Zhiyuan Jiang

Optimal Antenna Cluster Size in Cell-Free Large-Scale Distributed Antenna Systems With Imperfect CSI and Intercluster Interference

In cell-free large-scale distributed antenna systems (L-DASs), the antennas are distributed over the intended coverage area. Introducing cooperation among the antennas can significantly improve the system throughput. However, only finite-cluster-size (limited) cooperation with L-DAS antenna clusters is realistic due to practical limitations. In this paper, the impact of antenna cluster size on the downlink sum rate of frequency-division duplex (FDD) cell-free L-DASs is analyzed, considering imperfect channel state information (CSI) and intercluster interference (ICLI). We investigate the optimal cluster size in terms of maximizing the downlink sum rate with respect to the overhead of imperfect channel training and feedback. When the number of users is sufficiently large, closed-form lower bounds of the ergodic sum capacity, which are leveraged to analyze the system performance, are derived by constructing different rate-achieving user-scheduling schemes. Both analog and digital feedback schemes are considered to evaluate the imperfect channel feedback. Based on these, closed-form expressions of the optimal cluster size for 1-D antenna topology systems and the corresponding achievable rates are derived. The scaling law of the optimal cluster size for 2-D antenna topology systems is also given. Numerical results demonstrate the impact of the signal-to-noise ratio (SNR) and the block length on the achievable rates and the optimal cluster size, which agree with our analytical results.

Capacity bounds of downlink network MIMO systems with inter-cluster interference

To fully understand the capacity of clustered network-MIMO systems and analyze the system performance (throughput, energy-efficiency or quality of service), one must have an analytical expression of the system capacity or capacity bounds. In this paper, the impact of cluster size on the downlink network-MIMO system capacity is analyzed considering inter-cluster interference (ICLI) based on the 1-dimensional Wyner model. For the nonfading channels, the lower and upper bounds of the per-cell capacity with ICLI are derived. The per-cell capacity with ICLI demonstrates a linear growth versus the cluster size in the interference-limited regime due to ICLI. The lower and upper bounds are generalized to a 2-dimensional cellular system. The ICLI turns out to have a great impact on the system capacity when the cluster size is small. Introducing Rayleigh fading channels, assuming the number of users in each cell is sufficiently large, a lower bound of the per-cell ergodic capacity with ICLI is derived.

Pilot-Data Superposition for Beam-Based FDD Massive MIMO Downlinks

In this letter, we propose a superposition signaling of pilots and data scheme (SPD) for beam-based frequency-division-duplex massive multiple-input multiple-output systems, which allows pilots and data to be transmitted simultaneously in the downlink. The proposed SPD scheme leverages spatial channel correlations to reduce the dimensionality loss, and more importantly, addresses the problem of uneven user channel correlations by superposition signaling of pilots and information bearing data symbols. Essentially, users with smaller dimensionality loss entail less pilots based on the SPD scheme. Simulations results reveal significant throughput gain by the SPD scheme over the state-of-the-art pilot-based approaches.

Antenna-beam spatial transformation in c-RAN with large antenna arrays

A spatial transformation scheme is proposed to be deployed at the radio-units (RUs) in cloud radio-access-networks (C-RAN) to alleviate the fronthaul burden and baseband signal processing complexity. The module transforms antenna domain signals to the beam domain, such that the spatial dimensionality is significantly reduced for RUs with large antenna arrays. A low-complexity transformation scheme is investigated with finite uplink receive samples. It leverages a combined discrete-Fourier-transform (C-DFT) approach, which shows significant signal power gain over the conventional DFT scheme by combining correlated DFT beams to form a more matched beam pattern. The optimality of the combination method is proved. The performance is validated by extensive simulations which are based on practical channel models and system parameters.

On dimensionality loss in FDD massive MIMO systems

Dimensionality loss is defined as the channel estimation overhead, which results in a loss of time-frequency resources in pilot-assisted wireless systems. In this paper, the scaling result of dimensionality loss, i.e., the scaling factor, in frequency-division-duplex (FDD) massive multiple-input-multiple-output(MIMO) downlinks is derived. The scaling factor determines the amount of channel estimation overhead, and thus is vital to understand the downlink throughput in FDD massive MIMO systems. Moreover, the transmit diversity of the downlink channel is also derived. In the simulations, we adopt a geometry-based stochastic channel model to validate our analysis. The impact of several assumptions made in our analysis is also investigated.

