Showing papers by "Mats Bengtsson published in 2018"

Optimum and Suboptimum Transmit Beamforming

Abstract: Beamforming for transmission in a network is, in several aspects, more difficult than beamforming for reception. At first sight, the problems appear to be equivalent; because the radio channel is reciprocal, a good uplink beamformer should also work well in the downlink. Another fundamental difference is the channel knowledge. A receiver equipped with an antenna array can estimate the channel, or train a beamformer adaptively, using known pilot symbols in the transmitted signal. In a system with time division duplex, the uplink and downlink transmission shares the same frequency using different time slots. As long as the delay between the uplink and downlink time slots is short compared with the coherence time of the channel, the reciprocity principle of electromagnetics shows that the spatial signature of the downlink is identical to that of the uplink. If however, the duplex time separation is larger than the coherence time, instantaneous downlink channel is more or less uncorrelated with the estimated uplink channel.

On the Energy Efficiency of MIMO Hybrid Beamforming for Millimeter-Wave Systems With Nonlinear Power Amplifiers

Abstract: Multiple-input multiple-output (MIMO) millimeter-wave (mm-wave) systems are vulnerable to hardware impairments due to operating at high frequencies and employing a large number of radio-frequency hardware components In particular, nonlinear power amplifiers (PAs) employed at the transmitter distort the signal when operated close to saturation due to energy efficiency considerations In this paper, we study the performance of an MIMO mm-wave hybrid beamforming scheme in the presence of nonlinear PAs First, we develop a statistical model for the transmitted signal in such systems and show that the spatial direction of the inband distortion is shaped by the beamforming filter This suggests that even in the large antenna regime, where narrow beams can be steered toward the receiver, the impact of nonlinear PAs should not be ignored Then, by employing a realistic power consumption model for the PAs, we investigate the tradeoff between spectral and energy efficiency in such systems Our results show that increasing the transmit power level when the number of transmit antennas grows large can be counter-effective in terms of energy efficiency Furthermore, using numerical simulation, we show that when the transmit power is large, analog beamforming leads to higher spectral and energy efficiency compared to digital and hybrid beamforming schemes

On the Energy Efficiency of MIMO Hybrid Beamforming for Millimeter Wave Systems with Nonlinear Power Amplifiers

Abstract: Multiple-input multiple-output (MIMO) millimeter wave (mmWave) systems are vulnerable to hardware impairments due to operating at high frequencies and employing a large number of radio- frequency (RF) hardware components. In particular, nonlinear power amplifiers (PAs) employed at the transmitter distort the signal when operated close to saturation due to energy efficiency considerations. In this paper, we study the performance of a MIMO mmWave hybrid beamforming scheme in the presence of nonlinear PAs. First, we develop a statistical model for the transmitted signal in such systems and show that the spatial direction of the inband distortion is shaped by the beamforming filter. This suggests that even in the large antenna regime, where narrow beams can be steered toward the receiver, the impact of nonlinear PAs should not be ignored. Then, by employing a realistic power consumption model for the PAs, we investigate the trade-off between spectral and energy efficiency in such systems. Our results show that increasing the transmit power level when the number of transmit antennas grows large can be counter-effective in terms of energy efficiency. Furthermore, using numerical simulation, we show that when the transmit power is large, analog beamforming leads to higher spectral and energy efficiency compared to digital and hybrid beamforming schemes.

Deep Learning for Frame Error Probability Prediction in BICM-OFDM Systems

Abstract: In the context of wireless communications, we propose a deep learning approach to learn the mapping from the instantaneous state of a frequency selective fading channel to the corresponding frame error probability (FEP) for an arbitrary set of transmission parameters. We propose an abstract model of a bit interleaved coded modulation (BICM) orthogonal frequency division multiplexing (OFDM) link chain and show that the maximum likelihood (ML) estimator of the model parameters estimates the true FEP distribution. Further, we exploit deep neural networks as a general purpose tool to implement our model and propose a training scheme for which, even while training with the binary frame error events (i.e., ACKs / NACKs), the network outputs converge to the FEP conditioned on the input channel state. We provide simulation results that demonstrate gains in the FEP prediction accuracy with our approach as compared to the traditional effective exponential SIR metric (EESM) approach for a range of channel code rates, and show that these gains can be exploited to increase the link throughput.

Distributed Largest Eigenvalue-Based Spectrum Sensing Using Diffusion LMS

Abstract: In this paper, we propose a distributed detection scheme for cognitive radio (CR) networks, based on the largest eigenvalues (LEs) of adaptively estimated correlation matrices (CMs), assuming that the primary user signal is temporally correlated The proposed algorithm is fully distributed, thereby avoiding the potential single point of failure that a fusion center would imply Different forms of diffusion least mean square algorithms are used for estimating and averaging the CMs over the CR network for the LE detection and the resulting estimation performance is analyzed using a common framework In order to obtain analytic results on the detection performance, the exact distribution of the CM estimates are approximated by a Wishart distribution, by matching the moments The theoretical findings are verified through simulations

A Learning Approach for Optimal Codebook Selection in Spatial Modulation Systems

Abstract: For spatial modulation (SM) systems that utilize multiple transmit antennas/patterns with a single radio front-end, we propose a learning approach to predict the average symbol error rate (SER) conditioned on the instantaneous channel state. We show that the predicted SER can be used to lower the average SER over Rayleigh fading channels by selecting the optimal codebook in each transmission instance. Further by exploiting that feedforward artificial neural networks (ANNs) trained with a mean squared error (MSE) criterion estimate the conditional a posteriori probabilities, we maximize the expected rate for each transmission instance and thereby improve the link spectral efficiency.

Distributed Coordinated Transmission with Forward-Backward Training for 5G Radio Access

Abstract: Coordinated multipoint (CoMP) transmission and reception have been considered in cellular networks for enabling larger coverage, improved rates, and interference mitigation To harness the gains of coordinated beamforming, fast information exchange over a backhaul connecting the cooperating base stations (BSs) is required In practice, the bandwidth and delay limitations of the backhaul may not be able to meet such stringent demands These impairments motivate the study of cooperative approaches based only on local channel state information (CSI) and which require minimal or no information exchange between the BSs To this end, several distributed approaches are introduced for coordinated beamforming (CB)-CoMP The proposed methods rely on the channel reciprocity and iterative spatially precoded over-the-air pilot signaling We elaborate how forward-backward (F-B) training facilitates distributed CB by allowing BSs and user equipments (UEs) to iteratively optimize their respective transmitters/receivers based on only locally measured CSI The trade-off due to the overhead from the F-B iterations is discussed We also consider the challenge of dynamic TDD where the UE-UE channel knowledge cannot be acquired at the BSs by exploiting channel reciprocity Finally, standardization activities and practical requirements for enabling the proposed F-B training schemes in 5G radio access are discussed

Channel Dependent Codebook Design in Spatial Modulation

Abstract: In this paper, we present a modulation design based on Spatial Modulation for the uplink in IoT applications The proposed modulation design uses a Tabu search based deterministic heuristic to adapt the modulation link based on channel information fed back by the receiver Our approach allows adaptivity to rate and energy constraints We numerically validate the proposed method on a scenario with full channel state information available at the transceiver, showing clear performance gains compared to simpler heuristics and channel independent codebook designs