Publications

Bi-directional signaling for dynamic TDD with decentralized beamforming

This paper introduces a novel bi-directional signaling scheme for decentralized beamformer design to maximize the weighted sum rate (WSR) of multi-cell multi-user multiple-input multiple-output dynamic time division duplexing (TDD) system. In particular, the base stations are assumed to be either in uplink or downlink state based on the instantaneous traffic demand. The WSR maximization problem is solved via the corresponding weighted sum mean-squared error (MSE) minimization problem. An iterative algorithm is proposed with the bi-directional signaling embedded into the TDD frame structure. A detailed signaling architecture is proposed to facilitate channel estimation, exchanging the user beamformers and the user MSE weight matrices among the coordinating nodes. The proposed coordinated system is compared with the uncoordinated system while taking into account the overhead originated from bi-directional signaling. Finally, a periodic beamformer re-initialization strategy is introduced, which enhances the beamformer convergence rate and the system performance in a time-correlated fading environment. The proposed coordinated system provides significant performance gain in an interference limited environment.

Full-Duplex Relaying Systems Subject to Co-Channel Interference and Noise in Nakagami-m Fading

This paper investigates the performance of dual-hop full-duplex relaying schemes subject to co-channel interference (CCI) and noise. In our analysis, two main scenarios are considered: in the first scenario, the link between the source and destination is seen as interference and, in the second one, it is seen as useful information. In both schemes, the effect of self-interference at the relay is taken into account. Accurate, closed- form expressions for the outage probability are derived for the general case, in which CCI and noise are assumed at both the relay and destination. The derived expressions allow for independent non-identically distributed Nakagami-m fading. Based on these expressions, special cases (but, yet new) assuming CCI only at the relay and assuming CCI only at the destination are examined. It is shown that CCI at the relay is more harmful for the system performance than CCI at the destination.

System level analysis of multi-operator small cell network at 10 GHz

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.

Network Slicing with Mobile Edge Computing for Micro-Operator Networks in Beyond 5G

We model the scenarios of network slicing allocation for the micro-operator (MO) network. The MO creates the slices "as a service" of wireless resource and then allocates these slices to multiple mobile network operators (MNOs). We propose the slice allocation problem of multiple MNOs with the goal of maximizing the social welfare of the network defined as sum rate of all MNOs. The many-to-one matching game framework is adopted to solve this problem. Then, the generic Markov Chain Monte Carlo (MCMC) method is introduced for the computation of game theoretical solution. After the MNOs obtain the slices, for each small cell base station (SBS), we investigate the role of power allocation using Q-learning and uniform power. We numerically show that the solution of the matching game leads to two-sided stable matching. Furthermore, for each MNO, we explore the problem of infrastructure cost minimization constrained on the latency at the user equipment (UE). The optimal solution is given by a greedy fractional knapsack algorithm. We illustrate that it is sufficient for the MNO to use a small fraction of the SBS to serve the UE while satisfying the latency constraint. For the problem of overall data rate maximization, we numerically show that the power allocation has significant effect on the social welfare of the system.

LiDAR Aided Human Blockage Prediction for 6G

Leveraging higher frequencies up to THz band paves the way towards a faster network in the next generation of wireless communications. However, such shorter wavelengths are susceptible to higher scattering and path loss forcing the link to depend predominantly on the line-of-sight (LOS) path. Dynamic movement of humans has been identified as a major source of blockages to such LOS links. In this work, we aim to overcome this challenge by predicting human blockages to the LOS link enabling the transmitter to anticipate the blockage and act intelligently. We propose an end-to-end system of infrastructure-mounted LiDAR sensors to capture the dynamics of the communication environment visually, process the data with deep learning and ray casting techniques to predict future blockages. Experiments indicate that the system achieves an accuracy of 87% predicting the upcoming blockages while maintaining a precision of 78% and a recall of 79% for a window of 300 ms.

Impact of Interference Between Neighbouring 5G Micro Operators

No abstract is provided for this article.

Multi-operator spectrum sharing using matching game in small cells network

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.

