Jing Qian

Channel knowledge map (CKM) is viewed as a digital twin of wireless channels, providing location-specific channel knowledge for environment-aware communications. A fundamental problem in CKM-assisted communications is how to construct the CKM efficiently. Current research focuses on interpolating or predicting channel knowledge based on error-free channel knowledge from measured regions, ignoring the extraction of channel knowledge. This paper addresses this gap by unifying the extraction and representation of channel knowledge. We propose a novel CKM construction framework that leverages the received signals of the base station (BS) as online and low-cost data. Specifically, we partition the BS coverage area into spatial grids. The channel knowledge per grid is represented by a set of multi-path powers, delays, and angles, based on the principle of spatial consistency. In extracting these channel parameters, the challenges lie in strong intercell interference and non-linear relationships between received signals and channel parameters. To address these issues, we formulate the problem of CKM construction into a problem of Bayesian inference, employing a interference-activity prior model to characterize the path-loss differences of interferers. Under the Bayesian inference framework, we develop a hybrid messagepassing algorithm for the interference-cancellation-based CKM construction. Based on the CKM, we obtain the joint frequency-space covariance of the user channel and design a CKM-assisted Bayesian channel estimator. The computational complexity of the channel estimator is substantially reduced by exploiting the CKM-derived covariance structure. Numerical results show that the proposed CKM provides accurate channel parameters at low signal-to-interference-plus-noise ratio (SINR) and that the CKM-assisted channel estimator significantly outperforms state-of-the-art counterparts.

How to achieve the best compromise between user experience and energy consumption has become a topic worth studying in both terminal and network sides of 6G mobile communication system. To solve this problem, this paper proposes a Deep Reinforcement Learning (DRL) based smart power saving framework. However, the state-of-art RL models face the problem of slow convergence in new environments, which seriously affects the performance of online usage. To address the generalization ability of the RL model for untrained state-action correlations, this paper proposes a Deep Variational Reinforcement Learning (DVRL) model based on Partially Observable Markov Decision Process (POMDP) and Variational Inference theory. Moreover, in order to represent more accurately the user experience, this paper have introduced the E2E-QoE model. Simulation results demonstrate that the proposed scheme exhibits generalization capability on test sets with unknown partially observable states, which achieves a 23.9% power-saving gain under good signal and 26.7% user experience gain under weak signal. Additionally, road test results indicate that the proposed scheme achieves reductions in average normalized power consumption of 0.063 and 0.117 in good and weak scenarios, respectively, compared to traditional MDP-based DRL.

The fifth-generation (5G) wireless communication signal is ideal for positioning by the user equipment (UE) due to its high precision, low cost, low latency, and easy integration. However, UE-side positioning faces significant challenges in complex electromagnetic environments. Obstacles that block the line-of-sight (LOS) path can severely impact localization accuracy. This paper proposes a deep learning architecture with two-stage inference to achieve high-precision positioning at the user side with multipath channels. For the first time, online learning is achieved without the need to retrain the neural network (NN), making it suitable for implementation on UE. Specifically, the variational inference theory is used to sense the environment and improve positioning accuracy using multipath information. This approach significantly reduces range error in non-line-of-sight (NLOS) channels. Furthermore, a new NN with a neural processes regressor (NPR) is proposed to learn the distribution of ranging bias directly from received signals. The proposed learning architecture and networks can be implemented without retraining, even under different circumstances and environments, making it more computationally efficient than most existing NNs and suitable for practical deployment. Simulation results demonstrate that the proposed approach outperforms conventional techniques with various channels.

In this paper, we present a novel multi-hop link scheduling framework that utilizes the vision perception from cameras of the road-side unit (RSU) as well as cameras of the vehicle to support the large-capacity and reliable transmission of high-speed dynamic vehicle network. Specifically, we propose a vision based link state identification method to determine whether the communications links among RSU and different vehicles are blocked or connected. We firstly utilize the 3D detection technique to obtain the vehicle spatial distribution in surrounding environment. Then, the geometric calculation is adopted to accurately analyze the link states between RSU and different vehicles. Moreover, we design an environmental statistical information based low-complexity link scheduling method, and utilize the joint statistical distribution of the residual transmission distance and the residual multi-hop latency to optimize the total transmission latency. Simulation results show that the proposed vision based link state identification method significantly outperforms the exiting methods, and the proposed link scheduling method can approximately achieve the optimal performance as that from the exhaustive search method but with much less computation overhead.

