Time Division Multiple Access Manner Research Articles

Chance-constrained resource allocation for cognitive wireless-powered backscatter communication networks

With the rapid development of the Internet of Things (IoT) and the increasing number of wireless nodes, the problems of scare spectrum and energy supply of nodes have become main issues. To achieve green IoT techniques and resolve the challenge of wireless power supply, wireless-powered backscatter communication as a promising transmission paradigm has been concerned by many scholars. In wireless-powered backscatter communication networks, the passive backscatter nodes can harvest the ambient radio frequency signals for the devices’ wireless charging and also reflect some information signals to the information receiver in a low-power-consumption way. To balance the relationship between the amount of energy harvesting and the amount of information rate, resource allocation is a key technique in wireless-powered backscatter communication networks. However, most of the current resource allocation algorithms assume available perfect channel state information and limited spectrum resource, it is impractical for actual backscatter systems due to the impact of channel delays, the nonlinearity of hardware circuits and quantization errors that may increase the possibility of outage probability. To this end, we investigate a robust resource allocation problem to improve system robustness and spectrum efficiency in a cognitive wireless-powered backscatter communication network, where secondary transmitters can work at the backscattering transmission mode and the harvest-then-transmit mode by a time division multiple access manner. The total throughput of the secondary users is maximized by jointly optimizing the transmission time, the transmit power, and the reflection coefficients of secondary transmitters under the constraints on the throughput outage probability of the users. To tackle the non-convex problem, we design a robust resource allocation algorithm to obtain the optimal solution by using the proper variable substitution method and Lagrange dual theory. Simulation results verify the effectiveness of the proposed algorithm in terms of lower outage probabilities.

Latency-Critical Downlink Multiple Access: A Hybrid Approach and Reliability Maximization

In this work, we study a downlink multi-user network, where a single access point (AP) is supposed to accomplish data transmissions to all users under low latency constraints. To more effectively cope with the multiple access demand, we consider a hybrid strategy for the multi-user downlink service in finite blocklength (FBL) regime, which combines broadcasting with time-division multiple access (TDMA). In the hybrid strategy, the users are first clustered into different groups. Different groups are served in a TDMA manner with dedicated time slots, while users within each group are served together via a broadcasting signal from the AP. By taking into account the fairness of transmission reliability among all users, we formulate a problem minimizing the maximum error probability among users via jointly determining the user grouping and allocating blocklength among all groups. To address the complicated non-convex problem, we first characterize the optimal blocklength allocation under each given grouping decision, which leads to an optimal closed-form allocation solution via solving an equation system. Based on the characterized features, we are enabled to efficiently distill out the optimal grouping from all possible groupings, which forms the efficient optimal solution for the optimal joint design. Afterwards, aiming at a complexity reduction, we further propose a low-complexity iterative solution, in which the grouping is iteratively improved via the introduced operations until a convergence to a suboptimum. Finally, via simulations, we validate the proposed solutions and reveal the close optimality of the iterative solution. In addition, the hybrid strategy has shown a significant reliability advantage in comparison to pure broadcasting or TDMA, and this performance advantage becomes further enlarged in case of more users.

Heuristic-based channel selection with enhanced deep learning for heart disease prediction under WBAN

The main intention of this proposal is to design and develop a new heart disease prediction model via WBAN using three stages. The first stage is data aggregation, in which data is scheduled in Time Division Multiple Access manner based on priority level, and the data from the public benchmark datasets are collected representing WBAN. In the second stage, a channel selection is performed using a developed hybrid metaheuristic algorithm named Tunicate Swarm-Sail Fish Optimization (TS-SFO) Algorithm. The main intention of the suggested channel selection algorithm is to solve the multi-objective problem based on certain constraints like Reference Signal Received Quality, Signal to Noise Ratio and channel capacity. The third stage is the heart disease prediction stage, in which the feature extraction and prediction are performed. The data transmitted in the selected channel is used for the feature extraction phase, where the weighted entropy-based statistical feature extraction is developed and extracts the essential statistical features. Then, an enhanced Recurrent Neural Network (RNN) is proposed by tuning certain parameters using the proposed TS-SFO for predicting heart disease with the help of extracted statistical features. Test results show that the flexible design and subsequent tuning of RNN hyper-parameters can achieve a high prediction rate.

