A sum inverse energy efficiency (SIEE) minimization problem is solved. Compared with conventional sum energy efficiency (EE) maximization problems, minimizing SIEE achieves a better fairness. The paper begins by proposing a framework for solving sum-fraction minimization (SFMin) problems, then uses a novel transform to solve the SIEE minimization problem in a multiple base station (BS) system. After the reformulation into a multi-convex problem, the alternating direction method of multipliers (ADMM) is used to further simplify the problem. Numerical results confirm the efficiency of the transform and the fairness improvement of the SIEE minimization. Simulation results show that the algorithm convergences fast and the ADMM method is efficient.

In this paper, we consider a fog computing system consisting of a multi-antenna access point (AP), an ultra-low power (ULP) single antenna device and a fog server. The ULP device is assumed to be capable of both energy harvesting (EH) and information decoding (ID) using a time-switching simultaneous wireless information and power transfer (SWIPT) scheme. The ULP device deploys the harvested energy for ID and either local computing or offloading the computations to the fog server depending on which strategy is most energy efficient. In this scenario, we optimize the time slots devoted to EH, ID and local computation as well as the time slot and power required for the offloading to minimize the energy cost of the ULP device. Numerical results are provided to study the effectiveness of the optimized fog computing system and the relevant challenges.

In this paper, we consider a simultaneous wireless information and power transfer (SWIPT)-based mobile edge computing (MEC) system consisting of a multi-antenna access point (AP), multiple single antenna ultra-low power devices (ULPDs) and a MEC server. The objective is to process the information gathered by the ULPDs relying only on the energy obtained through RF energy harvesting (EH). To achieve this, the computation burden is partially assigned to the ULPDs and partially offtoaded to a MEC server. It is assumed that the AP simultaneously delivers power and control signals to the ULPDs by using SWIPT. The ULP network exploits the harvested energy for decoding the control message and either locally computing or offloading the tasks to the MEC server. In this scenario, we jointly optimize the offloading decisions, the time slots devoted to EH, information decoding, local computation/offloading and the offloading powers to minimize the total energy cost of the ULPDs. Numerical results illustrate the effectiveness of the proposed framework.

In this paper, we consider a time-switching (TS) co-located simultaneous wireless information and power transfer (SWIPT) system consisting of multiple multi-antenna access points which serve multiple single antenna users. In this scenario, we propose a multi-objective optimization (MOO) framework to design jointly the Pareto optimal beamforming vector and the TS ratio for each receiver. The objective is to maximize the utility vector including the achieved data rates and the harvested energies of all users simultaneously. This problem is a non-convex rank-constrained MOO problem which is relaxed and transformed into a non-convex semidefinite program (SDP) based on the weighted Chebycheff method. The majorization-minimization algorithm is utilized to solve the nonconvex SDP and the optimal solution is proved to satisfy the rank constraint. We also study the problem of optimizing the beamforming vectors in a fixed TS ratio scenario with the same approach. Numerical results are provided for two coordinated access points with MISO configuration. The results illustrate the trade-off between harvested energy and information data rate objectives and show the effect of optimizing the precoding strategy and TS ratio on this trade-off.

In this study, the authors investigate the resource allocation issue for sensing-based orthogonal frequency-division multiple access (OFDMA) cognitive radio networks. They consider a network consisting multiple secondary users (SUs) and a secondary base station (BS) implementing a two-phase protocol. In the first phase, cooperative spectrum sensing is carried out to detect the vacant subchannels. In the second phase, SUs transmit data in the uplink to the BS by using OFDMA. They optimise the sensing parameters, transmit power and subchannel assignments jointly to minimise the total energy consumption with the constraints on SUs’ quality of service and detection probability of the primary user. This is a mixed binary integer programming problem which is NP (non-deterministic polynomial-time)-hard and generally intractable. They represent the problem as a bilevel problem and propose two efficient algorithms to solve the slave and master subproblems. They also study the separate optimisation, in which the sensing parameters of SUs are set regardless of the allocated resources. They investigate the energy savings of joint versus separate optimisation using numerical experiments. The results show that the joint optimisation method can introduce up to 16% of energy saving in zero sensing signal-to-noise ratio with the same total transmission bandwidth of 2.5 MHz.

In this paper, we consider a time-switching (TS) co-located simultaneous wireless information and power transfer (SWIPT) system consisting of multiple multi-antenna access points which serve multiple single antenna users. In this scenario, we design jointly the optimal transmit precoding covariance matrix and the TS ratio for each receiver to maximize the utility vector made of the achieved data rates and the energy harvested of all users simultaneously. This is a non-convex multi-objective optimization problem which has been transformed into an equivalent non-convex semidefinite programming and solved using local optimization method of sequential convex programming. Numerical results illustrate the trade-off between energy harvested and information data rate objectives and show the effect of optimizing the precoding strategy and TS ratio on this trade-off.