In this paper, we consider the topic of multiple-input multiple-output (MIMO) detection for coded systems. Tree search based detectors are well-behaved but have high complexity, while low-cost random sampling based detectors may suffer from distribution mismatch and performance loss. To overcome these issues, we propose a data-driven algorithm, called trained parameter based path sampling (TPbPS), to optimize parameters of the sampling distribution for random sampling based detectors. A more robust approach is designed to avoid sampling repetition and missing by derandomizing the path sampling, where paths are determined by the trained sampling distribution as well as fixed and uniformly-distributed numbers. The error probability is derived for the proposed TPbPS, and it shows that the full diversity can be achieved by elaborately designing a set of sampling path for each quantized noise level. Further, a soft-TPbPS algorithm of computing reliable soft outputs for channel decoders is proposed, via applying end-to-end Bayesian optimization to learn parameters of sampling distribution that goes straight at the objective of optimizing the block error rate performance. Combining these techniques yields enhanced soft MIMO detection designs that non-trivially advance the state-of-the-art, and provide significant performance or complexity gains over traditional methods.

Integrated sensing and communications (ISAC) has been deemed as a key technology for the sixth generation (6G) wireless communications systems. In this paper, we explore the inherent clustered nature of wireless users and design a multi-user based environment reconstruction scheme. Specifically, we first select users based on the estimation precision of channel’s multipath, including the line-of-sight (LOS) and the non-line-of-sight (NLOS) paths, to enhance the accuracy of environment reconstruction. Then, we develop a fusion strategy that merges communications signalling with camera image to increase the accuracy and robustness of environment reconstruction. The simulation results demonstrate that the proposed algorithm can achieve a remarkable sensing accuracy of centimeter level, which is about 17 times better than the scheme without user selection. Meanwhile, the fusion of communications data and vision data leads to a threefold accuracy improvement over the image only method, especially under challenging weather conditions like raining and snowing.

We propose a learning-aided soft multi-input multi-output (MIMO) detection method, consisting of a search path selection module and a symbol distribution fitting module. The search path selection module uses a neural network to generate a set of candidate paths. Different from traditional tree search algorithms, we utilize the soft estimates output by a neural network to calculate and cancel the symbol interference. The symbol distribution fitting module aims to solve the missing counter-hypotheses problem, where an additional neural network is employed to estimate the marginal posterior probability distribution of each symbol. Numerical results demonstrate the effectiveness of the proposed method especially when the allowed computational complexity is highly limited.

In order to reduce the downlink training overhead of mmWave communications, we propose a novel downlink beamforming strategy using the uplink sub-6GHz channel and downlink mmWave pilots that are sent from a few active antennas. Specifically, we design a novel dual-input neural network architecture, called FusionNet, to merge the sub-6GHz channel and the channel of a few active mmWave antennas. The proposed fusion model could intelligently adjust the attention paid (by the neural network) for sub-6GHz channel and mmWave channel by an attention mechanism. The output of the FusionNet represents the probability for each beam being the optimal one. We also propose an antenna selection model that can choose better active antennas to send the downlink pilots, in which the gradient of antenna selection vector is approximated by that of an antenna probability vector. Simulation results demonstrate the superior performance of the proposed strategy compared to the existing one that purely relies on the sub-6GHz information or compared to the shallow model that directly adds uniform pilots.

In this paper, we analyze the achievable rate for ambient backscatter communications under three different channels: the binary input and binary output (BIBO) channel, the binary input and signal output (BISO) channel, and the binary input and energy output (BIEO) channel. Instead of assuming Gaussian input distribution, the proposed study matches the practical ambient backscatter scenarios, where the input of the tag can only be binary. We derive the closed-form capacity expression as well as the capacity-achieving input distribution for the BIBO channel. To show the influence of the signal-to-noise ratio (SNR) on the capacity, a closed-form tight ceiling is also derived when SNR turns relatively large. For BISO and BIEO channel, we obtain the closed-form mutual information, while the semi-closed-form capacity value can be obtained via one dimensional searching. Simulations are provided to corroborate the theoretical studies. Interestingly, simulations show that: (i) the detection threshold maximizing the capacity of BIBO channel is the same as the one from the maximum likelihood signal detection; (ii) the maximal of the mutual information of all channels is achieved almost by a uniform input distribution; (iii) the mutual information of the BIEO channel is larger than that of the BIBO channel, but is smaller than that of the BISO channel.

Ambient Backscatter (AB), making use of both energy harvesting and backscattering, has recently become a promising solution to communications among low-power devices and demonstrates its potential application in the Internet of Things (IoT). Existing AB systems adopt two-state amplitude shift keying (ASK) or phase shift keying (PSK), where data are transmitted at the rate of one bit per symbol period. To increase the data rate, we investigate the high-order modulation where M-PSK is employed for backscattering. We derive the optimal multi-level energy detector and compute the closed-form symbol error rate (SER). To show the realizability of the proposed design, we build a 4PSK-AB hardware prototype, in which the selection of load impedance is discussed with the aid of phasor diagram illustration. The hardware prototype can achieve the date rate of 20 kbps. Besides, higher date rate is achievable for 98.7% of the time compared with binary AB communications, and the mean number of distinguishable symbols is 3.66.