Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System

This paper studies beam allocation and power optimization scheme to decrease the hardware cost and downlink power consumption of a multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. Our target is to improve energy efficiency (EE) and decrease power consumption without obvious system performance loss. To this end, we propose a beam allocation and power optimization scheme. First, the problem of beam allocation and power optimization is formulated as a multivariate mixed-integer non-linear programming problem. Second, due to the non-convexity of this problem, we decompose it into two sub-problems which are beam allocation and power optimization. Finally, the beam allocation problem is solved by using a convex optimization technique. We solve the power optimization problem in two steps. First, the non-convex problem is converted into a convex problem by using a quadratic transformation scheme. The second step implements Lagrange dual and sub-gradient methods to solve the optimization problem. Performance analysis and simulation results show that the proposed algorithm performs almost identical to the exhaustive search (ES) method, while the greedy beam allocation and suboptimal beam allocation methods are far from the ES. Furthermore, experiment results demonstrated that our proposed algorithm outperforms the compared the greedy beam allocation method and the suboptimal beam allocation scheme in terms of average service ratio.

Caching deployment based on energy efficiency in device-to-device cooperative networks

The rapid growth of mobile data traffic demand will cause congestion to the future communication network. The cache-enabled device-to-device communication has been proven to effectively enhance the performance of wireless communication networks. This article investigates the caching deployment problem from the energy efficiency in the cache-enabled device-to-device networks. According to the random geometry theory modeling, the closed form expression of energy efficiency is derived, which measures the average number of successful transmitted file bits per unit time and per unit power consumption. And then we establish an optimization problem to maximize energy efficiency. As the formulated optimization problem is a multiple-ratio fractional programming problem that cannot be solved conveniently, we propose a quadratic transformation method to nest in the energy efficiency maximization problem. To tackle this problem, an iterative optimization algorithm is proposed to optimize the caching policy and network energy efficiency. The simulation results demonstrate that the proposed policy can achieve higher energy efficiency and hit probability in the cache-enabled device-to-device network.

Some q-Supercongruences from Transformation Formulas for Basic Hypergeometric Series

Several new q-supercongruences are obtained using transformation formulas for basic hypergeometric series, together with various techniques such as suitably combining terms, and creative microscoping, a method recently developed by the first author in collaboration with Zudilin. More concretely, the results in this paper include q-analogues of supercongruences (referring to p-adic identities remaining valid for some higher power of p) established by Long, by Long and Ramakrishna, and several other q-supercongruences. The six basic hypergeometric transformation formulas which are made use of are Watson’s transformation, a quadratic transformation of Rahman, a cubic transformation of Gasper and Rahman, a quartic transformation of Gasper and Rahman, a double series transformation of Ismail, Rahman and Suslov, and a new transformation formula for a nonterminating very-well-poised $${}_{12}\phi _{11}$$ 12 ϕ 11 series. Also, the nonterminating q-Dixon summation formula is used. A special case of the new $${}_{12}\phi _{11}$$ 12 ϕ 11 transformation formula is further utilized to obtain a generalization of Rogers’ linearization formula for the continuous q-ultraspherical polynomials.

Quadratic transformation of multivariate aggregation functions

In this work, we prove that quadratic transformations of aggregation functions must come from quadratic aggregation functions. We also show that this is different from quadratic transformations of (multivariate) semi-copulas and quasi-copulas. In the latter case, those two classes are actually the same and consists of convex combinations of the identity map and another fixed quadratic transformation. In other words, it is a convex set with two extreme points. This result is different from the bivariate case in which the two classes are different and both are convex with four extreme points.

ANALYSIS OF RESIDUES AS METHOD FOR THE FORMALIZATION OF CHANGES IN COMPOSITION WINTER DIESEL FUEL AFTER CAVITATIONAL IMPACT

An example of the cumulant analysis of the work and the quadratic transformation centered and non-centered random variables

Energy-Efficient Resource Allocation for NOMA-based Backscatter Communications

Backscatter communication (BackCom) is a promising technique for achieving high spectrum efficiency and power efficiency in the future Internet of Things systems. The capacity of BackCom networks can be maximized by optimizing the backscatter time and the reflection coefficient (RC). However, system energy efficiency (EE) cannot be guaranteed usually. In this paper, we investigate the energy-efficient resource allocation problem of a non-orthogonal multiple access (NOMA)-based BackCom. Particularly, the base station (BS) transmits signals to two cellular users based on the NOMA protocol, meanwhile, a backscatter device backscatters the signals to users using the passive radio technology. The total EE of the considered system is maximized by jointly optimizing power allocation for each NOMA user and the RC of backscatter device where the decoding order and the quality of service (QoS) of each user are guaranteed. To solve such a non-convex problem, we develop an efficient iterative algorithm to obtain the optimal solutions by using Dinkelbach's method and the quadratic transformation approach. Numerical results show that the proposed algorithm can significantly improve the system EE compared with the orthogonal multiple access (OMA) scheme and the NOMA system without backscatter devices.

Energy-Efficient Resource Allocation for NOMA-based Backscatter Communications

Backscatter communication (BackCom) is a promising technique for achieving high spectrum efficiency and power efficiency in the future Internet of Things systems. The capacity of BackCom networks can be maximized by optimizing the backscatter time and the reflection coefficient (RC). However, system energy efficiency (EE) cannot be guaranteed usually. In this paper, we investigate the energy-efficient resource allocation problem of a non-orthogonal multiple access (NOMA)-based BackCom. Particularly, the base station (BS) transmits signals to two cellular users based on the NOMA protocol, meanwhile, a backscatter device backscatters the signals to users using the passive radio technology. The total EE of the considered system is maximized by jointly optimizing power allocation for each NOMA user and the RC of backscatter device where the decoding order and the quality of service (QoS) of each user are guaranteed. To solve such a non-convex problem, we develop an efficient iterative algorithm to obtain the optimal solutions by using Dinkelbach's method and the quadratic transformation approach. Numerical results show that the proposed algorithm can significantly improve the system EE compared with the orthogonal multiple access (OMA) scheme and the NOMA system without backscatter devices.