Yueping Wu

Delay-Aware BS Discontinuous Transmission Control and User Scheduling for Energy Harvesting Downlink Coordinated MIMO Systems

In this paper, we propose a two-timescale delay-optimal base station discontinuous transmission (BS-DTX) control and user scheduling for downlink coordinated MIMO systems with energy harvesting capability. To reduce the complexity and signaling overhead in practical systems, the BS-DTX control is adaptive to both the energy state information (ESI) and the data queue state information (QSI) over a longer timescale. The user scheduling is adaptive to the ESI, the QSI and the channel state information (CSI) over a shorter timescale. We show that the two-timescale delay-optimal control problem can be modeled as an infinite horizon average cost partially observed Markov decision problem (POMDP), which is well known to be a difficult problem in general. By using sample-path analysis and exploiting specific problem structure, we first obtain some structural results on the optimal control policy and derive an equivalent Bellman equation with reduced state space. To reduce the complexity and facilitate distributed implementation, we obtain a delay-aware distributed solution with the BS-DTX control at the BS controller (BSC) and the user scheduling at each cluster manager (CM) using approximate dynamic programming and distributed stochastic learning. We show that the proposed distributed two-timescale algorithm converges almost surely. Furthermore, using queueing theory, stochastic geometry, and optimization techniques, we derive sufficient conditions for the data queues to be stable in the coordinated MIMO network and discuss various design insights.

Analysis and Optimization of Caching and Multicasting in Large-Scale Cache-Enabled Wireless Networks

Caching and multicasting at base stations are two promising approaches to supporting massive content delivery over wireless networks. However, existing analysis and designs do not fully explore and exploit the potential advantages of the two approaches. In this paper, we consider the analysis and optimization of caching and multicasting in a large-scale cache-enabled wireless network. We propose a random caching and multicasting design. By carefully handling different types of interferers and adopting appropriate approximations, we derive a tractable expression for the successful transmission probability in the general region, utilizing tools from stochastic geometry. We also obtain a closed-form expression for the successful transmission probability in the high signal-to-noise ratio (SNR) and user density region. Then, we consider the successful transmission probability maximization, which is a very complex nonconvex problem in general. Using optimization techniques, we develop an iterative numerical algorithm to obtain a local optimal caching and multicasting design in the general region. To reduce complexity and maintain superior performance, we also derive an asymptotically optimal caching and multicasting design in the asymptotic region, based on a two-stage optimization framework. Finally, numerical simulations show that the asymptotically optimal design achieves a significant gain in successful transmission probability over some baseline schemes in the general region.

Generalized Framework for the Analysis of Linear MIMO Transmission Schemes in Decentralized Wireless Ad Hoc Networks

We develop a general framework for the analysis of a broad class of point-to-point linear multiple-input multiple-output (MIMO) transmission schemes in decentralized wireless ad hoc networks. New general closed-form expressions are derived for the outage probability, throughput and transmission capacity. For the throughput, we investigate the optimal number of data streams in various asymptotic regimes, which is shown to be dependent on different network parameters. For the transmission capacity, we prove that it scales linearly with the number of antennas, provided that the number of data streams also scales linearly with the number of antennas, in addition to meeting some mild technical conditions. We also characterize the optimal number of data streams for maximizing the transmission capacity. To make our discussion concrete, we apply our general framework to investigate three popular MIMO schemes, each requiring different levels of feedback. In particular, we consider eigenmode selection with MIMO singular value decomposition, multiple transmit antenna selection, and open-loop spatial multiplexing. Our analysis of these schemes reveals that significant performance gains are achieved by utilizing feedback under a range of network conditions.

