Zhinan Xu

Relaying for IEEE 802.11p at road intersection using a vehicular non-stationary channel model

Traffic s afety at road intersections can be improved by establishing reliable communications between vehicles. For vehicle-to-vehicle communications, this requires information exchange in non line-of-sight (NLOS) conditions due to the obstruction by buildings. In order to overcome the low receive signal-to-noise ratio (SNR) due to NLOS, we consider to place a relay at road intersections to enhance the reliability of communication links. In this paper, we implement a vehicular non-stationary geometry based stochastic channel model for road intersections, which is an extension of an existing highway channel model. The model is verified by comparison with vehicular channel measurements. Using the proposed channel model, we present link level simulation results for IEEE 802.11p relaying at varying transmitter/receiver locations using different channel estimation techniques. The results show that a relay at the intersection is able to greatly extend the reliable communications region. Besides, in the high SNR regime with moderate or high mobility transmitter and receiver, the block type least square channel estimator is the bottleneck that limits the relaying performance. An advanced iterative channel estimator is also simulated, which exhibits robustness against increased vehicle velocities.

Threshold-based selective feedback for opportunistic interference alignment

Opportunistic interference alignment (OIA) exploits channel randomness and multiuser diversity by user selection. In this paper, we address the major disadvantage of OIA which requires the feedback of the locally measured interference alignment from all users. We propose a selective feedback scheme for OIA by thresholding, where only a subset of users are required to send feedback to the transmitter. The proposed scheme can reduce the amount of feedback and still achieve the optimal degrees of freedom (DoF). We characterize the threshold and the corresponding feedback load to achieve the full DoF for a given signal-to-noise ratio. Both theoretical analysis and simulation results show that the amount of feedback can be dramatically reduced (by one order of magnitude at 20dB SNR and two orders of magnitude at 30dB SNR), while still preserving the essential DoF promised by conventional OIA with full feedback.

Time-variant channel prediction for interference alignment with limited feedback

We propose a novel limited feedback algorithm for single-input single-output (SISO) interference alignment in time-variant channels. The feedback algorithm enables reduced-rank channel prediction to compensate for the channel estimation error due to time selectivity and feedback delay. An upper bound for the rate loss caused by feedback quantization and channel prediction is derived. We characterize the scaling of the required number of feedback bits in order to decouple the rate loss due to channel quantization from the transmit power. Based on our derived upper bound, we develop a dimension switching algorithm which is able to find the best tradeoff between quantization error and prediction error. Simulation results show that a rate gain over the traditional non-predictive feedback strategy can be secured and a 60% higher rate is achieved at 20dB signal-to-noise ratio.

Opportunistic interference alignment with 1-bit feedback in 3-cell interference channels

Opportunistic interference alignment (OIA) exploits channel randomness and multiuser diversity by user selection. The transmitter needs channel state information (CSI), which is usually measured on the receiver side and sent to the transmitter side via a feedback channel. Lee and Choi show that d degrees of freedom (DoF) per transmitter are achievable in a 3-cell MIMO interference channel assuming a fully informed network, where every user feeds back a real-valued variable to their own transmitter. This paper investigates the achievable DoF using only 1-bit feedback per user. We prove that 1-bit feedback is sufficient to achieve the optimal DoF d. Most importantly, the required number of users for OIA with 1-bit feedback remains the same as with real-valued feedback. Moreover, for a given system configuration, we provide an optimal choice of the 1-bit quantizer, which captures most of the capacity provided by a system with real-valued feedback.

A ray tracing algorithm for intelligent transport systems in tunnels

It is well-known that the radio wave propagation mechanisms inside a tunnel are different from the typical outdoor and indoor situations. Since the tunnels represent a significant type of vehicular environments, understanding the channel characteristics for the in-tunnel scenario is crucial for intelligent transport systems design. A widely used tool for simulating channel characteristics for outdoor and indoor scenarios is a deterministic propagation prediction tool, known as ray tracing (RT). However, RT applied for tunnel scenarios has not been studied adequately. In this paper, we first evaluate the real-world in-tunnel vehicle-to-vehicle radio channel measurements on the basis of time-varying power delay profile analysis. Secondly we introduce a RT tool that includes influence of the moving objects, to predict wave propagation mechanisms in the tunnel. In order to reduce computational complexity of RT, we suggest to combine an approximate algorithm for the higher-order reflection components with conventional RT and use a novel subdivision algorithm for modeling the diffuse scattering. Combining the higher-order reflection algorithm with conventional RT allows us to obtain more accurate delay spread results. The numerical simulations show that contribution of both the higher-order reflection and the diffuse components are equally important for the in-tunnel scenarios.

