No Arabic abstract
Massive MIMO, a candidate for 5G technology, promises significant gains in wireless data rates and link reliability by using large numbers of antennas (more than 64) at the base transceiver station (BTS). Extra antennas help by focusing the transmission and reception of signal energy into ever-smaller regions of space. This brings huge improvements in throughput. However, it requires a large number of Radio Frequency (RF) chains (usually equal to number of transmit antennas), which is a major drawback. One approach to overcome these issues is to use Spatial Modulation (SM). In SM, an index of transmit antenna is used as an additional source of information to improve the overall spectral efficiency. In particular, a group of any number of information bits is mapped into two constellations: a signal constellation based on modulation scheme and a spatial constellation to encode the index of the transmit antenna. However, a low spectral efficiency is main drawback of SM. Therefore, a combination of SM with Spatial Multiplexing is an effective way to increase spectral efficiency with limited number of RF chains.
In this paper, we consider the downlink of a massive multiple-input-multiple-output (MIMO) single user transmission system operating in the millimeter wave outdoor narrowband channel environment. We propose a novel receive spatial modulation architecture aimed to reduce the power consumption at the user terminal, while attaining a significant throughput. The energy consumption reduction is obtained through the use of analog devices (amplitude detector), which reduces the number of radio frequency chains and analog-to-digital-converters (ADCs). The base station transmits spatial and modulation symbols per channel use. We show that the optimal spatial symbol detector is a threshold detector that can be implemented by using one bit ADC. We derive closed form expressions for the detection threshold at different signal-to-noise-ratio (SNR) regions showing that a simple threshold can be obtained at high SNR and its performance approaches the exact threshold. We derive expressions for the average bit error probability in the presence and absence of the threshold estimation error showing that a small number of pilot symbols is needed. A performance comparison is done between the proposed system and fully digital MIMO showing that a suitable constellation selection can reduce the performance gap.
In this paper, we experimentally demonstrate a real-time software defined multiple input multiple output (MIMO) visible light communication (VLC) system employing link adaptation of spatial multiplexing and spatial diversity. Real-time MIMO signal processing is implemented by using the Field Programmable Gate Array (FPGA) based Universal Software Radio Peripheral (USRP) devices. Software defined implantation of MIMO VLC can assist in enabling an adaptive and reconfigurable communication system without hardware changes. We measured the error vector magnitude (EVM), bit error rate (BER) and spectral efficiency performance for single carrier M-QAM MIMO VLC using spatial diversity and spatial multiplexing. Results show that spatial diversity MIMO VLC improves error performance at the cost of spectral efficiency that spatial multiplexing should enhance. We propose the adaptive MIMO solution that both modulation schema and MIMO schema are dynamically adapted to the changing channel conditions for enhancing the error performance and spectral efficiency. The average error-free spectral efficiency of adaptive 2x2 MIMO VLC achieved 12 b/s/Hz over 2 meters indoor dynamic transmission.
This paper proposes a joint transmitter-receiver design to minimize the weighted sum power under the post-processing signal-to-interference-and-noise ratio (post-SINR) constraints for all subchannels. Simulation results demonstrate that the algorithm can not only satisfy the post-SINR constraints but also easily adjust the power distribution among the users by changing the weights accordingly. Hence the algorithm can be used to alleviates the adjacent cell interference by reducing the transmitting power to the edge users without performance penalty.
The Internet of things (IoT) holds much commercial potential and could facilitate distributed multiple-input multiple-output (MIMO) communication in future systems. We study a distributed reception scenario in which a transmitter equipped with multiple antennas sends multiple streams via spatial multiplexing to a large number of geographically separated single antenna receive nodes. The receive nodes then quantize their received signals and forward the quantized received signals to a receive fusion center. With global channel knowledge and forwarded quantized information from the receive nodes, the fusion center attempts to decode the transmitted symbols. We assume the transmit vector consists of phase shift keying (PSK) constellation points, and each receive node quantizes its received signal with one bit for each of the real and imaginary parts of the signal to minimize the transmission overhead between the receive nodes and the fusion center. Fusing this data is a non-trivial problem because the receive nodes cannot decode the transmitted symbols before quantization. Instead, each receive node processes a single quantity, i.e., the received signal, regardless of the number of transmitted symbols. We develop an optimal maximum likelihood (ML) receiver and a low-complexity zero-forcing (ZF)-type receiver at the fusion center. Despite its suboptimality, the ZF-type receiver is simple to implement and shows comparable performance with the ML receiver in the low signal-to-noise ratio (SNR) regime but experiences an error rate floor at high SNR. It is shown that this error floor can be overcome by increasing the number of receive nodes. Hence, the ZF-type receiver would be a practical solution for distributed reception with spatial multiplexing in the era of the IoT where we can easily have a large number of receive nodes.
This letter presents a novel detection strategy for Spatially-Multiplexed Generalized Spatial Modulation systems. It is a multi-stage detection that produces a list of candidates of the transmitted signal vector, sorted according to the proximity of the data vector to one of the possible vector subspaces. The quality metric and list-length metric selects the best candidate and manages the list length, respectively. Performance results show that it significantly reduces the performance gap to the optimal maximum likelihood detector, while maintaining significant computational cost reduction.