Interference Management in Ultradense Networks: A User-Centric Coalition Formation Game Approach

Ultradense networks have been identified as a promising technology to accomplish objectives of the fifth-generation wireless networks. However, the severe mutual interference generated by the densely deployed femtocells constitutes a great challenge. Different from the most prior studies that center on femtocell access points (FAPs) and neglect the influence of users’ location when allocating subchannels, a centralized user-centric merge-and-split rule based coalition formation game, which can be well supported in the framework of the cloud/centralized radio access network, is proposed. This user-centric game makes it possible to utilize user information (e.g., distance) in estimating interuser interference so that the interference mitigation can be more accurate and effective. Besides, a novel resource allocation algorithm based on graph theory is presented. It can eliminate intratier interference efficiently by allocating users who may severely interfere each other in the conflict-graph with orthogonal subchannels as far as possible in a distance-aware sequence, and allocating subchannels in a profit-calculating method if idle subchannels are unavailable. Furthermore, in order to overcome the limitation that “only one subchannel can be allocated to each user” in previous coalitional games, a supplementary allocation algorithm is put forward to allocate remainder subchannels such that the system spectral efficiency can be improved. Simulation results show that the proposed algorithms improve the aggregate throughput up to 51.04%, 62.70%, 157.46%, and 482.42% comparing with the coalition formation game transmitted in a time-division multiple access (TDMA) manner within each coalition with virtual multiple-input multiple-output (MIMO) case, the reuse 1 case, the FAP-centric coalition formation game with modified recursive core case, and the coalition formation game transmitted in a TDMA manner among users with virtual MIMO case, respectively. The coalitional game proposed in this paper converges to a final stable partition in finite iterations.

Dynamic Resource Allocation for Energy Efficient Transmission in Digital Subscriber Lines

Linear matrix precoding, also known as vectoring, is a well-known technique to mitigate multiuser interference in the downlink digital subscriber line (DSL) transmission. While effective in canceling interference, vectoring does incur major communication overhead and computational overhead in terms of the transmission of idle symbols and precoder-data multiplications at each data frame, resulting in significant energy consumption when the number of lines is large. To facilitate energy efficient transmission, it has been recently proposed (in the G.fast standard) that each data frame is divided into a normal operating interval (NOI) and a discontinuous operating interval (DOI). In the NOI, all lines (or users) are involved in a common vectoring group, which requires a large matrix precoder, whereas in the DOI, the lines are subdivided into multiple small nonoverlapping vectoring subgroups and are transmitted in a time division multiple access manner, requiring small matrix multiplications and, thus, improving the energy efficiency. In this paper, we consider the key dynamic resource allocation problems in downlink DSL: given the real-time demands, determine the optimal transmission scheme: The optimal NOI and DOI size in each data frame as well as the optimal grouping strategy in the DOI, and optimally adjust the transmission scheme. We formulate these optimal dynamic resource allocation problems and propose efficient real-time algorithms to solve them to global optimality. Simulation results are shown to demonstrate the efficiency and the effectiveness of the proposed algorithms.

Secrecy Performance Optimization for Wireless Powered Communication Networks With an Energy Harvesting Jammer

In this paper, we consider a wireless powered communication network with an energy harvesting (EH) jammer where eavesdroppers try to wiretap the communication between users and a hybrid access-point (H-AP). In our system, the H-AP first transmits an energy signal to recharge the batteries of the EH users and the EH jammer in the energy transfer (ET) phase. Then, in the subsequent information transfer (IT) phase, each user sends information to the H-AP in a time division multiple access manner, while the jammer generates jamming signals to interfere the eavesdroppers. We adopt two different secrecy performance measurements according to the level of channel state information (CSI) of the eavesdroppers. First, with a single user, we maximize the secrecy rate by optimizing the time allocation between the ET and the IT phase when perfect CSI of the eavesdroppers is available at all nodes. In contrast, when the instantaneous CSI of the eavesdroppers is not available at legitimate nodes, we analyze and minimize the secrecy outage probability. We also extend the single user analysis to a more general multi-user situation with an additional consideration of the transmit power allocation at the jammer. Finally, we evaluate the performance of our proposed solutions through simulations and demonstrate that a performance gain compared to conventional schemes becomes more pronounced with the increased number of eavesdroppers and users.

Joint Power and Time Allocation in Full-Duplex Wireless Powered Communication Networks

We consider a full-duplex wireless powered communication network (WPCN) with one hybrid access point (H-AP) and a set of distributed users, where downlink wireless energy broadcasting is employed at H-AP and at the same time, uplink wireless information transmission takes place at users in a time-division multiple access manner. We extend proportional fair scheduler to this category of network when dealing with "doubly near-far problem," where users far away from H-AP achieve low throughput but suffer from both low harvested energy and high data transmission power consumption. We jointly optimize power and time allocation for each user to achieve proportional fairness while controlling the energy consumption offset for network to a low level. By using optimization techniques, the optimal transmit power and transmission time for users are obtained via proposed algorithm. Simulation results confirm the positive effect on improving the fairness metric and reducing energy consumption offset for network.