Abstract
In frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, the overhead of downlink channel estimation is complex in terms of the pilot overhead, the calculation cost and the estimation process. In this paper, we propose a low-complexity downlink channel estimation scheme based on compressed sensing for mobile multi-antenna terminals. In the scheme, the mobile terminal estimates the downlink massive MIMO channel and utilises the characteristics of the spatial sparsity of the massive MIMO channel to reduce the feedback overhead by feeding back the nonzero value of the sparse channel. Specifically, we propose a low-complexity estimation algorithm based on compressive sensing for multi-antenna terminals to reduce the computational overhead of the terminal. Since different antennas of a terminal share the same support set, the algorithm estimates multiple indices per iteration, collecting the estimated indices of different antennas at the end of each iteration, thereby reducing the total number of iterations of the algorithm. Then, we derive a halting condition for a greedy algorithm that stops the iteration process according to the residual energy. The simulation results illustrate the efficiency of the halting condition for the greedy algorithm and the low complexity of the proposed algorithm. In contrast to different greedy algorithms and the Bayesian algorithm, the proposed algorithm has a complexity that decreases as the number of terminal antennas.
Highlights
With the popularity of smartphones and the rise of the Internet of Things (IoT), data traffic transmitted over wireless networks has grown exponentially every year [1]
This paper proposes a low-complexity channel estimation algorithm for an frequency division duplexing (FDD) large-scale multiple-input multiple-output (MIMO) system with multi-antenna terminals
The main contributions of this paper are as follows: 1) We propose a low-complexity channel estimation algorithm that utilises the sparsity property of a large-scale MIMO channel and the multi-antenna property of terminals
Summary
With the popularity of smartphones and the rise of the Internet of Things (IoT), data traffic transmitted over wireless networks has grown exponentially every year [1]. This paper proposes a low-complexity channel estimation algorithm for an FDD large-scale MIMO system with multi-antenna terminals. The main contributions of this paper are as follows: 1) We propose a low-complexity channel estimation algorithm that utilises the sparsity property of a large-scale MIMO channel and the multi-antenna property of terminals. 3) We validate the proposed algorithm and halting threshold in utilising the sparsity of the channel and the property of multi-antenna terminals while reducing the time consumption of the estimation compared with that of other greedy algorithms and the Bayesian algorithm. The obtained Hk is the estimation of the large-scale MIMO channel matrix Hk according to Yk. The channel between the BS and the kth terminal is expressed as follows [29], [30]: θk + d φk + a. From the view of a terminal, scatterers surround the terminal, and the terminal receives rays from all directions, with a ≈ π
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