In this paper, a quality of experience (QoE)- oriented beam management and admission control (AC) problem is investigated with consideration of time-varying channels and delay requirement for different functional split (FS) millimeter (mm)-wave fronthaul networks. The original problem aims to maximize time-averaged QoE of distributed units (DUs) with co-existence of short-term and long-term constraints, which makes the considered problem intractable. By leveraging Lyapunov optimization technique, the formulated problem is converted into a series of AC and beam allocation (BA) problems. Two AC and BA (ACBA) algorithms are proposed to solve the converted problem based on whether it is implemented at central unit (CU-ACBA) or DU (DU-ACBA). Simulation results demonstrate technical insights and applicability of the proposed algorithms by indicating the appropriate scenario for each FS option (FSO).

Long range wide area networks (LoRaWAN) is one of the novel specification for the applications of Internet-of-Things. Recent research focused on the propagation model and coverage discussion based on experimental measurement. In this paper, power- class analytical model is firstly proposed to model the operations of LoRaWAN end-devices for throughput and power consumption. Based on this model, the energy efficient class-selection algorithm (EECA) is proposed to achieve the optimal energy efficiency with the consideration of power consumption on end-devices by the suitable class selection scheme. A system level simulation has been conducted for performance evaluation. The simulation results provide the guideline for system parameters configuration and class selection on each end-devices under different scenarios.

In this paper, we study joint interference cancellation and resource allocation for full-duplex (FD) cloud radio access networks (C-RANs) with antenna correlation. The target is to maximize the capacity of downlink (DL) user equipments (UEs) with guaranteed quality of service (QoS) of uplink (UL) UEs under the constraints of DL and UL transmit power. Intractability of the considered problem involves non-convexity and coupling between postcoding and remote radio head (RRH) selection. To deal with the high coupling between system variables, an estimation-free self-interference (SI) cancellation (EFSC) scheme with merit of significantly reducing signaling overhead for channel estimation is proposed. The reduced signaling overhead is also derived in closed-form expression. Furthermore, we propose a generalized Bender's decomposition-based resource allocation (GRA) algorithm, which separates the continuous and discrete variables to solve the optimization problem. With the design of a flexible utility function, the tradeoff between DL capacity and co-channel interference (CCI) can be achieved. Moreover, we identify the scenario in which antenna correlation will be beneficial for both the UL and DL communications in FD C-RAN with the implementation of EFSC scheme. The effectiveness of the proposed methods and theorems are verified via simulation results.

Non-orthogonal multiple access (NOMA) has been envisioned as a useful component of fifth generation (5G) mobile networks. As imperfect channel state information (CSI) due to channel estimation errors poses problems for most wireless receivers, it presents even greater challenges in successive interference cancellation (SIC) reception of NOMA signals. We present a novel approach to the multi-user detection problem by exploiting the important constraints of forward error correction (FEC) code word. We devise our new receiver based on the minimum output energy (MOE) criterion while preserving a distortionless response to the user equipment (UE) of interest. In particular, we efficiently adopt the UE signatures presented by the FEC channel codes under distinct permutations to separate desired signals of interest from interfering UEs. We formulate our receiver optimization into a quadratic-programming problem anchored with a set of code constraints. Our simulations demonstrate that the proposed code-anchored quadratic programming (CQP) receiver can accurately improve SIC performance and provide robustness to CSI errors better than other legacy schemes.

In this paper, licensed-assisted access (LAA) for dual-band small cell network is investigated. A framework for LTE coexisting with incumbent unlicensed systems is firstly introduced. The analytical expressions are also derived to characterize the transmission strategies for both LTE and wireless fidelity (WiFi) systems over unlicensed spectrum. Based on the natures provided by analytical models, a discontinuous transmission (DTX)-enabled LAA (DLA) mechanism is proposed to maximize energy efficiency with quality- of-service (QoS) support. Also, a dual access-based opportunistic traffic offloading (DOTO) scheme is developed to determine whether traffic offloading or channel aggregation, i.e., WiFi or LAA, shall be adopted in unlicensed spectrum. Numerical results demonstrate that the proposed DLA mechanism can dynamically adjust DTX pattern to opportunistic share unlicensed spectrum or achieve energy conservation from the perspective of energy efficiency. The tradeoff between traffic offloading and channel aggregation over unlicensed spectrum is also provided by evaluating the performance of proposed DOTO scheme.

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.

The uplink spectrum of cellular networks is often underutilized due to the asymmetric behavior of wireless data services. To enhance the efficiency, a full-duplex (FD) cellular system is proposed to reuse the uplink spectrum for concurrent uplink and downlink transmissions. By jointly designing transmit beamforming and power control, a weighted sum-interference power minimization problem is formulated for downlink users to manage the self- interference and multiuser interference with the guaranteed QoS of uplink users. An iterative joint transmit beamforming and power control (TB-PC) algorithm is then proposed to solve the joint design problem based on the dual decomposition. The performance is validated by computer simulation, and the proposed FD scheme can potentially improve the spectrum efficiency when a small serving cell is considered for downlink users, as compared with the conventional half- duplex scheme.

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.

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.

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.

Full-duplex (FD) wireless communication systems have become an important research issue since it can increase spectral efficiency through higher spectrum utilization. The main challenge in achieving FD is the cancellation of strong interference at the received interface from the transmitted interface of a node, namely, self-interference (SI). Therefore, a technique for SI cancellation using an oblique projection-based postcoding (OPP) scheme is proposed in this paper. The major merit of the proposed OPP method is the non-necessity of SI channel estimation, which can reduce the signaling overhead and can be adopted even when the SI channel distribution is unknown. Simulation results show that the proposed OPP scheme can achieve significant performance gain in terms of signal-to-interference-plus-noise ratio (SINR) compared to conventional method.

Recently, the centralized management in small cell (SC) networks enabled by the existence of a central controller and backhaul links is developed to meet the growing data demand. The effect of load-balancing under the considerations of limited capacity backhaul and co-channel interference is investigated in this paper. Our main goal is to maximize energy efficiency (EE) in SC network through a joint decision strategy consists of user association and radio resource allocation with quality-of-service (QoS) support. To tackle this mixed combinatorial optimization problem, a load-balanced user association and radio resource allocation (LB-UARA) mechanism based on the quantum-behaved particle swarm optimization algorithm is proposed. Simulation results demonstrate that the objectives of QoS satisfaction and energy conservation can be realized via load-balancing procedure in our proposed LB-UARA mechanism.

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.

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.

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.

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.