Pei-Rong Li

Energy-efficient power allocation for distributed large-scale MIMO cloud radio access networks

Recently, promoting energy efficiency is an important research issue in the wireless communication system. This paper investigates the resource management problem with the regularized zero-forcing (RZF) precoding for the distributed large-scale multiple-input multiple-output cloud radio access network (DLS MIMO C-RAN) which consists of a large number of spatially distributed remote radio heads (RRHs). The challenges of this power allocation problem arise from the presence of both interference and imperfect channel state information at the transmitter (CSIT). Therefore, the C-RAN based power allocation schemes are designed to efficiently allocate the transmit power of each RRH. Moreover, the large random matrix theory is applied to derive the asymptotic expressions for large number of antennas. Simulation results show that the proposed schemes can provide better energy efficiency with the consideration of quality-of-service (QoS) support.

Joint Base Station Association and Radio Resource Allocation for Downlink Carrier Aggregation in LTE-Advanced Systems

The deployment of small cells with carrier aggregation (CA) technique is a significant feature of next-generation cellular networks. The potential benefits of dense base stations (BSs) deployment are provided by using proper interference management mechanisms. Meanwhile, the user equipments (UEs) can simultaneously access multiple component carriers (CCs) to meet the dramatically increased traffic demand by means of CA. In this paper, an optimization problem consists of UE-BS association, CC configuration, frequency resources scheduling, and power allocation with adaptive modulation schemes is investigated to enhance energy efficiency, focusing on the quality of service (QoS)-aware CA. To obtain the near optimal solutions as well as the consideration of computational complexity, a cross entropy- based algorithm (CEA) is proposed to jointly solve the combinatorial problem. Compared to the traditional heuristic algorithms, the merits of proposed scheme can be observed via simulation results.

Novel Design on Multiple Channel Sensing for Partially Observable Cognitive Radio Networks

A great amount of research has devoted to cognitive radio (CR) in recent years in order to improve spectrum efficiency. In decentralized CR networks, it is not realistic for CR users to sense entire spectrum in practice due to hardware limitations. Consequently, the partially observable Markov decision process (POMDP) can be utilized to provide CR users with sufficient information in partially observable environments. Existing POMDP-based protocols adopt channel aggregation techniques in order to improve spectrum opportunities and system performance. However, the required time for channel sensing is neglected which can result in large sensing time overhead and spectrum opportunity loss in realistic environments. In this paper, based on partially observable channel state with the consideration of sensing overhead, the stochastic multiple channel sensing (SMCS) protocol is proposed to conduct optimal channel selection for maximizing the aggregated throughput of CR users. By adopting the proposed SMCS protocol, CR users can highly accommodate themselves to rapidly varying environment based on the dynamically adjustable channel sensing strategy. Moreover, the channel sensing problem is further extended to imperfect sensing scenario, which can severely degrade system throughput due to packet collision between primary users (PUs) and CR users. Consequently, in addition to channel selection, it is required for CR users to determine the sensing time length in order to address the collision problem. The two-phase SMCS (TSMCS) protocol is proposed to maximize the aggregated throughput of CR users while still fulfilling PUs’ quality-of-service (QoS) requirements. Numerical results show that the proposed SMCS and TSMCS protocols can effectively maximize the aggregated throughput for decentralized CR networks.

Novel Design of Optimal Spectrum Sharing for Cognitive Radio-Enabled LTE-A Multi-Cell Networks

For long term evolution-advanced (LTE-A) system, carrier aggregation (CA) allows LTE evolved Node B (eNB) and user equipment (UE) to access idle frequency spectrum to serve continuously growing traffic demands. To efficiently enhance network throughput, the concept of cognitive radio (CR) is adopted for LTE-A system to dynamically access opportunistic spectrum not occupied by primary system. Under the consideration of limited feedback system, the partially observable Markov decision process (POMDP) is applied in this paper to estimate the channel occupancy information (COI) on shared spectrum by partially sensing the frequency carriers. A POMDP-based spectrum sharing (POSS) scheme is then proposed to realize throughput maximization via the designs of contention mechanism and resource allocation policy according to the partially observable COI, channel state information, and number of contending cells in the network. Compared with the exhaustive search, the proposed approach can achieve optimal performance gain with lower computational complexity, which is proved to be a linear time algorithm. Numerical results illustrate that the proposed POSS protocol can effectively improve system throughput on shared spectrum for the LTE-A networks.

Joint Clusterization and Power Allocation for Cloud Radio Access Networks

In this paper, the cloud radio access network (C- RAN) is considered to extend the transmission coverage via the distributed deployment of large- scale remote radio units (RRUs). However, this type of structure can induce considerable computational loadings due to the centralized management mechanisms. To reduce the complexity incurred in the C-RAN architecture, the clusterization technique is designed to categorize those RRUs into several groups. For the purpose of enhancing energy efficiency (EE) as well as the consideration of computational complexity, the joint clusterization and power allocation schemes are proposed to obtain the better tradeoff under the quality-of-service (QoS) requirement for each user equipment (UE). Simulation results show that the proposed algorithms can provide better performance gain than the existing method.

