In the millimeter wave massive multiple-input multiple-output (MIMO) system, hybrid precoding is regarded as an effective solution to reduce hardware complexity and energy consumption. Due to the fixed connections between RF chains and antennas, the energy efficiency of hybrid precoding with fully-connected and sub-connected structure is limited. In this paper, we investigate a fully-adaptive-connected hybrid precoding with low-resolution phase shifters, where the connections between RF chains and antennas are dynamically controlled by switches. We formulate the problem of energy efficiency maximization as a non-coherent combining problem. Inspired by the cross-entropy (CE) optimization in machine learning, we propose a CE-based fully-adaptive hybrid precoding (CE-FAHP) algorithm, whose complexity is linear with the number of transmitted antennas. The probability mass function of all elements in analog precoder is updated iteratively by minimizing the CE, which is utilized to generate the near-optimal analog precoder. Simulation results demonstrate that the proposed algorithm can achieve higher energy efficiency than the existing matching assisted fully-adaptive hybrid precoding (MA-FAHP) algorithm with lower complexity.

Hybrid precoding and combining is a practical technique in MU-MIMO system with significantly reduced complexity. However, it still requires a complex analog network to connect the RF chains and antennas. In this paper, we propose a novel hybrid block diagonalization scheme for the downlink transmission with a compact RF structure. Specifically, proposed structure relies on domestic connection instead of global connection to link RF chains and antennas. Fix-degree phase shifters provide candidate signals and simple on-off switches are used to route the signal to antennas, so RF adders are not required. We formulate how to design the RF precoder and combiner by optimizing the probability distribution through partial cross-entropy minimization. Simulation results verify the performance of proposed scheme with different configurations.

An energy-efficient power control algorithm with max-min fairness for energy harvesting ad hoc networks is studied in this paper. Specially, each node of the network has an ability of harvesting energy from external environment, e.g., thermal or solar etc. We aim to maximize the minimum individual energy efficiency while guaranteeing the energy causality constraint and quality of service (QoS) of each node. The fairness-aware energy-efficient power control problem is formulated as a fractional nonconvex problem. To make the formulated problem tractable, we transform the original problem into an equivalent subtractive-form problem via Dinkelbach approach. A combinational framework of successive convex approximation (SCA) and geometric programming (GP) is further adopted to obtain a near optimal solution of the power control problem. Simulation results show that the proposed algorithm can provide greater fairness in energy harvesting ad hoc networks.

This paper investigates the resource allocation problem in full-duplex (FD) relay-assisted device-to-device (D2D) communications underlaying a multi-carrier cellular network. Under residual self-interference (SI), we aim to maximize the energy efficiency (EE) of D2D links via subcarrier assignment and power allocation while satisfying the quality of service (QoS) requirements of both D2D and cellular links. To solve the formulated EE maximization problem, we propose an iterative algorithm based on nonlinear fractional programming, where power allocation and subcarrier assignment are performed sequentially in each iteration, to handle the nonconvexity of the original problem. It is shown that the power allocation problem fits in D.C. (difference of two concave functions) programming while the subcarrier assignment corresponds to a job assignment problem whose optimal solution can be found in polynomial time. Finally, simulation results demonstrate that the proposed algorithm converges rapidly and achieves higher EE of D2D communications than existing schemes.

This paper investigates the physical layer security issue of a device-to-device (D2D) underlaid cellular system with a multi-antenna base station (BS) and a multi-antenna eavesdropper. To investigate the potential of D2D communication in improving network security, the conventional network without D2D users (DUs) is first considered. It is shown that the problem of maximizing the sum secrecy rate (SR) of cellular users (CUs) for this special case can be transformed to an assignment problem and optimally solved. Then, a D2D underlaid network is considered. Since the joint optimization of resource block (RB) allocation, CU-DU matching and power control is a mixed integer programming, the problem is difficult to handle. Hence, the RB assignment process is first conducted by ignoring D2D communication, and an iterative algorithm is then proposed to solve the remaining problem. Simulation results show that the sum SR of CUs can be greatly increased by D2D communication, and compared with the existing schemes, a better secrecy performance can be obtained by the proposed algorithms.

