The 802.11ax standard final specification is expected in 2019, however first parameters are just released. The target of the new standard is four times improvement of the average throughput within the given area. This standard is dedicated for usage in dense environment such as stadiums, means of municipal communication, conference halls and others. The main target is to support many users at the same time with the single access point. The question arises if the new standard will have higher throughput then previous ones in the single user mode. The author calculated the maximal theoretical throughput of the 802.11ax standard and compared the results with the throughput of older 802.11 standards such as 802.11n and 802.11ac. The new he-wifi-network example included in the ns-3.27 release of the NS-3 simulator was used to simulate the throughput between the access point and the user terminal. The results indicate that in some conditions the 802.11ac standard has higher throughput than the new 802.11ax standard.
Cloud radio access network (C-RAN) has been proposed as a solution to reducing the huge cost of network upgrade while providing the spectral and energy efficiency needed for the new generation cellular networks. In order to reduce the interference that occur in C-RAN and maximize throughput, this paper proposes a sequentially distributed coalition formation (SDCF) game in which players, in this case the remote radio heads (RRHs), can sequentially join multiple coalitions to maximize their throughput. Contrary to overlapping coalition formation (OCF) game where players contribute fractions of their limited resources to different coalitions, the SDCF game offers better stability by allowing sequential coalition formation depending on the availability of resources and therefore providing a balance between efficient spectrum use and interference management. An algorithm for the proposed model is developed based on the merge-only method. The performance of the proposed algorithm in terms of stability, complexity and convergence to final coalition structure is also investigated. Simulation results show that the proposed SDCF game did not only maximize the throughput in the C-RAN, but it also shows better performances and larger capabilities to manage interference with increasing number of RRHs compared to existing methods.
This work presents concepts of the use of algorithms inspired by the functions and properties of the nervous system in dense wireless networks. In particular, selected features of the brain consisting of a large number of nerve connections were analyzed, which is why they are a good model for a dense network. In addition, the action of a selected cells from the nervous system (such as neuron, microglia or astrocyte) as well as phenomena observed in it (e.g. neuroplasticity) are presented.
In this study, the concepts of simultaneous user association and resource allocation in non-orthogonal multiple access systems have been investigated. Subscribers are randomly distributed in them. In the paper, a novel cooperative energy harvesting model is introduced so that user equipment near to the base stations acts as relay for further subscribers. In order to consider the local limitations of alternative energy resources, it was assumed that alternative energy would be shared among the base stations by means of the dynamic grid network. In this architecture, non-orthogonal resource allocation and user association frameworks should be reconfigured because conventional schemes use orthogonal multiple access. Hence, this paper suggests a novel approach to joint optimum cooperative power allocation and user association techniques to achieve a maximum degree of energy efficiency for the whole system in which the quality of experience parameters are assumed to be bounded during multi-cell multicast sessions. The model was also modified to develop joint multi-layered resource control and user association that can distinguish the service pattern in cooperative energy heterogeneous systems with non-orthogonal multiple access to obtain more resource optimality than in the current approaches. The effectiveness of the suggested approach is confirmed by numerical results. Also, the results reveal that non-orthogonal multiple access can provide greater energy efficiency than the conventional orthogonal multiple access approaches such as e.g. the MAX-SINR scheme.
One of the ways to improve calculations related to determining the position of a node in the IoT measurement system is to use artificial neural networks (ANN) to calculate coordinates. The method described in the article is based on the measurement of the RSSI (Received Signal Strength Indicator), which value is then processed by the neural network. Hence, the proposed system works in two stages. In the first stage, RSSI coefficient samples are taken, and then the node location is determined on an ongoing basis. Coordinates anchor nodes (i.e. sensors with fixed and previously known positions) and the matrix of RSSI coefficients are used in the learning process of the neural network. Then the RSSI matrix determined for the system in which the nodes with unknown positions are located is fed into the neural network inputs. The result of the work is a system and algorithm that allows determining the location of the object without processing data separately in nodes with low computational performance.