Publications

This paper studies in-band interference of different kind of ultra wideband (UWB) signals. UWB frequency spectra are produced by using several types of narrow pulse waveforms, all based on the Gaussian pulse. Due to the extremely wide bandwidth, these signals spread over the frequency bands allocated to other radio systems. In-band interference power is calculated over the IF bandwidths of two victim receivers as a function of pulse width. Also, the signal attenuation with distance is presented. The victim systems under the study are GPS (L1-band) and GSM900. Based on the results, the 3/sup rd/ derivative of a Gaussian pulse seems to be the best choice, from the interference point of view, among the waveforms included in this study. It causes less interference than the Gaussian pulse or Gaussian doublet if the pulse widths are shorter than 1 ns. The system utilizing the direct sequence concept caused less in-band interference power than the system utilizing time hopping amongst all waveforms studied. The studied UWB systems are based on bi-phase baseband data modulation.

The fifth generation (5G) networks will increasingly target specific indoor deployments such as closed factory premises or open shopping malls for local providing of high-quality services. Local 5G network operator models, such as the newly proposed micro-operators, have gained increasing interest with guaranteed quality-of-service delivery, in spatially confined areas, especially inside the buildings. However, establishing local 5G networks with quality guarantees requires local spectrum access rights which are in the agendas of many national regulators. In this paper, we study the feasibility of indoor deployment of local 5G networks in two new 5G bands including 3.6 GHz and 26 GHz. We consider different deployment alternatives for a local 5G micro-operator in the bands with different antenna configurations and network densities. Then, we evaluate the impact of the different deployments on the performance of users via system level simulations of the average and cell-edge throughputs in downlink and uplink. Numerically, we have observed that the performance achieved in these two bands can be improved by increasing the base station (BS) density of the network. Furthermore, we have seen that the centre frequency does not significantly impact on the downlink performance unless the network is noise-limited. However, the uplink performance in the 26 GHz band is affected by the higher coupling losses between the BSs and the mobile terminals. We have also noticed that beamforming and wider channel bandwidths are useful for improving the network performance in 26 GHz band.

We consider the sum rate maximization problem in downlink OFDMA systems subject to total transmit power and minimum user rate constraints. A low complexity suboptimal resource allocation algorithm is proposed for joint optimization of multiuser subcarrier assignment and power allocation. The algorithm is based on Lagrangian relaxation where the non-convex optimization problem is decomposed into a master problem and a subproblem. Subproblem has a similar form as standard weighted sum rate maximization subject to total transmit power. Standard subgradient method is used in the master problem and a suboptimal algorithm inspired from primal decomposition techniques is also proposed to approximately solve the subproblem. Simulations are provided to compare the performance of the proposed algorithm to fixed spectrum assignment based algorithms with adaptive power control.

Underlay femtocells promise to enhance the coverage and rate performance of next-generation wireless networks. Nevertheless, many concerns still remain in the context of shared-spectrum operations and quality of service (QoS) provisioning at the macrocell users. In this paper, we introduce a novel approach for cooperative femtocell-to-femtocell interference management based on interference draining. Accordingly, a group of femtocells can decide to cooperate and improve their downlink rate, by exploiting the available frequency-spatial directions, and still guarantee a minimum target QoS at the closest MUEs. To address this problem, we use tools from cooperative game theory that enable the femtocells to decide, in a distributed manner, on whether to cooperate or not. In the proposed framework, each femtocell access point individually decides its own cooperative strategy, and maximizes a utility function that captures the cooperative gains and the limitations due to the macrocell users QoS targets. We show that, using the proposed approach, the femtocells can self-organize into a network partition composed of disjoint groups of femtocells which form the recursive core of the cooperative game. Simulation results show significant gains in terms of average payoff per femtocell, reaching up to 23%, for a network of K = 140 FBSs, relative to the non-cooperative approach.

Self-energy recycling (sER), which allows transmit energy re-utilization, has emerged as a viable option for improving the energy efficiency (EE) in low-power Internet of Things networks. In this work, we investigate its benefits also in terms of reliability improvements and compare the performance of full-duplex (FD) and half-duplex (HD) schemes when using multi-antenna techniques in a communication system. We analyze the trade-offs when considering not only the energy spent on transmission but also the circuitry power consumption, thus making the analysis of much more practical interest. In addition to the well known spectral efficiency improvements, results show that FD also outperforms HD in terms of reliability. We show that sER introduces not only benefits in EE matters but also some modifications on how to achieve maximum reliability fairness between uplink and downlink transmissions, which is the main goal in this work. In order to achieve this objective, we propose the use of a dynamic FD scheme where the small base station (SBS) determines the optimal allocation of antennas for transmission and reception. We show the significant improvement gains of this strategy for the system outage probability when compared to the simple HD and FD schemes.

