Zihuai Lin

Performance Impact of LoS and NLoS Transmissions in Dense Cellular Networks

In this paper, we introduce a sophisticated path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions to study their impact on the performance of dense small cell networks (SCNs). Analytical results are obtained for the coverage probability and the area spectral efficiency (ASE), assuming both a general path loss model and a special case with a linear LoS probability function. The performance impact of LoS and NLoS transmissions in dense SCNs in terms of the coverage probability and the ASE is significant, both quantitatively and qualitatively, compared with the previous work that does not differentiate LoS and NLoS transmissions. Our analysis demonstrates that the network coverage probability first increases with the increase of the base station (BS) density, and then decreases as the SCN becomes denser. This decrease further makes the ASE suffer from a slow growth or even a decrease with network densification. The ASE will grow almost linearly as the BS density goes ultra dense. For practical regime of the BS density, the performance results derived from our analysis are distinctively different from previous results, and thus shed new insights on the design and deployment of future dense SCNs.

Analysis of receiver algorithms for lte LTE SC-FDMA based uplink MIMO systems

This letter derives mathematical expressions for the received signal-to-interference-plus-noise ratio (SINR) of uplink Single Carrier (SC) Frequency Division Multiple Access (FDMA) multiuser MIMO systems. An improved frequency domain receiver algorithm is derived for the studied systems, and is shown to be significantly superior to the conventional linear MMSE based receiver in terms of SINR and bit error rate (BER) performance.

Distributed Caching for Data Dissemination in the Downlink of Heterogeneous Networks

Heterogeneous cellular networks (HCNs) with embedded small cells are considered, where multiple mobile users wish to download network content of different popularity. By caching data into the small-cell base stations, we will design distributed caching optimization algorithms via belief propagation (BP) for minimizing the downloading latency. First, we derive the delay-minimization objective function and formulate an optimization problem. Then, we develop a framework for modeling the underlying HCN topology with the aid of a factor graph. Furthermore, a distributed BP algorithm is proposed based on the networks factor graph. Next, we prove that a fixed point of convergence exists for our distributed BP algorithm. In order to reduce the complexity of the BP, we propose a heuristic BP algorithm. Furthermore, we evaluate the average downloading performance of our HCN for different numbers and locations of the base stations and mobile users, with the aid of stochastic geometry theory. By modeling the nodes distributions using a Poisson point process, we develop the expressions of the average factor graph degree distribution, as well as an upper bound of the outage probability for random caching schemes. We also improve the performance of random caching. Our simulations show that 1) the proposed distributed BP algorithm has a near-optimal delay performance, approaching that of the high-complexity exhaustive search method; 2) the modified BP offers a good delay performance at low communication complexity; 3) both the average degree distribution and the outage upper bound analysis relying on stochastic geometry match well with our Monte-Carlo simulations; and 4) the optimization based on the upper bound provides both a better outage and a better delay performance than the benchmarks.

Pricing and Resource Allocation via Game Theory for a Small-Cell Video Caching System

Evidence indicates that downloading on-demand videos accounts for a dramatic increase in data traffic over cellular networks. Caching popular videos in the storage of small-cell base stations (SBS), namely, small-cell caching, is an efficient technology for reducing the transmission latency while mitigating the redundant transmissions of popular videos over back-haul channels. In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several video retailers (VRs), and mobile users (MUs). The NSP leases its SBSs to the VRs for the purpose of making profits, and the VRs, after storing popular videos in the rented SBSs, can provide faster local video transmissions to the MUs, thereby gaining more profits. We conceive this system within the framework of Stackelberg game by treating the SBSs as specific types of resources. We first model the MUs and SBSs as two independent Poisson point processes, and develop, via stochastic geometry theory, the probability of the specific event that an MU obtains the video of its choice directly from the memory of an SBS. Then, based on the probability derived, we formulate a Stackelberg game to jointly maximize the average profit of both the NSP and the VRs. In addition, we investigate the Stackelberg equilibrium by solving a non-convex optimization problem. With the aid of this game theoretic framework, we shed light on the relationship between four important factors: the optimal pricing of leasing an SBS, the SBSs allocation among the VRs, the storage size of the SBSs, and the popularity distribution of the VRs. Monte Carlo simulations show that our stochastic geometry-based analytical results closely match the empirical ones. Numerical results are also provided for quantifying the proposed game-theoretic framework by showing its efficiency on pricing and resource allocation.

Distributed Network Coding for Wireless Sensor Networks Based on Rateless LT Codes

In this paper, we propose a distributed network coding (DNC) scheme based on digital fountain rateless LT codes for wireless sensor networks (WSNs), where a group of source nodes communicate with a single destination node through a group of relay nodes in a two-hop fashion. The aim of the proposed scheme is to achieve network coding gains. The relay nodes perform linear combinations by using network coding to form network-coded symbols and send them to the destination node. At the destination, a graph-based, rateless code is formed on the fly. The main contribution of this paper is the derivation of an analytical symbol error rate (SER) bound for the proposed coding scheme.

