Abstract
In this paper, we derive a closed-form expression for the evaluation of the average vectorsymbol error rate (VER) of group detection followed by maximum-likelihood (GD-ML) receivers in large multi-user multiple-input multiple-output (MU-MIMO) systems with transmit side correlated Rayleigh channels. We assume M antennas at the base station (BS), N closely-located, single-antenna user equipment (UEs) with load factor λ = N/M , and N ≫ 1; consequently, we evaluate the performance of GD-ML receivers as the load factor grows to unity. The derived expression requires a negligible correlation at the receive side of the communication channel. Hence, from a practical point-of-view, when considering scenarios with a large number of UEs, the derived analytical expression is generally more applicable for systems with a distributed massive number of BS antennas. Numerical results are provided to validate our derived expression. We observe that the GD-ML with Nu group size achieves a diversity order proportional to M - N + Nu. Moreover, we show that for small group sizes, the analytical and simulation results remain close, and at moderate to high signal-to-noise ratio (SNR), the derived expression very closely matches the simulations, whereas this match becomes perfect as the users' side correlation increases. We also demonstrate that GD-ML outperforms the zero-forcing (ZF) and minimum mean-squared error (MMSE) receivers, in terms of VER; where for high λ, GD-ML exploits the maximum spatial multiplexing gain. Moreover, in terms of floating-point operations (FLOPs), we show that GD-ML receivers have almost the same complexity as ZF and MMSE where the ML detection stage adds a negligible complexity compared to the channel matrix inversion operation.
Highlights
Massive multiple-input multiple-output (MIMO) is a key technological enabler for the high throughput anticipated by 5G wireless communication networks
In terms of floating-point operations (FLOPs), we show that group detection followed by maximum-likelihood (GD-maximum likelihood (ML)) receivers have almost the same complexity as ZF and minimum mean-squared error (MMSE) where the ML detection stage adds a negligible complexity compared to the channel matrix inversion operation
We evaluate the complexity of the Group detection (GD)-ML, ZF, MMSE, and ordered successive interference cancellation (OSIC) receivers in terms of floating-point operations (FLOPs), where we show that the GD-ML algorithm has approximately the same level of complexity as the other ZF and MMSE linear detectors
Summary
Massive MIMO is a key technological enabler for the high throughput anticipated by 5G wireless communication networks. Solution believed to be capable of serving many users simultaneously using the same time-frequency resource [1]–[3]. This spatial-multiplexing capability is realized due to a large number of antennas, which for many practical reasons, cannot be assumed infinite. In such systems, the load factor, λ, is defined as the ratio of the total number of UEs to the number of receive antennas at the BS. With the constantly increasing number of connected devices, systems that can handle high load factors to accommodate the maximum possible users in the network are becoming highly desirable
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have