In this letter, we propose an online power allocation (PA) method to maximize energy efficiency (EE) in energy harvesting systems with realistic battery constraints for two receivers. The optimization problem in this system configuration is challenging since it has a non-convex fractional form and the information of channel quality and the harvested energy can only be obtained causally in practice. To overcome these issues, we design time average EE maximization through Lyapunov optimization techniques where the transmission power is computed with current battery information and channel fading. Moreover, based on random matrix theory, we present a simplified online EE algorithm. Numerical experiments verify that the proposed PA outperforms conventional online approaches with much reduced computational complexity.

In this paper, we investigate a new solution for the energy efficiency (EE) maximization and power allocation problem in point-to-point multiple-input multiple-output (MIMO) spatial multiplexing schemes. Different from conventional energy-efficient optimization approaches that require iterative numerical algorithms, we derive an optimal solution in a closed form, which provides an insight upon the relation between the optimum EE and system parameters such as circuit power and channel conditions. In addition, using the proposed closed form function, we present an upper bound on the optimum EE in terms of the full active transmit antennas parameters. Based on the derived upper bound, we also propose a new antenna selection algorithm which achieves almost the same performance as the optimum solution with much reduced complexity.

In this paper, we consider a multi-user (MU) radio-frequency (RF) beam training scenario with user selection. We propose a new beam training scheme that alleviates the latency issue in the conventional IEEE 802.11ad by adopting a sequential downlink-downlink transmit sector sweep combination. Then, we analyze the average rate performance of several RF beam training schemes in two different asymptotic scenarios. In addition, we characterize the performance gain of the proposed method over other schemes. Our analytic results confirm that the proposed method achieves the average rate performance of the optimal fullsearch method in the asymptotic region with much reduced training overhead. It is shown from the simulation results that the proposed scheme outperforms the conventional beam training schemes and achieves a 35% performance gain over the optimal full search scheme when considering the beam training overhead in practical MU millimeter-wave channel environments. We also confirm that our analytical results match well with the numerical results.

In this paper, we study a joint spatial division multiplexing (JSDM) beamforming scheme, which enables large-scale spatial multiplexing gains for massive multi-input multi-output downlink systems. In contrast to the conventional JSDM, which employs a block diagonalization method as a pre-beamformer, we aim to maximize sum-rate by applying minimum-mean-squared error (MMSE) approaches when designing a pre-beamformer and a multi-user precoder sequentially. First, to suppress inter-group interference, we design the pre-beamformer, which minimizes an upper bound of the sum mean-squared-error in the large-scale array regime. Then, to mitigate same-group interference, we present the multi-user precoder based on the weighted MMSE (WMMSE) optimization method, which requires the same channel state information overhead as the conventional JSDM. Also, in order to reduce the computational complexity, we compute deterministic equivalents of the WMMSE beamforming parameters to generate the beamformers by employing asymptotic results of large system analysis. Through simulation results, we confirm that the proposed two-step beamforming methods bring substantial performance gains in terms of sum-rate over the conventional JSDM schemes especially in a low and medium signal-to-noise ratio regime with comparable complexity.

When implementing single radio frequency (RF) chain multi-user (MU) millimeter-wave (mmWave) systems, an RF beamforming algorithm with a short beam training overhead is essential. In this paper, we propose a new MU RF beamforming algorithm based on the conventional RF beam training method in IEEE 802.11ad. Then, we investigate its asymptotic behavior for a large number of users scenario. We show that the proposed scheme approaches the optimal full search scheme with much reduced beam training latency as the number of users grows. Our simulation results demonstrate that a performance gain of the proposed method over the full search method is about 35% in practical MU mmWave environments in terms of the effective data rate.

This paper considers the maximization of total throughput and device lifetime for point-to-point multiple-input multiple-output communication systems, where a transmission node has a battery which exhibits non-linear battery discharge behaviors. We adopt a battery model called Peukert’s law to render the non-linear battery characteristics and formulate the battery constraint from Peukert’s law. Then, we prove that the total throughput is a strictly concave function in terms of the battery lifetime under the battery constraint. Also, we derive the optimality conditions for the total throughput maximization and device lifetime maximization problems from the strict concavity, and compute the optimal solutions by a bi-section method. Furthermore, we provide a solution based on asymptotic analysis for the case, where the eigenvalue distribution of the channel matrix is not available. In the simulation section, we conduct accurate battery discharge simulations to validate this paper. We confirm that the analysis matches well with the battery simulations, and the derived optimal scheme outperforms the baseline schemes, which neglect the non-linear battery discharge properties. Also, the proposed solution based on asymptotic analysis is shown to have almost the same performance compared with the optimal solution.

In this paper, we focus on maximizing weighted sum energy efficiency (EE) for a multi-cell multi-user channel. In order to solve this non-convex problem, we first decompose the original problem into a sequence of parallel subproblems which can optimized separately. For each subproblem, a base station employs dirty paper coding to maximize the EE for users within a cell while regulating interference induced to other cells. Since each subproblem can be transformed to a convex multiple-access channel problem, the proposed method provides a closed-form solution for power allocation. Then, based on the derived optimal covariance matrix for each subproblem, a local optimal solution is obtained to maximize the sum EE. Finally, simulation results show that our algorithm based on non-linear precoding achieves about 20 percent performance gains over the conventional linear precoding method.