Due to higher cost and spectrum scarcity, it is expected that an efficient use of spectrum in fifth generation (5G) networks will rather rely on sharing than exclusive licenses, especially when higher frequency allocations are considered. In this paper, the performance of a dense indoor multi-operator small cell network at 10 GHz is analyzed. The main goal is to show the benefits obtained at higher carrier frequency due to network densification while mobile network operators are sharing the spectrum. The analysis is assessed through extensive system level simulations. The main performance metrics are user throughput and signal-to-interference-and-noise ratio. Results show that when 10 GHz carrier frequency is used it allows higher network densities while satisfying user throughput requirements. However, when network is sparse lower carrier frequency leads to better performance. When network is dense, on average 2 Mb/s better mean throughput is achieved at 10 GHz when compared to traditional cellular frequency.

No abstract is provided for this article.

In this paper, we develop novel two-tier interference management strategies that enable macrocell users (MUEs) to improve their performance, with the help of open-access femtocells. To this end, we propose a rate-splitting technique using which the MUEs optimize their uplink transmissions by dividing their signals into two types: a coarse message that is intended for direct transmission to the macrocell base station and a fine message that is decoded by a neighboring femtocell and subsequently relayed over a heterogeneous (wireless/wired) backhaul. For deploying the proposed technique, we formulate a non-cooperative game between the MUEs in which each MUE can decide on its relaying femtocell while maximizing a utility function that captures both the achieved throughput and the expected backhaul delay. Simulation results show that the proposed approach yields up to 125% rate improvement and up to 2 times delay reduction with wired backhaul and, 150% rate improvement and up to 10 times delay reduction with wireless backhaul, relative to classical interference management approaches, with no cross-tier cooperation.

In this paper, we study a problem where multiple operators (OPs) need to share a common pool of spectrum with each other. Our objective is to maximize the social welfare, defined as the overall weighted sum rate of the OPs. The problem is decomposed into two parts: the first part is to allocate RBs to OPs, which we do so by extending the framework of many-to-one matching game with externalities. The second part is to allocate power of small cell base stations (SBSs) belonging to each OP, which is accomplished using reinforcement learning. Assuming that the SBSs associated with each OPs are spatially distributed according to Poisson point process (PPP), we show that pairwise stable matchings achieve local maximas of the social welfare function. We propose two algorithms to search for the stable matchings. Simulation results show that these algorithms are well behaved in terms of convergence and efficiency of the solutions.

This paper presents a framework to evaluate the probability that a decision error event occurs in wireless sensor networks, including sensing and communication errors. We consider a scenario where sensors need to identify whether a given event has occurred based on its periodic, noisy, observations of a given signal. Such information about the signal needs to be sent to a fusion center that decides about the actual state at that specific observation time. The communication links -- single- or multi-hop -- are modeled as binary symmetric channels, which may have different error probabilities. The decision at the fusion center is based on OR, AND, K-OUT-OF-N and MAJORITY Boolean operations on the received signals associated to individual sensor observations. We derive closed-form equations for the average decision error probability as a function of the system parameters (e.g. number of sensors and hops) and the input signal characterization. Our analyses show the best decision rule is closely related to the frequency that the observed events occur and the number of sensors. In our numerical example, we show that the AND rule outperforms MAJORITY if such an event is rare and there is only a handful number of sensors. Conversely, if there is a large number of sensors or more evenly distributed event occurrences, the MAJORITY is the best choice. We further show that, while the error probability using the MAJORITY rule asymptotically goes to 0 with increasing number of sensors, it is also more susceptible to higher channel error probabilities.

In this paper, a new full-duplex (FD) relaying scheme for a cooperative cognitive underlay network is proposed. The secondary network is composed of one secondary transmitter, one full-duplex secondary relay, and one secondary destination. The relay employs the selective-decode-and-forward (SDF) protocol. The secondary destination jointly decodes the signals from the secondary transmitter and the FD secondary relay so that the direct link can be seen as useful information rather than interference. The analysis includes the effect of the interference from the primary transmitter and the self-interference at the relay. Under equal power allocation strategy, closed-form expressions for the outage probability are derived for the proposed FD cooperative cognitive scheme, and the feasibility of FD relaying under cognitive constraints is shown. Our results also reveal that the proposed full-duplex joint-decoding (FDJD) cognitive network considerably outperforms the known full-duplex dual-hop (FDDH) scheme. Moreover, we propose an optimal power allocation (OPA) scheme. On the basis of the signal-to-interference-plus noise of the secondary network, the OPA strategy can choose between two modes of operation, cooperation between source and relay or source transmission only. Our results show that the FDJD scheme under the proposed OPA policy presents the best performance among all schemes investigated in this paper.

This letter studies content caching in cloud-aided wireless networks, where small cell base stations with limited storage are connected to the cloud via limited capacity fronthaul links. By formulating a utility (inverse of service delay) maximization problem, we propose a cache update algorithm based on spatio-temporal traffic demands. To account for the large number of contents, we propose a content clustering algorithm to group similar contents. Subsequently, with the aid of regret learning at small cell base stations and the cloud, each base station caches contents based on the learned content popularity subject to its storage constraints. The performance of the proposed caching algorithm is evaluated for sparse and dense environments, while investigating the tradeoff between global and local class popularity. Simulation results show 15% and 40% gains in the proposed method compared to various baselines.