Jinlong Hu

Broadband wireless communications on high speed trains

This tutorial paper provides a comprehensive overview on the recent development in broadband wireless communications for high speed trains. Starting with the introduction of the two-hop network structure, radio-over-fiber (RoF) based cell planning is described in detail. Moreover, based on the analysis of differences between conventional cellular systems and the one for high speed trains, promising techniques are recommended to improve the performance of handover, which is one of the main challenges in high speed train communications. Finally, in order to combat the fast fading caused by the high mobility, robust algorithms are needed in physical layer signal processing, including synchronization, channel estimation, modulation/demodulation, and so on.

Maximum entropy based IP-traffic classification in mobile communication networks

In order to maintain sufficient mobile communication network capacity for value added services, 3GPP has recently introduced an architecture called ”SIPTO”(Selected IP Traffic Offload) to offload selected mobile IP traffic from the core network. This brings new challenge to on line traffic classification scheme needed to figure out the traffic which should be offloaded in real time. Although traffic classification methodologies have already been investigated in wired IP network, they are not applicable in mobile environment where high bit error rates (BER) and temporary disconnections are observed due to hostile wireless channel conditions. This paper proposes a maximum entropy based IP-traffic classification scheme (METCS) to address on line traffic classification problems in mobile communication networks. METCS extracts the application layer payload pattern using randomly arrived packets instead of the first few sequential packets of an application flow. Simulation results show that METCS outperforms existing methods by offering average 5%-8% improvement in classification accuracy with about 60% time.

FFT Traffic Classification-Based Dynamic Selected IP Traffic Offload Mechanism for LTE HeNB Networks

Traffic offloading is a promising technique to alleviate the traffic load in LTE core networks. Based on 3GPP “SIPTO” (Selected IP Traffic Offload) architecture, this paper proposes dynamic SIPTO mechanism (D-SIPTO) for traffic offloading in LTE HeNB networks, which combines fast fourier transform (FFT) based IP traffic classification scheme (FFTTCS) with the dynamic traffic offload path selection algorithm (DTOPSA). Simulation results show that FFTTCS can realize on-line traffic classification with similar precisions but only using less than 10 % of the time needed by existing methods. Combined with DTOPSA, the proposed D-SIPTO can reduce the core network traffic by 60 % while selecting the optimal offload path according to the type of traffic to be offloaded.

A Distributed QoS Provision Scheme in IEEE802.16 Mesh Networks with Directional Antennas

QoS provisioning in wireless mesh networks has been to known as a challenging issue. In a distributed scheduling based wireless mesh backhaul network, conventional connection-based QoS provision mechanism requires considerable amount of overhead for per-link QoS signaling, thus cannot support high speed real time traffic. In this paper,we propose a DiffServ-like QoS provision mechanism for IEEE 802.16 mesh networks with directional antennas to increase network capacity and enable real time traffic. We develop a distributed scheduling algorithm based on our proposed scheme. Simulation results demonstrate that directional antennas can achieve higher network capacity compared with omni-antennas, and our QoS scheme can effectively guarantee QoS requirements of real time traffic.

Maximum Data Rate Power Allocation for MIMO Spatial Multiplexing Systems with Imperfect CSI

In MIMO systems, spatial multiplexing is a powerful technique for increasing channel capacity by transmitting multiple data streams in the same channel simultaneously. Moreover, additional performance can be extracted in the presence of channel state information (CSI) at the transmitter. However, channel estimation error usually exists in practical systems and leads to imperfect CSI. As a result, the system performance is degraded. Fortunately power allocation can mitigate the problem effectively. In this paper, the power allocation problem is investigated in the case of imperfect CSI with accurate system model. A greedy power allocation (GPA) algorithm with adaptive modulation scheme is proposed to maximize the system data rate while satisfying each data streams bit error rate requirement. Simulation results show that GPA can reduce the effects of imperfect CSI and obtain better performance than other traditional algorithms, e.g. waterfilling and equal power allocation algorithms.

