Online data marketplaces emerge in diverse data-driven applications, where dynamically arriving consumers pur-chase the data at posted prices. The data value decays over time in many tasks, such as machine learning predictions and realtime systems. Existing query pricing methods do not consider the time-discounting data value. In this paper, we study the query feature-based data pricing problem with unknown time-discounting data valuation. We propose an effective online data pricing mechanism Pride to maximize the cumulative sales revenue. It leverages the powerful property of the ellipsoid method to efficiently solve online optimization via exploration and exploitation. Based on Thompson sampling, we present a novel non-stationary MAB algorithm Biased-TS to determine a suitable discount factor and attain the dynamic posted price. It is theoretically proved that, the regret upper bound order of Pride is dominated by the discretization error$O(\frac{T}{k})$, where$K$and$T$are the numbers of discount candidates and total trading rounds, respectively. Biased-TS gets a sub-linear regret upper bound$O(K^{3}\sqrt{T\ln T}+K\exp\{4\sqrt{\ln T}\})$. Extensive experiments using both synthetic and real datasets demonstrate that Pride yields around 90% of the optimal cumulative revenue, and it substantially outperforms the state-of-the-art methods.

Massive MIMO is one of the promising technologies to be employed in the next generation mobile communication systems. Multi-user transmission is the nature of massive MIMO due to the high order of degree of freedoms and large beamforming gain provided by the large scale antenna arrays mounted at the base station side. Considering this nature and the characteristics of massive MIMO channels, we propose wideband and high-order multi-user MIMO transmission mode for future radio access. Under such transmission mode, the simple but efficient scheduling strategies, e.g., all-user on all-subband scheduling, can be considered for downlink scheduling to obtain the potential gain of massive MIMO. System-level performance and detailed analysis on the characteristics of channel state are provided through simulations, which show the advantages of proposed wideband and high-order MU-MIMO transmission mode for massive MIMO systems.

Coordinated Multi-Point (CoMP) transmission is a key technology for LTE-Advanced (LTE-A) to increase the downlink cell-edge throughput. Generally speaking, multi-cell channel state information (CSI) is needed at multiple coordinated eNBs to achieve the performance gain promised by CoMP in downlink. Sounding reference signal (SRS) could be an efficient tool to enable multi-cell CSI acquisition for time division duplex (TDD) LTE-A (TD-LTE-A) systems because of the inherent channel reciprocity in TDD systems. In this study, we investigate multi-cell SRS coordination schemes for TD-LTE-A downlink CoMP. We introduce a CoMP-specific sounding zone that facilitates multi-cell SRS coordination and then propose and compare three coordinated SRS resource allocation schemes within the CoMP-specific sounding zone, i.e., cyclic shift (CS) coordination, comb coordination, and orthogonal cover code (OCC) for the SRS. Computer simulations show the effectiveness of multi-cell SRS coordination and its necessity is revealed by the sum-rate analysis of TD-LTE-A downlink CoMP.

In this paper, we investigate the feasibility of applying interference alignment (IA) technology to multi-cell coordinated beamforming (CB) in a future release of the Long Term Evolution (LTE) standard. Specifically, we establish a link level model for a two-cell system according to the current LTE- Advanced (LTE-A) specifications, and evaluate the performance of an IA based CB scheme. The link-level performance of the IA based CB scheme is compared to that of conventional coordination schemes to identify the potential gain. We establish different simulation configurations to identify the factors that restrict the practical deployment of the IA based CB scheme and the potential requirements for extra standardization support.

This paper investigates low complexity coordinated beamforming (CB) schemes that adopt interference alignment (IA). The majority of existing IA CB schemes rely on iterative solutions that have drawbacks such as sensitivity to initialization conditions, high computational complexity, and infeasible system requirements. Low-complexity non-iterative solutions only exist for specific scenarios. In this paper, we first propose an IA scheme for a new scenario. Secondly, we discuss low complexity diversity enhancement methods which can be applied on top of any IA method to form the final IA CB scheme. The proposed scheme can achieve high multiplexing gain via IA while improving the capacity of the conventional IA schemes at the low signal-to-noise-ratio (SNR) regime.

We investigate the problem of two-cell multi-user co-channel transmission, wherein both inter-cell interference and intra-cell inter-user interference should be well managed. Interference alignment (IA) is theoretically proved to be a promising technology for managing co-channel interference. It can be used to mitigate inter-cell interference. When multiple users are served by a single cell, advanced multi- user multiple-input multiple output (MU-MIMO) transmission can be used to manage the intra-cell inter-user interference. In this paper, we present an efficient two-cell coordinated linear beamforming method where IA is combined with MU-MIMO to achieve high performance multi-cell cooperative transmission. The effectiveness of the proposed method is verified by numerical evaluations.

Multi-cell cooperative processing (MCP) is widely recognized as a key technology for next generation cellular networks due to its promising potential of increasing the spectrum utilization efficiency. The performance of novel MCP schemes in terms of e.g., capacity, throughput, are mostly evaluated numerically but not analytically. Although the latter approach offers more insights into the true nature, the high complexity of coordinated multi-cell systems usually makes analytical derivation intractable. In this paper, we make a comprehensive study of the rate region achieved by different downlink MCP schemes with per-cell power constraints. We derive in closed form the corresponding rate region boundaries based on simple but representative Wyner type model. The obtained analytical results provide a useful reference of the performance and complexity tradeoff of those schemes.

Multi-user multiple input multiple output (MU-MIMO) systems can achieve high spectral efficiency for wireless communication. The design of optimal transmit precoding for max-sum-rate (MSR) MU-MIMO downlink transmission is a non-trivial problem. Recent research approached this problem by exploiting the relationship between max-sum-rate and the minimum-mean-squared-error (MMSE) and proposed several iterative schemes. Based on these research results, we propose enhanced iterative MSR algorithms for linear MU-MIMO precoding which improve the sum rate performance and reduce the computational complexity. The effectiveness of the proposed method is verified by numerical simulations.

Precision time protocol (PTP) synchronizes clocks of networked elements by exchanging messages containing precise time-stamps. Based on the available timing information, different algorithms can be developed for the clock synchronization. This paper introduces a novel PTP-based method in which clock synchronization is formulated as a probabilistic inference problem and is solved by Kalman filtering. The performance of this approach is verified by numerical results.