Virtual reality (VR) setups often can be characterized as Body Area Networks (BANs) where one of the transceivers is mounted on the head of the user. In order to design and assess such systems, suitable channel models need to be established. This paper presents a novel model of the propagation channel covering both attenuation and delay dispersion for such BANs. The modeling approach separates the on-body propagation from the contributions of the environment, and is thus easy to generalize to other environments as well. The model is parameterized based on an existing measurement campaign, and can thus be directly used for BAN network simulations.

LTE-U (LTE-Unlicensed) is designed to coexist with Wi-Fi in the unlicensed band by balancing its duty cycle according to the number of coexisting Wi-Fi access points (APs) it detects. For example, a LTE-U base-station (BS) will reduce its duty cycle from 50% to 33% when it senses an increase in the number of co-channel Wi-Fi basic service sets (BSSs) from one to two. But the problem of detecting how many WiFi BSS' are operating on the channel in real-time, without decoding the Wi-Fi header, still remains. In this paper, we present a novel algorithm that solves the problem by using an autocorrelation (AC) function on the Wi-Fi preamble and setting appropriate detection thresholds to infer the number of Wi-Fi BSSs operating on the channel. Performing auto-correlation on the Wi-Fi preamble is a much simpler operation than decoding the entire Wi-Fi packet, which is what would be needed if one were to decode the MAC header to identify the BSS. We implement and experimentally validate the proposed AC detector and demonstrate that there is a differentiable pattern of AC events between one and two Wi-Fi APs. From the collected AC events, we determine a suitable threshold for a reliable detection of Wi-Fi APs. We show that using an AC threshold of NE= 0.8, we can achieve a probability of detection (PD) of 0.9 with a probability of false alarm (PFA) of less than 0.02. Finally, we demonstrate that the performance of the proposed AC detector is superior in terms of PDand PFA compared with the energy detector (ED).

In this paper we address the issue of association fairness when Wi-Fi and LTE unlicensed (LTE-U) coexist on the same channel in the unlicensed 5 GHz band. Since beacon transmission is the first step in starting the association process in Wi-Fi, we define association fairness as how fair LTE-U is in allowing Wi-Fi to start transmitting beacons on a channel that it occupies with a very large duty cycle. According to the LTE-U specification, if a LTE-U base station determines that a channel is vacant, it can transmit for up to 20 ms and turn OFF for only 1 ms, resulting in a duty cycle of 95%. In an area with heavy spectrum usage, there will be cases when a Wi-Fi access point wishes to share the same channel, as it does today with Wi-Fi. We study, both theoretically and experimentally, the effect that such a large LTE-U duty cycle can have on the association process, specifically Wi-Fi beacon transmission and reception. We demonstrate via an experimental set-up using National Instrument (NI) USRPs that a significant percentage of Wi-Fi beacons will either not be transmitted in a timely fashion or will not be received at the LTE-U BS thus making it difficult for the LTE-U BS to adapt its duty cycle in response to the Wi-Fi usage. Our experimental results corroborate our theoretical analysis. We compare the results with Wi-Fi/Wi-Fi coexistence and demonstrate that LTE-U/Wi-Fi coexistence is not fair when it comes to initial association since there is a much larger percentage of beacon errors in the latter case. Hence, the results in the paper indicate that in order to maintain association fairness, a LTE-U BS should not transmit at such high duty cycles, even if it deems the channel to be vacant.

We present an extrinsic information transfer (EXIT) chart-based design technique for irregular repeat-accumulate (IRA) codes used in 2-D magnetic recording (TDMR) turbo-equalization systems. The channel model includes Voronoi magnetic grains, 2-D intersymbol interference (2D-ISI) and additive white Gaussian noise (AWGN). The receiver uses a 2D-ISI BCJR equalizer and an IRA decoder. For one outer equalizer-decoder iteration, we propose theory and simulation-based methods for computing EXIT curves. The simulation method calculates experimental EXIT curves for the check node decoder (CND) and the combination of the variable node decoder (VND) and an equalizer. The theoretical approach recursively calculates CND and VND Gaussian mixture model parameters in order to calculate EXIT curves. We then fit the VND and CND EXIT curves to find optimized variable node degree distributions. Simulation results show that the TDMR-optimized IRA codes achieve up to a 6.2% density increase in user bits/grain (U/G) compared with IRA codes designed for AWGN channels. The theory-based code designs achieve the same or better U/G as the simulation-based designs, but require 98% less design computation time. We also derive optimized IRA codes for iterative turbo-equalization; these codes can achieve simultaneous U/G gains and SNR savings compared with AWGN-optimized codes.

This paper considers turbo equalization for 2-D magnetic recording. Magnetic grains are modeled as Voronoi regions of randomly distributed nuclei. Bits read from the magnetic grain model flow into a 2-D intersymbol interference (2D-ISI) model including additive white Gaussian noise. At high bit densities, some bits are not written on any grain, and hence are effectively “overwritten” by surrounding bits. The proposed system iteratively exchanges log-likelihood ratios (LLRs) between a 2D-ISI equalizer based on the forward-backward algorithm and an irregular repeat-accumulate (IRA) decoder. To combat bit overwrites, the system employs a non-linear function to map 2D-ISI extrinsic output LLRs to IRA decoder input LLRs. To pass back LLRs from the IRA decoder to the 2D-ISI equalizer, we design a simple likelihood-ratio-based LLR estimator. Simulations of the proposed system that employ the perturbed-bit-centers grain model proposed in a 2010 IEEE Transactions on Magnetics paper show a 6.5% increase in user bits per grain (U/G) and a 16.4 dB signal-to-noise ratio (SNR) gain compared with the previous paper, without iterative turbo equalization. Utilizing the LLR estimator to do iterative detection results in SNR gains of up to 1.7 dB compared with non-iterative detection. The random Voronoi model employed in this paper appears to be more difficult to equalize than the grain model in the 2010 paper. The proposed system with random Voronoi model achieves 0.4422 U/G at SNR =11.6 dB, i.e., about 8.8 Tb/in2at (typically assumed future grain density) 20 Tgr/in2; this is almost ten times the density of current systems at 10 Tgr/in2.

Spectrum handoff in cognitive radio technology has been emerged as a new method for improving performance of the cognitive radio networks. The authors propose a proactive spectrum handoff protocol based on a single rendezvous (SRV) coordination scheme without common control channel. This protocol utilises a multi‐user greedy channel selection method to select the best channel based on minimum service time for the secondary users to achieve a higher aggregate throughput. The authors proposed a novel theoretical analysis to evaluate the aggregate throughput of the secondary users in the proposed protocol. It uses Markov Chain to model the distributed channel selection scheme. The simulation results show the proposed protocol increases the throughput up to 38.7% in comparison with the study by Song and Xie, for 12 secondary users in the network with transmission rates of primary and secondary users equal to 5 (pkt/s) and 500 (pkt/s), respectively.