This paper considers the component carrier selection (CCS) problem for an LTE-U and WiFi coexistence network operating in an unlicensed spectrum band. An unlicensed component carrier selection (U-CCS) algorithm is proposed to select component carriers (CCs) for newly arrived LTE-U users. To support the U-CCS algorithm, an implementation of the carrier sense adaptive transmission (CSAT) mechanism is first presented to coordinate the transmission of LTE-U users and WiFi users on a single CC. The proposed U-CCS algorithm takes both carrier load and channel condition into account and aims to find a CC among all CCs at the arrival of a new LTE-U user, on which the user can achieve the maximum throughput, while ensuring that the average throughput of each WiFi user on the same CC meets the minimum throughput requirement. Simulation experiments show that the proposed U-CCS algorithm outperforms a couple of existing CC selection algorithms in terms of the minimum LTE-U user throughput, minimum WiFi user throughput, and newly arrived user throughput.

This paper investigates secrecy energy efficiency (SEE) optimization problem for simultaneous wireless information and power transfer (SWIPT) in a multiple-input single-output (MISO) downlink system, in which the legitimate user (LU) exploits power splitting (PS) scheme for information decoding (ID) and energy harvesting (EH) simultaneously. In order to prevent eavesdroppers from disturbing the reception of the legitimate user, BS also emits artificial noise (AN). The objective is to maximize SEE by jointly optimizing the confidential signal power, AN power and PS ratio while guaranteeing the secrecy rate, maximum transmitted power and minimum harvested energy constraints. The problem is nonconvex and hard to solve, we first convert it into an easier subtractive form, and then propose an effective two-stage algorithm. Numerical results illustrate the efficiency of the algorithm and the benefits of AN in enhancing the SEE performance.

This paper investigates the energy-efficient resource allocation problem for the energy harvesting-powered device-to-device (D2D) communications underlaying cellular networks under the harvest-then-transmit protocol. Our objective is to maximize the energy efficiency (EE) of D2D communications via joint time allocation and power control while satisfying the quality of service (QoS) requirement of cellular user (CU) and energy causality constraint. Exploiting the non-linear fractional programming, an iterative resource allocation scheme based on the Dinkelbach method is proposed to solve the formulated nonconvex optimization problem. For the optimization problem in each iteration, we decompose it into two subproblems: the power control subproblem and the time allocation subproblem. It is shown that the former subproblem fits in D.C. (difference of two concave functions) programming while the latter subproblem corresponds to a linear programming problem which can be efficiently solved. Simulation results validate the EE performance of our proposed scheme.

This paper is mainly to deal with the problem of long-term sparse channel estimation of coherent signals for mm-wave hybrid beamforming systems. The paper focuses on long-term beamforming, and it is based on large-scale fading channel directions but not the instantaneous small-scale fading gain, which reduces the computational complexity greatly. Moreover, the method the paper proposed couples 2-D beamspace MUSIC with the modified spatial smoothing with the advantages of processing coherent signals. It is also a compatible method which can be applied to the incoherent and coherent signals simultaneously. The computer simulation results verify the effectiveness and superiority of the proposed method when the signals are coherent.

This paper considers the blind estimation of the carrier frequency offset (CFO) and sample frequency offset (SFO) in orthogonal frequency division multiplexing (OFDM) system. Conventional methods require periodically transmitted pilot tones in frequency domain or identical training symbols in preamble. To achieve a blind estimation scheme, a joint maximum-likelihood (ML) estimator is proposed by applying another FFT within one symbol duration. Its simplified closed-form solution is then derived to reduce computational complexity. Theoretical analysis and simulation results confirm that the proposed low-complexity scheme achieves almost the same performance as ML estimation and outperforms other blind methods.

