This paper introduces a protocol that distributively constructs a collision-free schedule for multi-hop packet radio networks in the presence of hidden terminals. As a preliminary step, each wireless station computes the schedule length after gathering information about the number of flows in its neighbourhood. Then, a combination of deterministic and random backoffs are used to reach a collision-free schedule. A deterministic backoff is used after successful transmissions and a random backoff is used otherwise. It is explained that the short acknowledgement control packets can easily result in channel time fragmentation and, to avoid this, the use of link layer delayed acknowledgements is advocated and implemented. The performance results show that a collision-free protocol easily outperforms a collision-prone protocol such as Aloha. The time that is required for the network to converge to a collision-free schedule is assessed by means of simulation.
Later works have explored the validity of the idea for different kinds of traffic @cite_23 , modelled the network performance metrics @cite_24 , and studied the possibility of traffic differentiation @cite_5 . A comprehensive simulation study, together with a model of the learning process is presented in @cite_18 . This last paper also includes performance measures in non-ideal conditions, such as the presence of legacy stations or channel errors. More results regarding the learning process and fairness with legacy stations can be found in @cite_3 . An evaluation of a collision-free protocol and its interaction with the autorate fallback mechanism (ARF) in realistic multiple-input-multiple-output (MIMO) channels is presented in @cite_14 .
@cite_18 @cite_14 @cite_3 @cite_24 @cite_23 @cite_5
2094331586 2493390539 2953153691 347411582
Understanding and solving performance-related issues of current and future (5G+) networks requires the availability of realistic, yet simple and manageable, traffic models which capture and regenerate various properties of real traffic with sufficient accuracy and minimum number of parameters. Traffic in wireless cellular networks must be modeled in the space domain as well as the time domain. Modeling traffic in the time domain has been investigated well. However, for modeling the User Equipment (UE) distribution in the space domain, either the unrealistic uniform Poisson model, or some non-adjustable model, or specifc data from operators, is commonly used. In this paper, stochastic geometry is used to explain the similarities of traffic modeling in the time domain and the space domain. It is shown that traffic modeling in the time domain is a special one-dimensional case of traffic modeling in the space domain. Unified and non-parameterized metrics for characterizing the heterogeneity of traffic in the time domain and the space domain are proposed and their equivalence to the inter-arrival time, a well accepted metric in the time domain, is demonstrated. Coefficient of Variation (CoV), the normalized second-order statistic, is suggested as an appropriate statistical property of traffic to be measured. Simulation results show that the proposed metrics capture the properties of traffic more accurately than the existing metrics. Finally, the performance of LTE networks under modeled traffic using the new metrics is illustrated. We investigate the uplink throughput achievable by a multiple-user (MU) massive multiple-input multiple-output (MIMO) system, in which the base station is equipped with a large number of low-resolution analog-to-digital converters (ADCs). Our focus is on the case where neither the transmitter nor the receiver have any a priori channel state information. This implies that the fading realizations have to be learned through pilot transmission followed by channel estimation at the receiver, based on coarsely quantized observations. We propose a novel channel estimator, based on Bussgang’s decomposition, and a novel approximation to the rate achievable with finite-resolution ADCs, both for the case of finite-cardinality constellations and of Gaussian inputs, that is accurate for a broad range of system parameters. Through numerical results, we illustrate that, for the 1-bit quantized case, pilot-based channel estimation together with maximal-ratio combing, or zero-forcing detection enables reliable multi-user communication with high-order constellations, in spite of the severe nonlinearity introduced by the ADCs. Furthermore, we show that the rate achievable in the infinite-resolution (no quantization) case can be approached using ADCs with only a few bits of resolution. We finally investigate the robustness of low-ADC-resolution MU-MIMO uplink against receive power imbalances between the different users, caused for example by imperfect power control. We present a new stochastic service model with capacity sharing and interruptions, appropriate for the evaluation of the quality of real-time streaming (RTS), like e.g. mobile TV, in wireless cellular networks. The general model takes into account multi-class Markovian process of call arrivals, (to capture different radio channel conditions, requested streaming bit-rates and durations) and allows for a general resource allocation policy saying which users are temporarily denied the requested fixed streaming bit-rates (put in outage) due to resource constraints. We give expressions for several important performance characteristics of the model, including mean time spent in outage and mean number of outage incidents for a typical user of a given class. These expressions involve only stationary probabilities of the (free) traffic demand process, which is a vector of independent Poisson random variables describing the number of users of different classes. In order to analyze RTS in 3GPP Long Term Evolution (LTE) cellular networks, we specify our general model assuming orthogonal user channels with the peak bit-rates close to the theoretical Shannon's bound in the additive white Gaussian noise (AWGN) channel, which leads to the resource constraints in a multi-rate linear form. In this setting we consider a natural class of least-effort-served-first resource allocation policies, for which the characteristics of the model can be further evaluated using Fourier analysis of Poisson variables. Within this class we identify and evaluate an optimal and a fair policy, the latter being suggested by LTE implementations. We also propose some intermediate policies, which allow to solve the optimality fairness tradeoff caused by unequal user radio-channel conditions. Our results can be used for the evaluation of the quality of RTS in LTE networks and dimensioning of these networks. We consider a MIMO fading broadcast channel and compute achievable ergodic rates when channel state information is acquired at the receivers via downlink training and explicit channel feedback is performed to provide transmitter channel state information (CSIT). Both “analog” and quantized (digital) channel feedback are analyzed, and digital feedback is shown to be potentially superior when the feedback channel uses per channel coefficient is larger than 1. Also, we show that by proper design of the digital feedback link, errors in the feedback have a relatively minor effect even if simple uncoded modulation is used on the feedback channel. We extend our analysis to the case of fading MIMO Multiaccess Channel (MIMO-MAC) in the feedback link, as well as to the case of a time-varying channel and feedback delay. We show that by exploiting the MIMO-MAC nature of the uplink channel, a fully scalable system with both downlink multiplexing gain and feedback redundancy proportional to the number of base station antennas can be achieved. Furthermore, the feedback strategy is optimized by a non-trivial combination of time-division and space-division multiple-access. For the case of delayed feedback, we show that in the realistic case where the fading process has (normalized) maximum Doppler frequency shift 0 F < 1=2, a fraction 1 2F of the optimal multiplexing gain is achievable. The general conclusion of this work is that very significant downlink throughput is achievable with simple and efficient channel state feedback, provided that the feedback link is properly designed.