Time Division Duplex Systems Research Articles

GRU Based MCS Selection in Tactical Vehicle Communication

In this paper, we propose optimal modulation coding scheme (MCS) selection based on Gated Recurrent Unit (GRU) for one-to-one communication between tactical vehicles. The communication between tactical vehicles assumes orthogonal frequency division multiplexing (OFDM) and performs bidirectional communication with time division duplexing (TDD) manner. Since the TDD system uses the same frequency for transmitting and receiving, the bidirectional communication channels are the same. Based on the Signal-to-Noise Ratio (SNR) measuring from the received signal, the MCS at the future transmission time is predicted, utilizing a Gated Recurrent Unit (GRU), which is a type of Recurrent Neural Network (RNN). Existing methods for predicting the MCS from the received SNR include the mean value method and the recent value method, and the method based on the convolutional neural network (CNN). Based on the computer simulation results, the proposed GRU-based RNN technique shows a lower outage probability of communication than all conventional methods while provides the highest throughput.

Cross-link interference in the 5G cell search procedure: Observation and field experiments for mitigation

This paper presents field experiment results that illustrate the impact of cross-link interference on the cell search procedure in a 5G time division duplex system. We initially show how cross-link interference affects to the cell search procedure from an analytic aspect. An example of an experimental result provided in this paper shows how the user equipment wrongly estimates the symbol timing offset due to cross-link interference. Furthermore, we propose a modified cell search algorithm which is robust against the strong influence of cross-link interference. The proposed modification efficiently suppresses the effects of cross-link interference by applying selective energy normalization to the correlation process. The experimental results show that the modified algorithm can accurately estimate the symbol timing offset with a negligible increment in computation time.

Reinforcement learning-based energy efficiency optimization for RIS-Assisted UAV hybrid uplink and downlink system

As Reconfigurable Intelligent Surface (RIS) technology can intelligently reflect the incident signal, it can be deployed to maintain a line-of-sight connection and improve the signal strength between unmanned Aerial Vehicles (UAV) and users. Current researches on RIS-assisted UAV communication typically consider optimizing data rates for single-line communications and do not focus on the energy consumption of the UAV. Furthermore, it is frequently challenging to apply conventional algorithms to RIS-assisted UAV optimization problems in a timely way, and the two-dimensional trajectory design cannot fully utilize the 3D mobility of the UAV. In order to increase the data rates for all ground users while improving the energy efficiency of the system, we consider a RIS-assisted full-duplex UAV communication system, in which a DDQN-based algorithm is proposed to jointly optimize the phase shift of the RIS and the 3D trajectory of the UAV. Simulation results demonstrate that the proposed algorithm can achieve a significant data rate gain and energy efficiency compared to time-division duplex systems and other deep reinforcement learning algorithms.

On autoregressive and neural methods for massive-MIMO channel de-noising

In modern wireless communication systems, the Multiple-Input Multiple-Output (MIMO) technology allows to greatly increase power efficiency, the serving area, and the overall cell throughput through the use of the antenna array beamforming. Nevertheless, the MIMO systems require accurate channel state knowledge to apply correct precoding. In 5G Time Division Duplex (TDD) systems, the Channel State Information (CSI) is obtained via Sounding Reference Signals (SRS) transmitted by the User Equipment (UE). UEs have limited power capabilities and thus cannot achieve high Uplink (UL) Signal-to-Noise Ratio (SNR) on gNodeB (gNB) in large bandwidth. There are multiple techniques that can be applied to improve the accuracy of Channel Estimation (CE) in noisy conditions. In this paper, we describe a classical method, namely the Vector Autoregression (VAR) with adaptive model order estimation, as well as a modern Deep Neural Network (DNN) approach for the massive-MIMO channel estimation de-noising problem. The developed methods and signal pre and postprocessing steps are described, followed by their performance evaluation in a set of realistic simulations. The designed algorithms provide results outperforming the baseline spatio-temporal windowing approaches by approximately equal to 2dB effective Downlink (DL) Signal-to-Interference-plus-Noise Ratio (SINR) metric in single and multi-user MIMO scenarios. Extensive simulation results demonstrate the robustness of the developed methods to the dynamic channel conditions.

