References of "He, Ke 50046996"
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See detailLearning-Based Joint Channel Prediction and Antenna Selection for Massive MIMO with Partial CSI
He, Ke UL; Vu, Thang Xuan UL; Chatzinotas, Symeon UL et al

in IEEE GLOBECOM 2022 proceedings (2022, December)

This paper investigates the massive multi-input multi-output (MIMO) system in practical deployment scenarios, in which, to balance the economic and energy efficiency with the system performance, the ... [more ▼]

This paper investigates the massive multi-input multi-output (MIMO) system in practical deployment scenarios, in which, to balance the economic and energy efficiency with the system performance, the number of radio frequency (RF) chains is smaller than the number of antennas. The base station employs antenna selection (AS) to fully harness the spatial multiplexing gain. Conventional AS techniques require full channel state information (CSI), which is time-consuming as the antennas cannot be simultaneously connected to the RF chains during the channel estimation process. To tackle this issue, we propose a novel joint channel prediction and AS (JCPAS) framework to reduce the CSI acquisition time and improve the system performance under temporally correlated channels. Our proposed JCPAS framework is a fully probabilistic model driven by deep unsupervised learning. The proposed framework is able to predict the current full CSI, while requiring only a historical window of partial observations. Extensive simulation results show that the proposed JCPAS can significantly improve the system performance under temporally correlated channels, especially for very large-scale systems with highly correlated channels. [less ▲]

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See detailEfficient Memory-Bounded Optimal Detection for GSM-MIMO Systems
He, Ke UL; He, Le; Fan, Lisheng et al

in IEEE Transactions on Communications (2022), 70(7), 4359-4372

We investigate the optimal signal detection problem in large-scale multiple-input multiple-output (MIMO) system with the generalized spatial modulation (GSM) scheme, which can be formulated as a closest ... [more ▼]

We investigate the optimal signal detection problem in large-scale multiple-input multiple-output (MIMO) system with the generalized spatial modulation (GSM) scheme, which can be formulated as a closest lattice point search (CLPS). To identify invalid signals, an efficient pruning strategy is needed while searching on the GSM decision tree. However, the existing algorithms have exponential complexity, whereas they are infeasible in large-scale GSM-MIMO systems. In order to tackle this problem, we propose a memory-efficient pruning strategy by leveraging the combinatorial nature of the GSM signal structure. Thus, the required memory size is squared to the number of transmit antennas. We further propose an efficient memory-bounded maximum likelihood (ML) search (EM-MLS) algorithm by jointly employing the proposed pruning strategy and the memory-bounded best-first algorithm. Theoretical and simulation results show that our proposed algorithm can achieve the optimal bit error rate (BER) performance, while its memory size can be bounded. Moreover, the expected time complexity decreases exponentially with increasing the signal-to-noise ratio (SNR) as well as the system’s excess degree of freedom, and it often converges to squared time under practical scenarios. [less ▲]

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See detailToward Optimally Efficient Search With Deep Learning for Large-Scale MIMO Systems
He, Le; He, Ke UL; Fan, Lisheng et al

in IEEE Transactions on Communications (2022), 70(5), 3157-3168

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by ... [more ▼]

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the decision tree. Unfortunately, the existing optimal search algorithms often involve prohibitively high complexities, which indicates that they are infeasible in large-scale MIMO systems. To address this issue, we propose a general heuristic search algorithm, namely, hyper-accelerated tree search (HATS) algorithm. The proposed algorithm employs a deep neural network (DNN) to estimate the optimal heuristic, and then use the estimated heuristic to speed up the underlying memory-bounded search algorithm. This idea is inspired by the fact that the underlying heuristic search algorithm reaches the optimal efficiency with the optimal heuristic function. Simulation results show that the proposed algorithm reaches almost the optimal bit error rate (BER) performance in large-scale systems, while the memory size can be bounded. In the meanwhile, it visits nearly the fewest tree nodes. This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems. Besides, the code for this paper is available at https://github.com/skypitcher/hats. [less ▲]

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See detailCollaborative Cache-Aided Relaying Networks: Performance Evaluation and System Optimization
Tang, Shunpu; He, Ke UL; Chen, Lunyuan et al

in IEEE Journal on Selected Areas In Communications (2022)

This paper studies a multi-tier cache-aided relaying network, where the destination D is randomly located in the network and it requests files from the source S through the help of cache-aided base ... [more ▼]

This paper studies a multi-tier cache-aided relaying network, where the destination D is randomly located in the network and it requests files from the source S through the help of cache-aided base station (BS) and N relays. In this system, the multi-tier architecture imposes a significant impact on the system collaborative caching and file delivery, which brings a big challenge to the system performance evaluation and optimization. To address this problem, we first evaluate the system performance by deriving analytical outage probability expression, through fully taking into account the random location of the destination and different file delivery modes related to the file caching status. We then perform the asymptotic analysis on the system outage probability when the signal-to-noise ratio (SNR) is high, to enclose some important and meaningful insights on the network. We further optimize the caching strategies among the relays and BS, to improve the network outage probability. Simulations are performed to show the effectiveness of the derived analytical and asymptotic outage probability for the proposed caching strategy. In particular, the proposed caching is superior to the conventional caching strategies such as the most popular content (MPC) and equal probability caching (EPC) strategies. [less ▲]

Detailed reference viewed: 42 (1 UL)