orthogonal frequency division multiple access (OFDMA) is an enabling technology used in WiFi 6/6E (IEEE 802.11ax) to increase the throughput. With the addition of WiFi 6/6E and taking advantage of MIMO and OFDMA, many applications of wearable WiFi can be imagined. For example, WiFi can be used for tracking and fall detection. Wearable devices, such as those used in gaming, vital sign monitoring, and tracking, can also take advantage of wearable MIMO antennas. In this paper, a wearable small dual-band antenna is proposed that can be fabricated on felt or denim substrate. In the proposed antenna, a conductive layer is used as a reflector to improve the gain and reduce the sensitivity of the antenna performance to the body loading effects. The details of the design and its performance in a sample indoor MIMO setting are provided. The MIMO antenna is proposed for WiFi tracking and sensing applications. The performance of the MIMO antenna in an indoor setting is examined.

OFDM-Based Generalized Optical MIMO

The combination of multiple-input multiple-output (MIMO) transmission and orthogonal frequency division multiplexing (OFDM) modulation has been shown to be an effective way to substantially enhance the capacity of bandlimited optical wireless communication (OWC) systems. In this paper, we propose four OFDM-based generalized optical MIMO (GO-MIMO) techniques for intensity-modulation and direct-detection OWC systems, including OFDM-based frequency-domain generalized spatial modulation (FD-GSM), frequency-domain generalized spatial multiplexing (FD-GSMP), time-domain generalized spatial modulation (TD-GSM) and time-domain generalized spatial multiplexing (TD-GSMP). For OFDM-based FD-GSM and FD-GSMP, spatial mapping is performed in the frequency domain, while it is carried out in the time domain for OFDM-based TD-GSM and TD-GSMP. To efficiently estimate both spatial and constellation symbols in each OFDM-based GO-MIMO technique, a corresponding maximum-likelihood (ML) detection algorithm is designed. Extensive simulations are conducted to evaluate and compare the performance of OFDM-based GO-MIMO techniques under various MIMO settings in a typical indoor environment. Simulation results demonstrate that there exists an optimal GO-MIMO technique to achieve a target spectral efficiency for a given receiver location, and the obtained optimal GO-MIMO technique is also related to the specific location of the receiver.

Enhancing Secure MIMO Transmission via Intelligent Reflecting Surface

In this article, we consider an intelligent reflecting surface (IRS) assisted Guassian multiple-input multiple-output (MIMO) wiretap channel (WTC), and focus on enhancing its secrecy rate. Due to MIMO setting, all the existing solutions for enhancing the secrecy rate over multiple-input single-output WTC completely fall to this work. Furthermore, all the existing studies are simply based on an ideal assumption that full channel state information (CSI) of eavesdropper (Ev) is available. Therefore, we propose numerical solutions to enhance the secrecy rate of this channel under both full and no Ev's CSI cases. For the full CSI case, we propose a barrier method and one-by-one (OBO) optimization combined alternating optimization (AO) algorithm to jointly optimize the transmit covariance R at transmitter (Tx) and phase shift coefficient Q at IRS. For the case of no Ev's CSI, we develop an artificial noise (AN) aided joint transmission scheme to enhance the secrecy rate. In this scheme, a bisection search (BS) and OBO optimization combined AO algorithm is proposed to jointly optimize R and Q. Such scheme is also applied to enhance the secrecy rate under a special scenario in which the direct link between Tx and receiver (Rx)/Ev is blocked due to obstacles. In particular, we propose a BS and minorization-maximization (MM) combined AO algorithm with slightly faster convergence to optimize R and Q for this scenario. Simulation results have validated the monotonic convergence of the proposed algorithms, and it is shown that the proposed algorithms for the IRS-assisted design achieve significantly larger secrecy rate than the other benchmark schemes under full CSI. When Ev's CSI is unknown, the secrecy performance of this channel also can be enhanced by the proposed AN aided scheme, and there is a trade-off between increasing the quality of service at Rx and enhancing the secrecy rate.