Downlink Scheduling for QoS-Guaranteed Services in Multi-User MIMO Systems with Limited Feedback

Significant throughput gains and system fairness can be obtained by employing scheduling schemes based on precoding techniques. However, QoS guarantee requirements are seldom taken into account. In this paper, we propose a downlink scheduling algorithm for QoS-guaranteed services in multi-user multiple-input multiple-output (MIMO) systems with limited feedback. The proposed algorithm combines stream selection with multi-user packet scheduling. To maximize the overall capacity and reduce co-channel interference, streams are selected in accordance with precoding matrices. In multi-user packet scheduling, the base station first determines the SDMA region size for the primary streams. In addition, packet scheduling for the secondary streams is performed to completely exploit spatial multiplexing gains. Numerical results show that, at the cost of slightly lower system fairness, the proposed algorithm can achieve higher spectrum efficiency, and have a noticeable improvement in guaranteeing QoS requirements in terms of data rates and delay.

An Uplink Resource Allocation Scheme for SDMA-Based IEEE 802.16 MIMO-OFDMA Systems

In this paper, a low-complexity SDMA-based greedy resource allocation (SGRA) algorithm is proposed for the uplink of IEEE 802.16 MIMO-OFDMA systems taking into account co-channel interference. The objective of SGRA is to allocate resources in the space-time-frequency domain in order to maximize system throughput while guaranteeing QoS requirements of real time services. By performing efficient interference management, SGRA can be carried out in two phases. In the first phase, greedy resource allocation, primarily involving uplink scheduling and subchannel allocation across the MAC and PHY layers, is performed in the time-frequency domain. In the second phase, the resource allocation is extended to the space-time-frequency domain. Simulation results show that SGRA can improve system throughput while at the same time guaranteeing the delay and minimum data rate requirements of users.

A novel modification of WSF for DOA estimation

This paper addresses the most basic and crucial problem in smart antenna, i.e., the estimation of DOA (Direction-of-Arrival) finding. The performance of smart antenna system greatly depends on the resolution of DOA. MUSIC (MUiltiple SIgnal Classification) and ESPRIT (Estimation of Signal Paramter via Rotational Invariance Technique) are the most classic two algorithms for DOA finding in real systems. However, these two algorithms cannot handle coherent signals directly which happens for example in multipath propagation and the performance will be greatly deteriorated if the pre-processing technique such as spatial smoothing is used. Therefore, the system employing these two algorithms usually works in the condition of Line-of-Sight (LOS), e.g., in suburb circumstance. WSF (Weighted Subspace Fitting) algorithm is a more superior technique which has much higher resolution and can handle coherent signals without any pre-processing. However, conventional WSF needs to know the independent number of signals, otherwise its performance will be deteriorated. In this paper, we propose a modified WSF algorithm for DOA. The proposed modified WSF can detect the independent number of signals automatically and show much higher resolution compared to conventional WSF and MUSIC.

Analytical Investigation of LPF Mismatch in Direct Conversion Receivers

This paper analytically investigates the impact of low pass filter (LPF) mismatch in direct conversion receivers, which is a main source of frequency selective (FS) I/Q imbalance. Assuming a practical Elliptic LPF realized by active RC (resistor and capacitor) architecture, the LPF mismatch is mainly resulted from the non-ideal RCs in the circuit. The relationships between the imperfect RCs and the LPF mismatch and also the I/Q imbalance are analyzed using a first order Taylor expansion approximation. Close-form expressions are derived for the LPF mismatch variance and front end image leakage ratio (ILR), which are further verified by simulations. It is shown that for RC matching inaccuracies and tolerances less than 5%, the LPF mismatch (or the FS I/Q imbalance) is dominated by the RC matching inaccuracy, while the influence of RC tolerances is negligible. Moreover, simulations are carried out to investigate the impact of I/Q imbalance on the BER performance. It is shown that for both orthogonal frequency division multiplexing (OFDM) and single-carrier-frequency division multiple access (SC-FDMA), the bit error rate (BER) is seriously degraded by FS I/Q imbalance and an error floor will occur. In addition, the BER of OFDM is even worse than that of SC-FDMA, due to the poor image attenuation near the cutting off frequency. Therefore, compensation for the FS I/Q imbalance is always recommended, especially for OFDM systems.

Capacity optimization with stream control for MIMO Mesh networks

In this paper, we focus on the joint stream control and scheduling algorithm based on Multiple Input Multiple Output (MIMO) technique which can significantly improve network capacity without additional increase of bandwidth or transmit power. We propose a stream control algorithm to maximize the network capacity and develop a universal framework for capacity evaluation for MIMO Mesh networks. Simulation results demonstrate that our stream control algorithm can achieve higher network capacity compared with non-stream control.