On the Optimum Number of Hypotheses for Adaptive Reduced-Rank Subspace Selection

Intelligent transport systems (ITS) require low-latency dependable wireless communication links in between vehicles as well as between vehicles and the infrastructure. In vehicular communication scenarios communication channels are time- and frequency (doubly) dispersive and the channel statistics are non-stationary, i.e., they change over time. Hence, the design of appropriate channel estimators is challenging. Recently an adaptive reduced-rank channel estimation technique for non-stationary time-variant channel estimation was introduced by Zemen and Molisch, 2012. This technique uses a hypothesis test to obtain an estimate of the current channel statistics on a per frame basis. The optimum number of hypotheses is not known. In this paper we present new empirical insights on the optimum choice of the number of hypotheses for the hypothesis test for non-stationary time-variant channel estimation. With these considerations the complexity of an adaptive reduced-rank channel estimator can be reduced and its performance improved.

Real-time channel emulation of a geometry-based stochastic channel model on a SDR platform

In wireless vehicular communication, channel properties change rapidly over time. Both, the transmitter and the receiver, are moving, which generates not only time and frequency (doubly) selective channels but also channel statistics that are non-stationary, i.e., they change over time. New wireless vehicular communication systems for connected autonomous vehicles require validation and verification in vehicular environments to assure their proper functionality. To avoid time intensive, costly and difficult to repeat real-world measurements on the road, real-time channel emulators that target on emulating the wireless vehicular channel as accurately as possible, are needed. In this paper, we present a real-time channel emulator based on a software defined radio platform that is able to emulate real-valued path delays and Doppler shifts within a certain delay and Doppler region. The emulator uses a low-complexity subspace expansion model where the emulation complexity on the field programmable gate array (FPGA) is independent from the number of propagation paths. This makes it suitable to emulate realistic geometry-based non-stationary channel models with a large number of propagation paths.

A Sub-Band Divided Ray Tracing Algorithm Using the DPS Subspace in UWB Indoor Scenarios

Sub-band divided ray tracing (SDRT) is one technique that has been extensively used to obtain the channel characteristics for ultra-wideband (UWB) radio wave propagation in realistic indoor environments. However, the computational complexity of SDRT scales directly with the number of sub-bands. Although we have proposed a low-complexity SDRT algorithm for one terminal position [1], the computational complexity is still extremely high when involving multiple mobile terminal positions. Moreover, some indoor positioning techniques require for high positioning accuracy data from measurements/simulations with a very fine spatial resolution. To cope with this, we propose an algorithm to reduce the computational complexity of SDRT for multiple mobile terminal positions. The algorithm uses a projection of all propagation paths on a subspace spanned by two-dimensional discrete prolate spheroidal (DPS) sequences at each sub-band. It is important to note that, since the geometrical information of the propagation paths is the same in all sub-bands, the subspace dimension and basis coefficients in frequency dimension do not need to be recalculated at different sub-bands. We justify the simplifications of the proposed method by numerical simulations. Furthermore, we evaluate the effect of antenna characteristics on the proposed algorithm. Our proposed algorithm reduces the computational complexity by more than one order of magnitude for indoor scenarios.

Grassmannian delay-tolerant limited feedback for interference alignment

In this paper, we propose a delay-tolerant limited feedback algorithm for single-input single-output (SISO) interference alignment. The temporal correlation and band limitation of the time-selective fading process is exploited to model the channel impulse response with a low-dimensional basis expansion. The algorithm tracks and feeds back the evolution of the basis expansion coefficients on the Grassmannian manifold. These coefficients allow the transmitter to predict the future channel realizations to compensate the feedback delay. By feeding back the quantized basis expansion coefficients instead of the channel impulse responses, the number of feedback bits can be substantially reduced. Our numerical results demonstrate that by exploiting the subspace structure of the time-variant channel we can reduce the amount of feedback to 2 bits per channel realization.