تشخیص چند ورودی چند خروجی بهینه
ABSTRACT
I. INTRODUCTION
II. PRELIMINARIES AND SYSTEM MODEL
III. SIMULATIONS
IV. FUTURE RESEARCH DIRECTIONS
V. CONCLUSION
REFERENCES
ABSTRACT
Being increasingly spectral and energy efficient, massive multiple-input multiple-output (MIMO) is envisaged as a potential technology for fifth generation (5G) wireless communication networks. Radio spectrum has become a scarce resource in wireless communications and consequently imposes excessive cost on the high data rate transmission. Several linear and non linear detection techniques such as Zero-Forcing (ZF), Minimum Mean Square Error (MMSE), and Vertical Bell-Labs Layered Space Time (VBLAST) have been introduced. The purpose of such schemes is to mitigate the signal detection problems which are based on trade-offs between the bit error rate (BER) performance and computational complexities. The challenge in the design of massive-MIMO systems is developing less complex and efficient detection algorithms. The problem in building a receiver for massive-MIMO is to de-correlate the spatial signatures on the receiver antenna array. In this paper, we propose a novel algorithm viz: Hybrid n-Bit Heuristic AssistedVBLAST (HHAV) to perform an optimum decoding. We have simulated this structure in dynamic Rayleigh fading channel. We have also evaluated the AMP algorithm with two threshold functions which include AMP with ternary distribution (AMPT) and AMP with Gaussian distribution (AMPG). Numerical results confirm that HHAV algorithm performs significantly better than the in vogue aforementioned detection systems as introduced in recent years.
INTRODUCTION
Nowadays in wireless communication systems, massive MIMO has become the subject of an extensive research due to its potential to support higher data rate as compared to their counterpart single-input single-output (SISO) [1]–[3]. Hence, massive-MIMO systems with tens to hundreds of antennas at the base-station (BS) have gained the attention of researchers [2], [4]–[11]. A few hurdles, however, remain to be overcome to achieve optimum advantages offered by massive MIMO systems. One of them is to model a less computational and reliable detection system without compromising performance. The motivation to consider massive MIMO systems is their potential to meet the growing demands for higher throughput as the volume, velocity, and variety of data due to exponential augmentation in mobile users and communication networks [12]. The ever-increasing number of users in various mobile networks along with the additional enabling connectivity of the mobile services [13], [14], has necessitated the 5G technologies [15]. The aim of 5G is to provide the much-needed capacity, increased data rates, reduced latency, and improved quality of service as compared to previous technologies. In massive-MIMO, detection is a complicated process. It requires an extensive search over the space in order to find the closest received symbols in terms of euclidean distances for an optimal solution. However, these procedures are rendered unfeasible for larger systems such as massive MIMO systems.