Dynamic Channel Acquisition in MU-MIMO

Multiuser multiple-input-multiple-output (MU-MIMO) systems are known to be hindered by dimensionality loss due to channel state information (CSI) acquisition overhead. In this paper, we investigate user-scheduling in MU-MIMO systems on account of CSI acquisition overhead, where a base station dynamically acquires user channels to avoid choking the system with CSI overhead. The genie-aided optimization problem (GAP) is first formulated to maximize the Lyapunov-drift every scheduling step, incorporating user queue information and taking channel fluctuations into consideration. The scheduling scheme based on GAP, namely the GAP-rule, is proved to be throughput-optimal but practically infeasible, and thus serves as a performance bound. In view of the implementation overhead and delay unfairness of the GAP-rule, the T-frame dynamic channel acquisition scheme and the power-law DCA scheme are further proposed to mitigate the implementation overhead and delay unfairness, respectively. Both schemes are based on the GAP-rule and proved throughput-optimal. To make the schemes practically feasible, we then propose the heuristic schemes, queue-based quantized-block-length user scheduling scheme (QQS), T-frame QQS, and power-law QQS, which are the practical versions of the aforementioned GAP-based schemes, respectively. The QQS-based schemes substantially decrease the complexity, and also perform fairly close to the optimum. Numerical results evaluate the proposed schemes under various system parameters.

User scheduling in pilot-assisted TDD multiuser MIMO systems

User scheduling in multiuser multiple-input-multiple-output (MU-MIMO) systems is fundamentally different with single-user systems1, in the sense that without spatial multiplexing, users in single-user systems are sharing the time-frequency degree-of-freedoms (DoFs), whereas in MU-MIMO systems, due to the fact that the number of spatial DoFs scales with the number of users (assuming sufficient base station (BS) antennas), users are not sharing the DoFs, but rather creating additional DoFs for their own use. However, instead of limited by the available DoFs, the number of simultaneous users are limited by the channel state information (CSI) acquisition overhead in pilot-assisted MU-MIMO systems. In this paper, we investigate the user scheduling scheme in pilot-assisted time-division-duplex (TDD) MU-MIMO systems. Leveraging the Lyapunov optimization techniques, we derive the throughput-optimal scheduling policy which serves as a performance bound due to its non-causality and high complexity. We then propose a heuristic scheme, which is causal and substantially decreases the complexity. Moreover, it performs fairly close to the optimum.

Minimum power consumption of a base station with large-scale antenna array

In this paper we consider the minimum base station (BS) power consumption given the sum rate requirement in large-scale multiple-input-multiple-output (MIMO) systems. A single cell with an Mtot-antenna BS and N single-antenna users is considered. The BS power consumption consists of two parts: The part accounting for the total transmit power and the part proportional to the number of active antennas. Specifically, closed-form approximations (CFAs) of the optimal transmit power and optimal number of active antennas are derived when the sum rate requirement is high. A CFA of the ergodic sum capacity upper bound for the downlink broadcast channel is also given.

Joint User Scheduling and Beam Selection Optimization for Beam-Based Massive MIMO Downlinks

In beam-based massive multiple-input multiple-output systems, signals are processed spatially in the radio-frequency (RF) front end and thereby the number of RF chains can be reduced to save hardware cost, power consumptions, and pilot overhead. Most existing work focuses on how to select or design analog beams to achieve performance close to full digital systems. However, since beams are strongly correlated (directed) to certain users, the selection of beams and scheduling of users should be jointly considered. In this paper, we formulate the joint user scheduling and beam selection problem based on the Lyapunov-drift optimization framework and obtain the optimal scheduling policy in a closed form. For reduced overhead and computational cost, the proposed scheduling schemes are based only upon statistical channel state information. Towards this end, asymptotic expressions of the downlink broadcast channel capacity are derived. To address the weighted sum rate maximization problem in the Lyapunov optimization, an algorithm based on block coordinated update is proposed and proved to converge to the optimum of the relaxed problem. To further reduce the complexity, an incremental greedy scheduling algorithm is also proposed, whose performance is proved to be bounded within a constant multiplicative factor. Simulation results based on widely-used spatial channel models are given. It is shown that the proposed schemes are close to optimal and outperform several state-of-the-art schemes.

Proactive Content Push in Heterogeneous Networks with Multiple Energy Harvesting Small Cells

Energy harvesting is an emerging technology providing clean energy for wireless communication systems. Due to the randomness in energy arrivals, wireless service process needs to be matched with energy provision to avoid energy waste or shortage. Other than passively adjusting energy usage according to traffic and energy profiles, a framework, namely, GreenDelivery has been proposed to proactively push popular contents to users in advance, such that harvested energy can be utilized more efficiently. In this paper, a heterogeneous network with multiple GreenDelivery small cells is considered. Due to spatial proximity, adjacent small cells might conflict with each other due to simultaneous transmissions or repeated pushes of identical contents to the same user, which calls for a more sophisticated design of push scheme in a multi- cell scenario. To tackle the interference, small base station (SBS) scheduling is proposed, exploiting the intermittent nature of renewable energy, to temporally separate the transmission of adjacent small cells. Heuristic push schemes are then proposed to further reduce the user requests handled by macro base stations (MBS) with centralized and distributed realizations. Simulations show that the proposed push schemes outperform the baseline scheme in which contents are pushed in the descending order of their popularities, especially when content popularity is more uniformly distributed.