In this paper, a Stackelberg game based power control framework is proposed for interference coordination among device-to-device (D2D) communications and spectrum-sharing cellular networks. The cross-tier interference from D2D communications is controlled by the cellular base station (CBS) through setting a price for the shared subchannels. Based on the cross-tier interference price, each D2D pair adaptively updates its power strategy subsequently. A distributed power control algorithm with limited coordination among D2D pairs is proposed and guaranteed to converge to a Karush- Kuhn-Tucker (KKT) point of the optimization problem at D2D pairs's side by introducing a new co-tier price factor. At the CBS's side, a price update algorithm is designed ably according to cross-tier interference measurement. Simulation results show that the proposed algorithms can achieve higher sum rate for D2D communications when compared with that of the non-cooperative power allocation algorithm.

Due to the spatial sparsity caused by severe propagation loss, mm-wave channel estimation can be performed by estimating the directions and gains of the paths that have significant power. In this paper, we apply the beamspace 2-D multiple signal classification (MUSIC) method to estimate the path directions (the angles of departure and arrival) and use the least-squares method to estimate the path gains. Different from its element-space counterpart, the beamspace MUSIC method may exhibit spectrum ambiguity caused by the beamformers. In this paper, we therefore analyze the sufficient conditions on the beamformers under which the MUSIC spectrum has no ambiguity which also leads to the maximum number of resolvable path directions. Moreover, based on the uniform linear array with half-wavelength spacing, we show that the discrete Fourier transform beamformers, which are naturally analog and often employed in the mm-wave communication systems with hybrid precoding structure, can avoid the spectrum ambiguity and maximize the number of resolvable path directions. Simulation results demonstrate that the proposed 2-D beamspace MUSIC mm-wave channel estimator significantly outperforms existing estimators that are based on beam training and sparse recovery; and in the meantime, it requires much less training slots than these existing methods.

This paper considers the optimal energy-efficient transmission policy in time-sharing multiuser systems with a hybrid energy harvesting transmitter. The system operation energy is supplied by a constant energy source which is driven by a non-renewable resource and an energy harvester which harvests energy from its surrounding environment. Our goal is to maximize the energy-efficient transmission of multiuser systems considering the allocation of power and transmission time jointly in an off-line manner. The original nonconvex objective function is transformed into convex optimization problem via the fractional programming approach. Furtherly, we solve the convex problem by Lagrange dual decomposition method. Simulation results show that the proposed energy-efficient transmission policy has a better performance than the transmission policy which decomposes optimization problem into two parts (power allocation, time allocation) to solve iteratively.

In this paper, system throughput and user fairness for single-antenna user based on zero-forcing precoding are investigated with total power constraint (TPC) and per antenna power constraint (PAPC), respectively. By analyzing fairness of users signal to noise ratios (SNRs), the closed-form solution of fairness is obtained, and the tradeoff between system throughput and user fairness is presented. For multiple-antenna user, receive antenna selection (RAS) by maximizing channel norm is exploited to greatly reduce computational complexity. Simulation results show that different power constraints have a significant impact on system throughput and user fairness, and the throughput by considering user fairness decreases compared to that by considering maximum system throughput.

In this paper, we investigate the problem of maximizing the weighted sum energy efficiency (WS-EE) in downlink OFDMA small cell network. To solve the problem in a distributed manner and reduce the complexity, we decompose the resource blocks (RBs) allocation and the power control into two subproblems. For the subproblem of RBs allocation, the optimal RBs allocation strategy is obtained based on rate maximization given any feasible network power profile. For the subproblem of power control, a novel distributed pricing based algorithm is developed with limited inter-small cell coordination. Specifically, each small cell base station (SBS) updates its current interference information and broadcasts it to the other SBSs. With this collected information, the optimal power control strategy of each SBS is derived. Simulation results show that the proposed algorithm outperforms the existing algorithms in terms of the WS-EE.