In this paper, we propose a new beamforming design to maximize energy efficiency (EE) for multiple input single output interfering broadcast channels (IFBC). Under this model, the EE problem is non-convex due to the coupled interference and its fractional form, and thus it is difficult to solve the problem. Conventional algorithms which address this problem have adopted an iterative method for each channel realization, which requires high computational complexity. In order to reduce the computational complexity, we parameterize the beamforming vector by scalar parameters related to beam direction and power. Then, by employing asymptotic results of random matrix theory, we identify the optimal parameters to maximize the EE in the large system limit. Based on the asymptotic results, the proposed scheme can provide insights on the average EE performance, and a simple yet efficient beamforming strategy is introduced for the finite system case. Numerical results confirm that the proposed scheme shows a negligible performance loss compared to the best result achieved by the conventional approaches even with small system dimensions, with much reduced system complexity.

In this paper, we focus on maximizing weighted sum energy efficiency (EE) for a multi-cell multi-user channel. In order to solve this non-convex problem, we first decompose the original problem into a sequence of parallel subproblems which can be optimized separately. For each subproblem, a base station employs dirty paper coding to maximize the EE for users within the cell while regulating interference induced to other cells. Since each subproblem can be transformed to a convex multiple-access channel problem, the proposed method provide a closed-form power allocation. Then, based on the optimal covariance matrix, a locally optimal solution is obtained to maximize the sum EE. Finally, simulation results show that our algorithm based on the non-linear precoding achieves close to 20 percent gain than the conventional linear precoding method.

In this paper, we investigate an energy efficiency (EE) maximization problem in multi-user multiple input single output downlink channels. In this system model, the optimization problem is difficult to solve since it is in a non-convex fractional form. Hence, conventional algorithms have addressed the problem in an iterative manner for each channel realization that leads to high computational complexity. To tackle this complexity issue, we propose a new simple method based on the fact that the EE maximization is identical to the spectral efficiency maximization for the region of power below the certain transmit power referred to as saturation power. In order to determine the saturation power, we introduce upper and lower bounds of the EE performance by adopting maximal ratio transmission beamforming strategy. Then, we propose an efficient way to compute the saturation power for the maximization problem in closed form. Based on the derived saturation power, we suggest a simplified scheme to calculate EE with low complexity. The saturation power is parameterized by employing random matrix theory, which relies only on the second order channel statistics. Numerical results validate that the proposed algorithm achieves near optimal EE performance with much reduced complexity.

In this paper, we analyze the error probability and ergodic capacity performance for diversity reception schemes over generalized-K fading channels using a mixture gamma (MG) distribution. With high accuracy, the MG distribution can approximate a variety of composite fading channel models and provide mathematically tractable properties. In contrast to previous analysis approaches that require complicated signal-to-noise ratio (SNR) statistics, it is shown that a distribution of the received SNR for diversity reception schemes is composed of a weighted sum of gamma distributions by exploiting the properties of the MG distribution. Then, based on this result, we can derive the exact average symbol error probability and simple closed-form expressions of diversity and array gains for maximal ratio combining and selection combining. In addition, an expression of the ergodic capacity for these schemes is obtained in independent and identically distributed fading channels. Our results lead to meaningful insights for determining the system performance with parameters of the MG distribution. We show that our analysis can be expressed with any number of receiver branches over various fading conditions. Numerical results confirm that the derived error probability and ergodic capacity expressions match well with the empirical results.

A signature identification algorithm is a method to obtain the cell identification information for wireless cellular systems or determine the intended user for wireless local area network. In this paper, we propose a simple and efficient signature identification algorithm on the basis of Zadoff-Chu sequence in orthogonal frequency division multiplexing systems. In addition, we prove that the proposed algorithm achieves a maximum likelihood solution if the receiver knows the channel length. Also, the exact probabilities of signature identification failures of the proposed algorithm are provided for different power delay profiles. To demonstrate efficacy of the proposed algorithm in fading channels, we derive the failure probability at high signal-to-noise ratio (SNR). Through a high SNR expression, it is shown that the proposed algorithm fully exploits frequency selective fadings. Especially, we reveal that the slope of the failure probability curves at high SNR is determined by the channel length regardless of power delay profiles. Simulation results show that the proposed algorithm outperforms conventional signature algorithms in frequency selective fading channels. Also, we confirm that our analysis matches well with the empirical results of the proposed signature identification algorithm.

In this paper, we investigate the performance for diversity reception schemes over composite fading channels using a mixture gamma (MG) distribution. With high accuracy, the MG distribution can approximate a variety of composite fading channel models and provide mathematically tractable properties. In contrast to previous analysis approaches which require complicated signal-to-noise ratio statistics, we derive simple closed-form expressions of a diversity and array gain for important diversity reception schemes such as maximum ratio combining and selection combining by adopting the MG distribution. Our results lead to meaningful insights for determining the system performance with parameters of the MG distribution. We observe that for various transmitted signal modulations, our proposed analysis can be expressed with the general number of receiver branches over independent and non-identically distributed composite fading cases. Simulation results confirm that the derived diversity and array gain match well with the empirical results.