Joint Wireless Charging and Hybrid Power Based Resource Allocation for LTE-A Wireless Network

In this paper, an energy efficient resource allocation is investigated to enhance the network performance of cellular networks. Specifically, the small cells (SCs) using both green and on-grid energy and the power splitter-enabled wireless charging for user equipments (UEs) are considered. A quality-of-service (QoS)-constrained problem is then designed to maximize energy efficiency (EE) through the joint strategies of power allocation, resource block (RB) assignment, and power splitting ratio adjustment. By exploiting some mathematical transformations, the non-convex optimization problem can be solved by adopting the proposed joint wireless charging and hybrid power based resource allocation (JWHRA) algorithm. The efficiency of proposed algorithm is validated via simulation. Numerical results show that the proposed JWHRA scheme can provide higher EE with its joint design on wireless charging and hybrid energy source.

Discontinuous transmission-enabled licensed-assisted access for energy-efficient hetnets

In this paper, the spectrum sharing in both licensed and unlicensed bands under heterogeneous networks (HetNets) is investigated. A frame architecture of licensed-assisted access (LAA) in unlicensed bands is introduced by analyzing its statistical behavior of channel access opportunity. A multi-objective optimization problem is then designed for small cell to enhance energy efficiency by jointly maximizing the achievable sum-rate and minimizing the power consumption. The proposed discontinuous transmission (DTX)-enabled LAA (DLA) mechanism can be employed to find the Pareto optimal solution of resource allocation policy while guaranteeing the quality of service (QoS) of incumbent systems in both licensed and unlicensed bands. Numerical results show that the proposed DLA mechanism can enhance energy efficiency through dynamically allocating radio resources and adjusting system parameters to opportunistic share the licensed and unlicensed resources.

Energy minimization resource allocation schemes for relay-enhanced OFDMA networks

This paper studies the problem of joint allocation of subchannel, transmission power, and phase duration in the relay-enhanced bidirectional orthogonal frequency-division multiple access time division duplex systems. The challenges of this resource allocation problem arise from the complication of multiple-phase assignments within a subchannel since the relay station can provide an additional signal path from the base station to the user equipments (UEs). Existing research work does not fully consider all the influential factors to achieve feasible resource allocation for the relay-based networks. Since energy consumption is one of the principal issues, the energy minimization resource allocation (EMRA) schemes are proposed in this paper to design the allocation of subchannel, power, and phase duration for the UEs with the consideration of UE’s quality-of-service (QoS) requirements. Both the four-phase and two-phase bidirectional relaying assignments and the network coding technique are adopted to obtain the suboptimal solutions for the proposed EMRA schemes. Different weights are designed for the UEs to achieve the minimization of weighted system energy for the relay-enhanced networks. Simulation results show that the proposed EMRA schemes can provide comparably better energy conservation and outage performance with QoS support.

Channel-Aware Resource Allocation for Energy-Efficient Cloud Radio Access Networks Under Outage Specifications

This paper investigates a cloud radio access network (C-RAN) architecture for future wireless network, which focuses on centralized processing for spatially distributed remote radio heads (RRHs). Our main goal is to promote an energy efficient C-RAN under the consideration of multiple access interference (MAI) and imperfect channel state information at the transmitter (CSIT) via outage-aware resource allocation. The closed-form expression of fading-induced outage probability for each transceiver pair will be established by analyzing the statistical property of signal-to-interference-plus-noise. In the course of finding feasible solutions to enhance energy efficiency, the optimization procedures named C-RAN-based energy efficient power allocation (CEEPA) and low-complexity CEEPA are proposed. As the number of RRHs grows to infinity, the deterministic equivalents of performance metrics can be derived by applying recent results from random matrix theorem, which lead to an efficient way to obtain the asymptotic-optimal resource allocation policy. Also, the regularized zero-forcing precoding technique is adopted to mitigate MAI and can tackle the impact of imperfect CSIT thanks to the derivation of deterministic equivalents. Numerical simulations show that the proposed resource allocation schemes can provide better energy efficiency and the accuracy of asymptotic expressions is also verified. Furthermore, the merit of a distributed antenna system is demonstrated through the comparison with centralized antenna system.

Outage-based cooperative resource competition game in LTE-A small cell networks

To alleviate greenhouse effect, high network energy efficiency (EE) has increasingly become an important research target in wireless communication systems. In this paper, the investigation for resource blocks (RBs) and transmit power allocation to mitigate co-tier interference in small cell networks is provided. In view of the drawbacks for considerable computational loadings and the acquisition for global channel information under centralized control, this paper proposes a framework based on a cooperative game to distributively perform resource allocation with only limited information. Moreover, the outage probability analyses for both perfect and imperfect channel state information at the transmitter (CSIT) are discussed in this paper. Furthermore, a regret-based learning algorithm for the proposed outage-based cooperative resource competition game (OCRC) to reach the correlated equilibrium (CE) is adapted. Simulation results verify the convergence in proposed OCRC scheme. Additionally, the proposed OCRC scheme can maintain and provide better EE performance compared to equal power allocation (EPA) scheme with imperfect CSIT.