Improved TDD operation on Software-Defined Radio platforms towards future wireless standards

Software-Defined Radio (SDR) platforms are valuable for research and development activities or high-end systems that demand flexible wireless protocols. While low-latency digital baseband processing can be achieved using a dedicated processing unit, like an FPGA or hardware accelerator, its multi-purpose Radio Frequency (RF) front-end often poses a limitation. Zero Intermediate Frequency (ZIF) transceivers are favorable for SDR, however, even for Time Division Duplex (TDD) systems, these transceivers suffer from self-interference when the transmitting and receiving Local Oscillator (LO) is set to the same frequency. To achieve low self-interference, switching from receiving to transmitting mode is needed. However, the time this takes (turnaround time, TT) for configurable RF front-ends often violates the strict timing requirements of protocols like Wi-Fi and 5G, which require response times in the order of microseconds. In this work, we first evaluate the advantages and disadvantages of several methods to suppress self-interference of a ZIF transceiver. Next, a novel approach is proposed, which can reduce the TT to as low as 640ns using the widely used AD9361 configurable ZIF RF front-end, while the noise floor is at the same level as achieved by the conventional way of switching between transmit and receive mode. We have realized and validated this approach using openwifi — an open-source Wi-Fi implementation on SDR. As a result, the receiver sensitivity is improved by up to 17dB in the 2.4GHz band and 9.5dB in the 5GHz band, for over-the-air transmissions.

Transmit Antenna Selection Based on SNR prediction in TDD Systems Using Convolutional Neural Network

This paper proposes a method for predicting future signal-to-noise ratio (SNR) in a time-division-duplexing (TDD) mobile communication environments using a convolutional neural network (CNN). The communication system uses multiple receive antennas and transmit using only one or two antennas among them. A CNN model is proposed to predict the SNR at a future transmission time based on past SNRs received from multiple antennas. The probability of reception at a certain is set to 10-100%. In case that SNR cannot be measured due to the absence of reception, linear interpolation is performed using two adjacent recorded SNRs. If even two adjacent SNRs do not exist, the SNR is set to 0dB. Comparing the predicted SNRs at multiple antennas, the antenna with the highest SNR value is selected for future transmission. To verify antenna selection accuracy, computer simulation is conducted. The simulation results substantiate the superiority of the proposed method over conventional method in single antenna selection. Regarding multi-antenna selection, the proposed method demonstrates diminished accuracy relative to conventional methods at lower speeds. Nevertheless, a comprehensive evaluation considering the root mean square error (RMSE) demonstrates the overall superiority of the proposed method across all speeds.

MCS Selection Based on Convolutional Neural Network in TDD System

In this paper, a convolutional neural network (CNN) is proposed for selecting modulation and coding schemes (MCSs) at the time of future transmission in time-division-duplex (TDD) systems. The proposed method estimates the signal-to-noise ratio (SNR) obtained by the average of the equalizer’s output in the orthogonal frequency division multiplexing (OFDM) system and records it to select the most suitable MCS for future transmission. Two methods are proposed: one that directly selects an MCS and one that predicts the SNR first before selecting an MCS. The conventional method commonly used is to select an MCS based on the SNR of the most recently received signal. Computer simulations show that the outage probability and throughput of all proposed methods (direct and indirect) are superior to conventional methods (recent value). Shorter SNR sampling periods perform better than longer ones, and the accuracy of MCS selection decreases as mobile speed increases

Reconfigurable Intelligent Surface for FDD Systems: Design and Optimization

Reconfigurable intelligent surface (RIS) has recently emerged as a promising technology for wireless communications, which intelligently controls the phase shift of each unit cell to form desired beams. Most prior works on RIS consider time-division duplexing (TDD) systems, in which the same phase shifts can be applied to both uplink and downlink due to the channel reciprocity. However, for frequency-division duplexing (FDD) systems, using the same phase shifts will result in beam misalignment, thereby leading to performance degradation. To address this issue, in this paper, we study the practical RIS design and beamforming optimization for FDD systems. By representing the phase shifts of RIS with the equivalent circuit model which includes the resistance, inductances, and tunable capacitance, we propose a methodology to design the circuit parameters (i.e., inductances and capacitance) to meet the desired reflection requirements (i.e., phase tuning range, reflectivity, and zero phase slope) of both the uplink and downlink transmissions in FDD systems. Given the designed inductances, a practical binary RIS reflection model corresponding to two reflection states is then proposed. Furthermore, based on the proposed reflection model, a problem is formulated to jointly optimize the active and passive beamforming such that the minimum array response gain of the uplink and the downlink is maximized. An efficient iterative algorithm is proposed to obtain a suboptimal solution. Simulation results show that our proposed RIS design outperforms those benchmarks which design the circuits by only optimizing either uplink or downlink.