Will the Area Spectral Efficiency Monotonically Grow as Small Cells Go Dense

In this paper, we introduce a sophisticated path loss model into the stochastic geometry analysis incorporating both line-of-sight (LoS) and non- line-of-sight (NLoS) transmissions to study their performance impact in small cell networks (SCNs). Analytical results are obtained on the coverage probability and the area spectral efficiency (ASE) assuming both a general path loss model and a special case of path loss model recommended by the 3rd Generation Partnership Project (3GPP) standards. The performance impact of LoS and NLoS transmissions in SCNs in terms of the coverage probability and the ASE is shown to be significant both quantitatively and qualitatively, compared with previous work that does not differentiate LoS and NLoS transmissions. From the investigated set of parameters, our analysis demonstrates that when the density of small cells is larger than a threshold, the network coverage probability will decrease as small cells become denser, which in turn makes the ASE suffer from a slow growth or even a notable decrease. For practical regime of small cell density, the performance results derived from our analysis are distinctively different from previous results, and shed new insights on the design and deployment of future dense/ultra-dense SCNs. It is of significant interest to further study the generality of our conclusion in other network models and with other parameter sets.

Power and rate adaptation for wireless network coding with opportunistic scheduling

This paper analyzes the average capacity for a wireless network with joint opportunistic scheduling and wireless network coding. The capacity and the optimal power allocation scheme are derived for a multiuser fading broadcasting channel with perfect channel side information at the transmitter. The packets generated by the source nodes are encoded prior to the transmission to the relay node. The received encoded packets are mixed by XOR operation at the relay node and then broadcasted to the destination nodes. The encoder in the source nodes is designed in such a way that each destination node can give different interpretation of the received packets with their own side information. From the numerical and simulation results, we can see that the proposed simultaneous power and rate adaption for wireless network coding with opportunistic scheduling can significantly improve the average channel capacity.

Distributed and Optimal Resource Allocation for Power Beacon-Assisted Wireless-Powered Communications

In this paper, we investigate optimal resource allocation in a power beacon-assisted wireless-powered communication network (PB-WPCN), which consists of a set of hybrid access point (AP)-source pairs and a power beacon (PB). Each source, which has no embedded power supply, first harvests energy from its associated AP and/or the PB in the downlink (DL) and then uses the harvested energy to transmit information to its AP in the uplink (UL). We consider both cooperative and non-cooperative scenarios based on whether the PB is cooperative with the APs or not. For the cooperative scenario, we formulate a social welfare maximization problem to maximize the weighted sum-throughput of all AP-source pairs, which is subsequently solved by a water-filling based distributed algorithm. In the non-cooperative scenario, all the APs and the PB are assumed to be rational and self-interested such that incentives from each AP are needed for the PB to provide wireless charging service. We then formulate an auction game and propose an auction based distributed algorithm by considering the PB as the auctioneer and the APs as the bidders. Finally, numerical results are performed to validate the convergence of both the proposed algorithms and demonstrate the impacts of various system parameters.

Probabilistic Small-Cell Caching: Performance Analysis and Optimization

Small-cell caching utilizes the embedded storage of small-cell base stations (SBSs) to store popular contents for the sake of reducing duplicated content transmissions in networks and for offloading the data traffic from macrocell base stations to SBSs. In this paper, we study a probabilistic small-cell caching strategy, where each SBS caches a subset of contents with a specific caching probability. We consider two kinds of network architectures: 1) The SBSs are always active, which is referred to as the always-on architecture; and 2) the SBSs are activated on demand by mobile users (MUs), which is referred to as the dynamic on–off architecture. We focus our attention on the probability that MUs can successfully download content from the storage of SBSs. First, we derive theoretical results of this successful download probability (SDP) using stochastic geometry theory. Then, we investigate the impact of the SBS parameters, such as the transmission power and deployment intensity on the SDP. Furthermore, we optimize the caching probabilities by maximizing the SDP based on our stochastic geometry analysis. The intrinsic amalgamation of optimization theory and stochastic geometry based analysis leads to our optimal caching strategy, characterized by the resultant closed-form expressions. Our results show that in the always-on architecture, the optimal caching probabilities solely depend on the content request probabilities, while in the dynamic on–off architecture, they also relate to the MU-to-SBS intensity ratio. Interestingly, in both architectures, the optimal caching probabilities are linear functions of the square root of the content request probabilities. Monte-Carlo simulations validate our theoretical analysis and show that the proposed schemes relying on the optimal caching probabilities are capable of achieving substantial SDP improvement, compared with the benchmark schemes.

Novel Soft Information Forwarding Protocols in Two-Way Relay Channels

In this paper, we investigate novel soft mutual information forwarding (MIF) protocols in a two-way relay channel (TWRC), where two sources exchange information with the help of an intermediate relay. Based on the estimated signals from the two sources, the relay calculates the soft mutual information and then broadcasts it to the two sources. In particular, we propose two MIF protocols, namely, network-coded MIF (NC-MIF) and superposition-coded MIF (SC-MIF), which is suitable for different channel conditions. The expressions derived for the received signal-to-noise ratio (SNR) at the receivers reveal that, if both source-to-relay channels are in good condition, the NC-MIF outperforms the SC-MIF. Otherwise, the SC-MIF is superior to the NC-MIF. For the TWRC with varying channels, we further develop an adaptive scheme, which enables the dynamic switch between the two protocols, depending on the received SNR at the sources. Furthermore, the threshold that determines the switch of the protocols is developed as a close-form expression. Simulation results show that our adaptive scheme outperforms other protocols discussed in this paper over fading channels.