Sampling and Reconstruction of Multiple-Input Multiple-Output Channels

Based on the recent development of sampling and reconstruction results for slowly time-varying single-input single-output channel operators, we derive sampling results in the multiple-input multiple-output setting where all subchannels satisfy an underspread condition, that is, their spreading functions are supported on individual sets of small measure. At the center of our work is the extension of the single-input single-output dual tiling condition to this setting; it characterizes which periodic weighted delta trains can be used to identify a given class of multiple-input multiple-output channel operators satisfying a spreading support constraint. Building on the dual tiling condition, we compute reconstruction formulas for the operator's symbol in closed form and discuss the problem of identifying multiple-input multiple-output operators where only restrictions in size, but not on location and geometry, of the subchannel spreading supports are known.

Regional Tourism Demand Forecasting with Machine Learning Models: Gaussian Process Regression vs. Neural Network Models in a Multiple-Input Multiple-Output Setting

This study presents a multiple-input multiple-output (MIMO) approach for multi-step-ahead time series prediction with a Gaussian process regression (GPR) model. We assess the forecasting performance of the GPR model with respect to several neural network architectures. The MIMO setting allows modelling the cross-correlations between all regions simultaneously. We find that the radial basis function (RBF) network outperforms the GPR model, especially for long-term forecast horizons. As the memory of the models increases, the forecasting performance of the GPR improves, suggesting the convenience of designing a model selection criteria in order to estimate the optimal number of lags used for concatenation.

Link Adaptation for MIMO OFDM Visible Light Communication Systems

In this paper, we investigate link adaptation for an orthogonal frequency division multiplexing (OFDM)-based multiple-input multiple-output (MIMO) visible light communication (VLC) system. The proposed adaptive OFDM VLC system supports both repetition coding (RC) and spatial multiplexing (SM) as MIMO modes and allows spatial mode switching based on channel conditions. Regarding to the instantaneous signal-to-noise ratio for both RC and SM modes, the maximum constellation size that can be supported for each MIMO mode on each subcarrier is determined. The MIMO mode that gives the highest spectral efficiency (SE) is then selected. The proposed joint MIMO mode selection and bit loading scheme maximizes the SE while satisfying a target bit error rate. Our numerical results reveal that a peak data rate up to 18.3 Gb/sec can be achieved in a $16 \times 16$ MIMO setting using light emitting diodes with cut-off frequency of 10 MHz in typical indoor environments.

Achieving Ergodicity in Quasi-Static MIMO With Polynomial-Time Complexity and One Bit of Feedback

This letter establishes the computational complexity savings that a properly positioned single bit of feedback can provide in the computationally intense setting of quasi-static MIMO communications. Specifically, this letter identifies novel practically constructed feedback schemes and explicit and non-random multiple-input multiple-output (MIMO) encoding-decoding schemes that, in the presence of a single bit of feedback, jointly guarantee the optimal diversity-multiplexing tradeoff (DMT) with a polynomial time complexity. Going one step further, this letter also presents an opportunistic communication scheme that, at all rates including rates close to the maximum multiplexing gain, can provide near-ergodic reliability at just polynomial time computational complexity costs. This is the best known computational complexity that suffices to achieve near-ergodic reliability in the quasi-static MIMO settings.

Channel Hardening-Exploiting Message Passing (CHEMP) Receiver in Large-Scale MIMO Systems

In this paper, we propose a multiple-input multiple-output (MIMO) receiver algorithm that exploits channel hardening that occurs in large MIMO channels. Channel hardening refers to the phenomenon where the off-diagonal terms of the H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</sup> H matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix H increases. Specifically, we propose a message passing detection (MPD) algorithm which works with the real-valued matched filtered received vector (whose signal term becomes H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> Hx, where x is the transmitted vector), and uses a Gaussian approximation on the off-diagonal terms of the H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> H matrix. We also propose a simple estimation scheme which directly obtains an estimate of H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> H (instead of an estimate of H), which is used as an effective channel estimate in the MPD algorithm. We refer to this receiver as the channel hardening-exploiting message passing (CHEMP) receiver. The proposed CHEMP receiver achieves very good performance in large-scale MIMO systems (e.g., in systems with 16 to 128 uplink users and 128 base station antennas). For the considered large MIMO settings, the complexity of the proposed MPD algorithm is almost the same as or less than that of the minimum mean square error (MMSE) detection. This is because the MPD algorithm does not need a matrix inversion. It also achieves a significantly better performance compared to MMSE and other message passing detection algorithms using MMSE estimate of H. Further, we design optimized irregular low density parity check (LDPC) codes specific to the considered large MIMO channel and the CHEMP receiver through EXIT chart matching. The LDPC codes thus obtained achieve improved coded bit error rate performance compared to off-the-shelf irregular LDPC codes.