Heterogeneous Doppler Spread-Based CSI Estimation Planning for TDD Massive MIMO

Massive multi-input multi-output (MIMO) has been recognized as a key technology for fifth generation (5G) networks. Indeed, it enables to meet the challenging requirements of increased coverage, capacity and massive connectivity. Nevertheless, its performance is limited by channel estimation overhead which scales in time division duplexing (TDD) systems with the number of active users, consequently limiting the system’s efficiency. In this paper, we address the bottleneck of channel estimation in TDD Massive MIMO through optimized lean carrier. We propose an adaptive uplink training scheme that exploits the heterogeneous Doppler spreads of the different users in order to reduce the periodicity of uplink sounding signal transmission. The idea is to enable the network to plan its uplink training decisions for long time periods while taking into consideration user mobility. To this end, we formulate a two time-scale control problem that takes into account the different rates of the wireless channel and location changes. In the fast time scale, an optimized uplink training policy is derived based on estimated user locations. In the slow time scale, positioning decisions are optimized. Simulation results show that the optimized training policies provide considerable improvement in the system efficiency even with partial location knowledge.

Intelligent Reflecting Surface-Assisted Physical Layer Key Generation with Deep Learning in MIMO Systems

Physical layer secret key generation (PLKG) is a promising technology for establishing effective secret keys. Current works for PLKG mostly study key generation schemes in ideal communication environments with little or even no signal interference. In terms of this issue, exploiting the reconfigurable intelligent reflecting surface (IRS) to assist PLKG has caused an increasing interest. Most IRS-assisted PLKG schemes focus on the single-input-single-output (SISO), which is limited in future communications with multi-input-multi-output (MIMO). However, MIMO could bring a serious overhead of channel reciprocity extraction. To fill the gap, this paper proposes a novel low-overhead IRS-assisted PLKG scheme with deep learning in the MIMO communications environments. We first combine the direct channel and the reflecting channel established by the IRS to construct the channel response function, and we propose a theoretically optimal interaction matrix to approach the optimal achievable rate. Then we design a channel reciprocity-learning neural network with an IRS introduced (IRS-CRNet), which is exploited to extract the channel reciprocity in time division duplexing (TDD) systems. Moreover, a PLKG scheme based on the IRS-CRNet is proposed. Final simulation results verify the performance of the PLKG scheme based on the IRS-CRNet in terms of key generation rate, key error rate and randomness.

Deep Learning (DL)-Based Channel Prediction and Hybrid Beamforming for LEO Satellite Massive MIMO System

Low-Earth orbit (LEO) satellites are recognized as one of the most promising infrastructures for realizing global Internet of Things (IoT) services. With the explosive growth of user terminals (UTs) and data traffic, the integration of massive multiple-input multiple-output (mMIMO) techniques and LEO satellite communication systems has been regarded as a novel idea to enhance system capacity and realize global seamless high-speed interconnection. However, obtaining effective downlink channel state information (CSI) and establishing a simple and efficient hybrid beamforming mechanism are challenging tasks due to the limitations of objective factors, such as high dynamic, long delay, and low payload in LEO satellite scenarios. It is embodied in three aspects: 1) the untenable channel reciprocity in time division duplex (TDD) systems; 2) the training feedback costs and feedback delay in frequency division duplex (FDD) systems; and 3) the complex nonconvex optimization process faced by hybrid beamforming design. Driven by the performance advantages of the deep learning (DL) technology to deal with various problems in the field of physical layer communications, this article proposes to use a deep neural network (DNN) to solve the above challenges, and constructs SatCP and SatHB schemes for realizing downlink CSI acquirement and hybrid beamforming design, respectively. By deeply mining the potential correlation of the uplink-downlink channels between LEO satellites and UTs and exploring the mapping relationship between CSI and beamformers, the SatCP can assist LEO satellites to directly predict the future downlink CSI based on the observed uplink CSI with no need for downlink channel estimation, while the SatHB can easily generate the corresponding beamformers based on the downlink CSI predicted by the SatCP without requiring complex optimization. Numerical results demonstrate that the proposed SatCP and SatHB can play an effective auxiliary role in LEO satellite mMIMO communication systems.

A Gradual Method for Channel Non-Reciprocity Calibration in Cell-Free Massive MIMO

This letter proposes a method for gradually calibrating channel non-reciprocity in an ultra-dense cell-free massive multiple-input multiple-output (MIMO) system. Many studies assume perfect channel reciprocity in time division duplex (TDD) systems. However, reciprocity is not valid in actual channels. Therefore, determining the reciprocity-calibrated precoding matrix is important for the performance of the network. In a cell-free massive MIMO system, user equipment (UE) is served by many access nodes (ANs). Since there are many nodes, it is difficult to calibrate the matrix simultaneously. Using the proposed method, the network sequentially calibrates the precoding matrix in order of influence. Thus, the proposed method can obtain a large gain compared to a system without calibration by performing only minimal channel calibration. To show the capacity gain of the proposed method, a simulation was performed considering the overhead required for channel calibration.

Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems With Implicit CSI

In an aerial hybrid massive multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) system, how to design a spectral-efficient broadband multi-user hybrid beamforming with a limited pilot and feedback overhead is challenging. To this end, by modeling the key transmission modules as an end-to-end (E2E) neural network, this paper proposes a data-driven deep learning (DL)-based unified hybrid beamforming framework for both the time division duplex (TDD) and frequency division duplex (FDD) systems with implicit channel state information (CSI). For TDD systems, the proposed DL-based approach jointly models the uplink pilot combining and downlink hybrid beamforming modules as an E2E neural network. While for FDD systems, we jointly model the downlink pilot transmission, uplink CSI feedback, and downlink hybrid beamforming modules as an E2E neural network. Different from conventional approaches separately processing different modules, the proposed solution simultaneously optimizes all modules with the sum rate as the optimization object. Therefore, by perceiving the inherent property of air-to-ground massive MIMO-OFDM channel samples, the DL-based E2E neural network can establish the mapping function from the channel to the beamformer, so that the explicit channel reconstruction can be avoided with reduced pilot and feedback overhead. Besides, practical low-resolution phase shifters (PSs) introduce the quantization constraint, leading to the intractable gradient backpropagation when training the neural network. To mitigate the performance loss caused by the phase quantization error, we adopt the transfer learning strategy to further fine-tune the E2E neural network based on a pre-trained network that assumes the ideal infinite-resolution PSs. Numerical results show that our DL-based schemes have considerable advantages over state-of-the-art schemes.

Dual-Band Filtering Switches Using Multimode Dielectric Resonators With Hybrid Boundaries

Miniaturized dual-band filtering switches are proposed in this article using multimode dielectric resonators (DRs). The planar printed circuit boards (PCBs) are utilized not only to construct the hybrid electromagnetic boundary for quad-mode DR, but also to offer the switchable multimode feeding structures with the p-i-n diodes. When the diodes are reversely biased, all the four modes are excited simultaneously by the mixed electric and magnetic couplings and controlled flexibly to construct the dual-band response. In this manner, extra losses from diodes are avoided, and low-loss transmission is realized. When the diodes are forward biased, all the four modes are shut off at the same time. Owing to the used mixed electric and magnetic couplings, the isolation is enhanced further by the cancellation of the electric and magnetic couplings between the feeding line and the resonant modes. For demonstration, both dual-mode and quad-mode filtering switches with dual-band operations are fabricated and tested. With only one quad-mode DR, high performance, including low-loss dual-band transmission and high-level isolation, is observed in the measurement, making the proposed switches promising in high-standard multiband time division duplex (TDD) systems.

Performance Analysis of Cell-Free Massive MIMO System with Network-Assisted Full-Duplex under Time-Shifting Pilot Scheme

A network-assisted full-duplex (NAFD) system based on a cell-free (CF) massive multiple-input, multiple-output (MIMO) framework has been proposed to satisfy the demands of higher data transmission rates and efficient communication. However, pilot contamination may occur due to the reuse of pilot sequences in a massive MIMO. With this consideration, we raise an asynchronous channel estimation method based on an uplink and downlink time-shifting pilot-sending scheme, which is able to avoid pilot sequence reuse when obtaining channel state information (CSI), while the data signals could be transmitted simultaneously at the same frequency. The transmission processes of the proposed method above are divided into three phases, including pilot phase, estimation phase, and data phase, in chronological order. When the uplink is in pilot phase, the corresponding downlink is in data phase and vice versa. After the channel state information estimation, both uplinks and downlinks are in data phase. The maximum ratio combination (MRC) receiver and the maximum ratio transmission (MRT) precoding are adopted in the uplink and downlink. The closed-form expressions are derived based on large-scale random matrix theory. We compared our asymptotic results with practical results in simulation, and find that they are well matched. Moreover, the proposed method is superior to the normal time-division duplex (TDD) system.