Analysis and Design of Wireless Ad Hoc Networks With Channel Estimation Errors

We investigate the impact and design of two important application dependent parameters - the effective per-node data rate and the outage constraint - for single-antenna point-to-point transmission in wireless ad hoc networks. In contrast to most existing work, our results explicitly account for the effects of channel estimation. We first derive a new outage probability expression, from which we completely characterize the feasible range of effective per-node data rate and outage constraint pairs which yield a positive transmission capacity, and derive an exact expression for the transmission capacity in such cases. We then proceed to optimize the transmission capacity under different assumptions, yielding considerable new insight. First, assuming that all nodes place a stringent outage constraint, the optimal pilot-training length is derived and this is shown to increase with the frame length according to a square root law. Consequently, for a sufficiently long coherence interval, we show that there is a negligible loss in transmission capacity when using this optimal pilot-training length. Second, we show that if nodes place a constraint on their required effective data rate, then the outage constraint yielding the maximum transmission capacity is quite large. Moreover, this result demonstrates that, from an overall network perspective, it is preferable to operate with a higher total data rate (aggregated across all nodes) at the expense of a lower reliability.

Benefits of Transmit Antenna Selection in Ad Hoc Networks

In this paper, we investigate the benefits of providing limited-feedback and using transmit antenna selection (TAS) in ad hoc networks. We find that the TAS scheme can provide throughput gains of up to

MIMO beamforming with quantized feedback in ad hoc networks: Transmission capacity analysis

50%

User-Centric Interference Nulling in Downlink Multi-Antenna Heterogeneous Networks

compared to the non-feedback scheme. We also find that the performance gains of the TAS scheme are more significant for low path loss exponents and single-antenna transmission. Our results are obtained by deriving new closed-form expressions for the network throughput and transmission capacity. Moreover, we also propose design guidelines for TAS to determine the optimal number of antennas used for transmission.

Coverage and area spectral efficiency in downlink random cellular networks with channel estimation error

We investigate the performance of MIMO beamforming with quantized feedback in ad hoc networks. The primary findings are that a moderate number of feedback bits are sufficient to obtain significant transmission capacity gains compared to non-feedback schemes, whilst also achieving a high percentage of the transmission capacity obtained with unlimited feedback. We demonstrate that this achievable percentage is larger in high path loss environments, and that the number of feedback bits to maintain a fixed gain increases with the number of transmit antennas. These findings are obtained by new transmission capacity expressions which we derive.

Impact of training on multiple-antenna communications in wireless ad hoc networks

Heterogeneous networks (HetNets) have strong interference due to spectrum reuse. This affects the signal-to-interference ratio (SIR) of each user, and hence is one of the limiting factors of network performance. However, in previous works, interference management approaches in HetNets are mainly based on interference level, and thus cannot effectively utilize the limited resource to improve network performance. In this paper, we propose a user-centric interference nulling (IN) scheme in downlink two-tier HetNets to improve network performance by improving each users SIR. This scheme has three design parameters: the maximum degree of freedom for IN (i.e., maximum IN DoF), and the IN thresholds for the macro and pico users, respectively. Using tools from stochastic geometry, we first obtain a tractable expression of the coverage (equivalently outage) probability. Then, we characterize the asymptotic behavior of the outage probability in the high reliability regime. The asymptotic results show that the maximum IN DoF can affect the order gain of the asymptotic outage probability, while the IN thresholds only affect the coefficient of the asymptotic outage probability. Moreover, we show that the IN scheme can linearly improve the outage performance, and characterize the optimal maximum IN DoF which minimizes the asymptotic outage probability.

Asymptotic Outage Probability of MIMO-MRC Systems in Double-Correlated Rician Environments

We investigate the impact of channel estimation on the performance of downlink random cellular networks. First, we derive a new closed-form expression for the coverage probability under certain practical conditions. We show that the coverage probability is dependent on the user and base station (BS) densities solely through their ratio for arbitrary pilot-training length. Next, we derive the optimal pilot-training length that maximizes the area spectral efficiency (ASE) in several asymptotic regimes, and capture the dependence of this optimal length on the ratio between the user and BS densities. The ASE loss due to training is shown to be less significant in small cell networks with a larger base station density.