In this paper, we investigate the resource allocation issue for cooperative sensing-based cognitive radio networks (CRNs). The CRN consists of multiple secondary users (SUs) and a secondary base station (SBS), and implements a two-phase protocol. In the first phase, cooperative spectrum sensing is carried out to detect the vacant channels in a primary user network. In the second phase, the SUs transmit data in the uplink to the SBS. We optimize the sensing parameters, transmit power and channel assignments of the SUs jointly to minimize the total energy consumption with the constraints on the SUs' quality of service and detection probability of the primary users. We represent the problem as a bilevel optimization problem, in which the slave subproblem is to optimize the resource allocation parameters for any given sensing parameter, whereas the master subproblem is to optimize the sensing parameters. To benchmark the performance of our proposed joint optimization solution, we study the separate optimization, in which the sensing parameters of SUs are set regardless of the allocated resources. Numerical results show that, our proposed joint optimization method can introduce up to 15.07% and 15.6% energy saving with the total transmission bandwidth of 1.25 MHz and 2.5 MHz, respectively.

In this paper, we investigate the power allocation issue for cooperative-sensing-based code-division multiple access (CDMA) cognitive radio (CR) networks. We consider a network consisting of multiple secondary users (SUs) and a secondary base station (BS) implementing a two-phase protocol. In the first phase, censor-based cooperative spectrum sensing is carried out to detect the PU's presence. When the channel is estimated to be free, SUs transmit data in the uplink to the BS in the second phase by using CDMA. We optimize the sensing parameters and transmit power of SUs jointly to minimize the total energy consumption with the constraints on SUs' quality of service (QoS) and detection probability of the PU. This is a nonconvex problem, which we represent as a monotonic optimization problem and solve by means of monotonic programming. Furthermore, we study the separate optimization problem in which sensing parameters and transmit power are optimized to minimize the energy consumption of the first phase and the second phase disjointly. Numerical results show that the proposed joint optimization method saves energy consumption significantly in lower signal-to-noise ratios (SNRs). It can introduce as high as 3.9 and 8.7 dB joule energy saving per time slot in zero-sensing SNR under the same data rate constraints of 0.5 and 1 bit/s/Hz, respectively.

The authors consider centralised cooperative spectrum sensing under correlated shadowing in this study. Formulating the spectrum sensing problem as a Gauss–Gauss hypothesis test, they use a linear quadratic rule and show that it is the optimal detector under Bayesian criterion. They derive the upper and lower Bhattacharyya bounds and investigate the error performance of spectrum sensing by studying the behaviour of these upper and lower bounds. They also study the asymptotic error performance in two different scenarios of finite and infinite area networks. They show that by increasing the number of nodes the sensing error probability approaches zero in both cases but with different decay rates. The lower the correlation between nodes or the larger the network area, the faster the decay.

Cooperative spectrum sensing is one of the most important proposed methods to combat with fading, shadowing, and hidden primary user problems. Also, censoring idea was proposed to reduce the communication overhead in cooperative spectrum sensing. In this paper, optimal censor-based strategies are investigated. Optimization problem is formulated under the bandwidth constraint for fixed local rule scenario, fixed fusion rule (OR) scenario, and also joint optimization of local and fusion rules. Solutions to the first two problems are found analytically and the joint optimization problem is solved by means of an iterative search algorithm. Simulation results are also presented to study the impact of the communication constraint on the censoring scheme and the detection performance.

CFAR (Constant False-Alarm Rate) processors are useful for detecting radar targets in a background for which the parameters in the statistical distribution are not known. A variety of CFAR techniques such as CA (Cell Averaging), Go (Greatest Of), SO (Smallest Of), OS (Ordered Statistics) and ACMLD (Automatic Censored Mean-Level Detector) processors have been proposed for SISO (Single Input–Single Output) radars in a non-homogeneous background. In this paper, conventional CFAR algorithms including CA, SO, OS and ACMLD processors are generalized for MIMO (Multiple Input–Multiple Output) radars. The exact expressions for false-alarm probabilities of the proposed algorithms in a homogeneous background are presented. In addition, the detection performance of the proposed detectors is studied by means of simulation in the presence of interfering targets and also colored Gaussian clutter. Besides, the proposed CFAR processors are compared, and it is shown that the ACML-based algorithm is superior to the other investigated methods.

Constant False Alarm Rate (CFAR) processors are useful for detecting radar targets in background for which all parameters in the statistical distribution are not known and may be nonstationary. Many CFAR techniques have been proposed for conventional radars in nonhomogeneous background. One of the most robust CFAR detectors in multiple target situations is Automatic Censored Mean Level Detector (ACMLD) which does not require any prior knowledge about the number of interfering targets. In this paper, ACMLD algorithm is generalized for Multiple Input-Multiple Output (MIMO) radar. An exact expression for the false alarm probability of the proposed algorithm (M-ACMLD) is presented. Then, the performance analysis of M-ACMLD is studied in both homogeneous environment and multiple target situations.