Frequency Domain Detection and Precoding for Massive MIMO With Single Carrier Modulation

Single carrier modulation (SCM) schemes are attractive for uplink (UL) transmissions due to improved power efficiency at the user equipment (UE) transmitter compared with multi-carrier modulation schemes. In a massive MIMO scenario with SCM, the UL detection must mitigate the effects of inter-symbol interference and multi-user interference. This processing is effectively performed in the frequency domain (FD) using a minimum mean squared error (MMSE) detector when the transmission is framed with a cyclic prefix. This paper analyzes an MMSE-based detector compatible with distributed processing in a time-division duplex (TDD) system. The matrix inverses computed for the UL detection are then reused to perform multi-user precoding for the downlink (DL). We find that this scheme yields tremendous savings in computational complexity compared to commonly used zero-forcing (ZF) precoding without sacrificing any performance. Since MMSE processing introduces a bias to the estimates, we derive the scalar coefficients necessary to cancel the MMSE bias. The impact of channel estimation errors is analyzed for both the UL and DL cases in conjunction with a power-efficient approach to SCM channel estimation. Moreover, extensive simulations are performed to confirm our theoretical findings.

Deep Learning-Based Channel Reciprocity Learning for Physical Layer Secret Key Generation

Using the physical layer channel information of wireless devices to establish the highly consistent secret keys is a promising technology for improving the security of wireless networks. Nevertheless, in the time division duplex system, the reciprocity of the wireless channel that is the basic principle of key generation is impaired by nonsimultaneous sampling and noise factors. Existing physical layer key generation approaches rely on hand-crafted feature extraction algorithms, which have high overhead or security issues and are impractical in real-world situations. This paper presents a novel physical layer key generation method to extract highly consistent keys from imperfect channel responses, which exploits channel reciprocity through deep learning. Specifically, we first design the Channel Reciprocity Learning Net (CRLNet), a neural network for efficiently learning channel reciprocity features from the wireless channel in TDD OFDM systems. Later, a new key generation scheme based on CRLNet is developed that can achieve a high key agreement rate. Experiments indicate that the CRLNet-based key generation scheme performs excellently in terms of key generation rate, key error rate, and randomness, confirming that our method has better performance and lower overhead than existing methods.

Compressive Sensing-Based Secure Uplink Grant-Free Systems

Compressive sensing (CS) has been extensively employed in uplink grant-free communications, where data generated from different active users are transmitted to a base station (BS) without following the strict access grant process. Nevertheless, the state-of-the-art CS algorithms rely on a highly limited category of measurement matrix, that is, pilot matrix, which may be analyzed by an eavesdropper (Eve) to infer the user’s channel information. Thus, the physical layer security becomes a critical issue in uplink grant-free communications. In this article, the channel reciprocity in time-division duplex systems is utilized to design environment-aware (EA) pilots derived from transmission channels to prevent eavesdroppers from acquiring users’ channel information. The simulation results show that the proposed EA-based pilot approach possesses a high level of security by scrambling the Eve’s normalized mean square error performance of channel estimation.

Physical Layer Security in an OFDM Time Reversal SISO Communication With Imperfect Channel State Information

A frequency domain time-reversal (TR) precoder is proposed to perform physical layer security in single-input single-output (SISO) systems using orthogonal frequency-division multiplexing (OFDM) and artificial noise (AN) injection. This scheme guarantees the secrecy of a communication towards a legitimate user, Bob, by exploiting the frequency diversity selective behaviour in multipath channels. The transmitter, Alice, has imperfect channel state information (CSI) of the legitimate link thanks to the channel reciprocity in time division duplex systems and does not know the instantaneous CSI of a potential eavesdropper, Eve. Three optimal decoding structures at Eve are considered in a block fading environment depending on the handshake procedure between Alice and Bob. Closed-form approximations of the signal-to-noise ratio required at Bob and the maximal CSI error that can be made at Alice, in order to guarantee a communication ergodic secrecy rate (ESR), are derived. Furthermore, the optimal amount of AN energy to inject, considering imperfect CSI, is also given as a closed-form expression. A trade-off on the choice of the spreading factor of the TR precoder is established between maximizing the ESR and decreasing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {\epsilon }-$ </tex-math></inline-formula> achievable secrecy rate. Finally, thanks to these results, Alice can be a priori aware of the ESR over which she can establish a secure communication.

Coverage and Spectral Efficiency of Network Assisted Full Duplex in a Millimeter Wave System

To cope with the growing trend of asymmetric data traffic, we introduce a novel network assisted full duplex (NAFD) for a millimeter wave system. NAFD can dynamically allocate the number of remote radio heads in the uplink mode or in the downlink mode, which can facilitate simultaneous uplink and downlink communications. In this manuscript, we use stochastic geometry to analyze the distribution of the signal-to-interference-plus-noise ratio and the data rate in a NAFD system. The numerical results verify the analysis and show that the NAFD outperforms the dynamic time division duplex system and the traditional flexible duplex system in terms of spectral efficiency.