Wi-Fi Access Point (AP) optimization approaches are used in indoor positioning systems for signal coverage enhancement, as well as positioning precision improvement. Although the huge power consumption of the AP optimization forms a serious problem due to the signal coverage requirement for large-scale indoor environment, the conventional approaches treat the problem of power consumption independent from the design of indoor positioning systems. This paper proposes a new Fast Water-filling algorithm Group Power Constraint (FWA-GPC) based Wi-Fi AP optimization approach for indoor positioning in which the power consumed by the AP optimization is significantly considered. This paper has three contributions. First, it is not restricted to conventional concept of one AP for one candidate AP location, but considered spare APs once the active APs break off. Second, it utilizes the concept of water-filling model from adaptive channel power allocation to calculate the number of APs for each candidate AP location by maximizing the location fingerprint discrimination. Third, it uses a fast version, namely Fast Water-filling algorithm, to search for the optimal solution efficiently. The experimental results conducted in two typical indoor Wi-Fi environments prove that the proposed FWA-GPC performs better than the conventional AP optimization approaches. equal power consumed by each AP. The proposed approach examined in two representative indoor environments covered by multiple Wi-Fi APs. The experimental results report that the proposed FWA-GPC the average positioning error by 24.91%-29.24% and power consumption by 15.98%-30.03%, compared with the conventional Random, Max Mean, and InfoGain approaches. Furthermore, exploring the AP optimization approach for multi-floor indoor Wi-Fi environment forms an interesting topic in future.

Group Power Constraint Based Wi-Fi Access Point Optimization for Indoor Positioning

Water-filling (WF) is a well-established iterative solution to optimal power allocation in parallel fading channels. Slow iterative search can be impractical for allocating power to a large number of OFDM sub-channels. Neural networks (NN) can transform the iterative WF threshold search process into a direct high-dimensional mapping from channel gain to transmit power solution. Our results show that the NN can perform very well (error 0.05%) and can be shown to be indeed performing approximate WF power allocation. However, there is no guarantee on the NN is mapping between channel states and power output. Here, we attempt to explain the NN power allocation solution via the Meijer G-function as a general explainable symbolic mapping. Our early results indicate that whilst the Meijer G-function has universal representation potential, its large search space means finding the best symbolic representation is challenging.

Approximate Symbolic Explanation for Neural Network Enabled Water-Filling Power Allocation

Multi-channel optimisation relies on accurate channel state information (CSI) estimation Error distributions in CSI can propagate through optimisation algorithms to cause undesirable uncertainty in the solution space The transformation of uncertainty distributions differs between classic heuristic and Neural Network (NN) algorithms Here, we investigate how CSI uncertainty transforms from an additive Gaussian error in CSI into different power allocation distributions in a multi-channel system We offer theoretical insight into the uncertainty propagation for both Water-filling (WF) power allocation in comparison to diverse NN algorithms We use the Kullback–Leibler divergence to quantify uncertainty deviation from the trusted WF algorithm and offer some insight into the role of NN structure and activation functions on the uncertainty divergence, where we found that the activation function choice is more important than the size of the neural network

/pdf/uncertainty-propagation-in-neural-network-enabled-multi-1u01ae2ex2.pdf

Uncertainty Propagation in Neural Network Enabled Multi-Channel Optimisation

In this paper, we evaluate a downlink MultiUser Multiple-Input Multiple-Output (MU-MIMO) scenario, in which a 802.11ac access point with multiple antennas (up to 10) is transmitting to two receivers, each one with two antennas. Block diagonalization (BD) method is investigated under the Equivalent Isotropic Radiated Power (EIRP) constraint. This study shows that scaling the transmitted power according to the EIRP constraint can improve the multiuser (MU) sum capacity to single-user (SU) capacity ratio compared to the gain achieved under the transmitted power constraint.

https://hal.archives-ouvertes.fr/hal-01149441/document

Impact of EIRP Constraint on MU-MIMO 802.11ac Capacity Gain in Home Networks

The deployment facility and flexibility of wireless technologies have allowed considerable evolution of IEEE 802 .11 standards. The arrival of the optical fiber to the home has further motivated the research studies to increase data rates and capacity. ln this context, the latest IEEE 802.l lac standard ratified in 2014 proposes new technologies to achieve maximum data rates above 1 Gbit/s. ln particular, 802.l lac includes the Multi-User Multiple Input Multiple Output (MU-MIMO) and Transmit Beamforming (TxBF). These smart antenna technologies assume a variable transmit antenna pattern in function of the users' position and the corresponding multipath propagation channel characteristics. However, the actual radio planning tools give a fixed coverage map in dependent of any adaptive or smart antenna processing. This thesis studies the impact of the MU-MIMO and TxBF techniques on radio engineering, system optimization as well as radio planning tools. Indeed, the first part of this thesis deals with refining the sum rate capacity gains by identifying interesting system configurations and optimal system parameters. The second part addresses the beamforming gains in terms of the Signal-to-Noise Ratio (SNR) gain and interference level to have realistic radio coverage modeling.

Study of smart antennas and MU-MIMO techniques for indoor radio engineering and planning tools

In this paper, we investigate the sum rate of multiuser multiple-input multiple-output broadcast (MU-MIMO) communication systems, the channel state information (CSI) is supposed perfectly known at the base station. Since power allocation is not a convex optimization problem in the presence of multiuser interference (MUI) in general, the optimal solutions proposed in the literature have exponential computational complexity, which is hard to implement for practice. We propose an iterative water-filling algorithm that takes advantage of the classical simple water-filling principle. The computational complexity is significantly reduced in the proposed algorithm compared with the methods in the literature only with a negligible performance degradation. In addition, the generalized eigenvalue technique for beamforming design is utilized in this paper for minimizing MUI, the number of users and the number of antennas of each user can be arbitrary. Simulations show that the sum rate of the proposed method is close to the sum capacity of the MU-MIMO broadcast channel, especially in low signal-to-noise ratio (SNR) region.

Efficient iterative water-filling power allocation method in MU-MIMO broadcast channels

In this paper, sum rate optimization of multiuser multiple-input multiple-output broadcast (MU-MIMO) communication systems with perfect channel state information (CSI) at the base station is investigated. Since power allocation is a signomial optimization problem in the presence of multiuser interference (MUI), it is not a convex problem in general. Several optimal solutions proposed in the literature have exponential computational complexity, which is hard to implement for practice. We propose an iterative water-filling algorithm that takes advantage of the classical simple water-filling principle. The proposed algorithm reduces significantly the computational complexity compared with the methods in the literature only with a negligible performance degradation. In addition, the generalized eigenvalue technique for beamforming design is utilized in this paper for minimizing MUI, the number of users and the number of antennas of each user can be arbitrary. Simulations show that the sum rate of the proposed method is close to the sum capacity of the MU-MIMO broadcast channel, especially in low signal-to-noise ratio (SNR) region.

Efficient power allocation strategy in multiuser MIMO broadcast channels

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Efficient iterative water-filling power allocation method in MU-MIMO broadcast channels

Efficient power allocation strategy in multiuser MIMO broadcast channels