Bidirectional MIMO Channel Tracking Based on PASTd and Performance Evaluation

We consider a bidirectional time division duplex (TDD) multiple-input multiple-output (MIMO) communication system with time-varying channel and additive white Gaussian noise (AWGN). A blind bidirectional channel tracking algorithm, based on the projection approximation subspace tracking (PAST) algorithm, is applied in both terminals. The resulting singular value decomposition (SVD) of the channel matrix is then used to approximately diagonalize the channel. The proposed method is applied to an orthogonal frequency-division multiplexing-(OFDM-)MIMO setting with a typical indoor time-domain reflection model. The computational cost of the proposed algorithm, compared with other state-of-the-art algorithms, is relatively small. The Kalman filter is utilized for establishing a benchmark for the obtained performance of the proposed tracking algorithm. The performance degradation relative to a full channel state information (CSI) due to the application of the tracking algorithm is evaluated in terms of average effective rate and the outage probability and compared with alternative tracking algorithms. The obtained results are also compared with a benchmark obtained by the Kalman filter with known input signal and channel characteristics. It is shown that the expected degradation in performance of frequency-domain algorithms (which do not exploit the smooth frequency response of the channel) is only minor compared with time-domain algorithms in a range of reasonable signal-to-noise ratio (SNR) levels. The proposed bidirectional frequency-domain tracking algorithm, proposed in this paper, is shown to attain communication rates close to the benchmark and to outperform a competing algorithm. The paper is concluded by evaluating the proposed blind tracking method in terms of the outage probability and the symbol error rate (SER) versus. SNR for binary phase shift keying (BPSK) and 4-Quadrature amplitude modulation (QAM) constellations.

Cochannel Interference Mitigation and Cooperative Processing in Downlink Multicell Multiuser MIMO Networks

Recently, the remarkable capacity potential of multiple-input multiple-output (MIMO) wireless communication systems was unveiled. The predicted enormous capacity gain of MIMO is nonetheless significantly limited by cochannel interference (CCI) in realistic cellular environments. The previously proposed advanced receiver technique improves the system performance at the cost of increased receiver complexity, and the achieved system capacity is still significantly away from the interference-free capacity upper bound, especially in environments with strong CCI. In this paper, base station cooperative processing is explored to address the CCI mitigation problem in downlink multicell multiuser MIMO networks, and is shown to dramatically increase the capacity with strong CCI. Both information-theoretic dirty paper coding approach and several more practical joint transmission schemes are studied with pooled and practical per-base power constraints, respectively. Besides the CCI mitigation potential, other advantages of cooperative processing including the power gain, channel rank/conditioning advantage, and macrodiversity protection are also addressed. The potential of our proposed joint transmission schemes is verified with both heuristic and realistic cellular MIMO settings.

A broadcast approach for a single-user slowly fading mimo channel

A broadcast transmission strategy for the slowly fading Gaussian multiple-input multiple-output (MIMO) channel is introduced. This broadcast strategy is an extension of the single-input single-output (SISO) broadcast approach. Perfect channel state information (CSI) is assumed known at the receiver end only. This strategy facilitates to adapt the reliably decoded rate to the actual channel state without having any feedback link to the transmitter. Transmission of layered coded information is motivated by the theory of majorization. We derive the basic equations characterizing achievable rates of the strategy. Several ad hoc approximations to the achievable region are considered and their performance is compared with the SISO setting and the ergodic capacity. It has been demonstrated that a single-layer outage approach is reasonably efficient in the MIMO setting in terms of the average reliably decoded rate. A multiple-access channel (MAC) broadcast approach is also applied for the MIMO case, and demonstrated to be relatively efficient.