On the Performance of Alternative 5G Micro-Operator Deployments in 3.6 GHz and 26 GHz Bands

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.

Energy-Efficient Resource Allocation in Multi-layered Rate-Splitting Networks With Partial GSIC for eMBB and URLLC Coexistence

A Distributed Multi-channel Cognitive MAC Protocol for IEEE 802.11s Wireless Mesh Networks

In this paper, we propose a novel distributed frequency agile medium access control (MAC) extension to the IEEE 802.11s amendment for the next generation wireless mesh networks (WMNs). The proposed scheme has complete backward compatibility with the legacy IEEE 802.11 and the emerging 802.11s while, it is perfectly capable of multi-channel deployment of available frequency opportunities in order to coordinate concurrent multiple data transmissions. The root concept of the proposed enhancement is mainly based on the deployment of well-known ISM frequency bands, where the existing 802.11-based wireless equipments nowadays operate, as the common control channel in order to establish concurrent data transmissions. Here, we apply the aforementioned key concept to the IEEE 802.11s common channel framework (CCF) to attain two important goals: on one hand, the proposed scheme improves the channel utilization and capacity using the concept of cognitive radio and on the other hand, using the same concept it leads to lower access delay due to smarter decision making procedures exploited for link layer connection establishment. Through extensive simulations, which also take into account primary user (PU) appearance in non-ISM frequency opportunities, performance of the proposed medium access control (MAC) enhancement is evaluated showing its remarkable efficiency and better wireless medium access management.

Enabling macrocell-femtocell coexistence through interference draining

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.

Compensation of channel state estimation errors in adaptive MIMO-OFDM systems

The knowledge of channel state information (CSI) at the transmitter (TX) can dramatically increase the spectral efficiency of a multiple-input multiple-output (MIMO) system. In the case of time division duplex (TDD) systems, CSI can be easily obtained due to radio channel reciprocity. However, perfect CSI is too strong an assumption for a wireless system. The paper presents a robust link adaptation method for TDD systems employing MIMO-OFDM channel eigenmode based signalling. The scheme takes into account channel estimation errors at the TX side, and, by analyzing the noise enhancement at the equalizer output, we propose a simple method to maintain a constant frame error rate (FER) by controlling the instantaneous transmitted power in such a way that the average SNR at the equalizer output is kept constant. Simulation results show that the target FER can he maintained even in the presence of CSI errors; promising results were obtained by using a low complexity LS channel estimator at the TX side and a linear equalizer at the receiver. Thus, the proposed scheme constitutes a practical solution for high data rate wireless applications.

Environment-Aware Joint Active/Passive Beamforming for RIS-Aided Communications Leveraging Channel Knowledge Map

Reconfigurable intelligent surface (RIS)-aided communication is a promising technology for 6G systems to reconfigure the propagation environment proactively. However, it requires efficient real-time channel training, which suffers excessive overhead. To resolve this challenge, taking advantage of sensing with radio waves and localization, we propose a novel environment-aware joint active/passive beamforming for RIS-aided wireless communication based on the new concept of channel knowledge map (CKM). In the proposed scheme, the user equipments (UEs) location information is combined with the radio environment information provided by CKM to achieve efficient beamforming without real-time training. Simulation results show the proposed scheme's superior performance over training-based beamforming, which is also quite robust to errors related to UE's location in practice.

Welcome message - 5GU 2014

The 5th generation (5G) mobile communication systems are expected to revolutionize everything seen so far in wireless systems. The requirements for 5G vary by application but will include data rates ranging from very low sensor data to very high video content delivery, stringent low latency requirements, low energy consumption, and high reliability. All of these technological requirements are expected to be achieved while keeping the same or lower cost than today's technologies. The application scenarios range from usual broadband mobile to machine-to-machine communications, real-time highly reliable control with low latency and low data rate sensor networks with large number of nodes, to mention a few.

In-band interference of three kinds of UWB signals in GPS L1 band and GSM900 uplink band

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.

Learning-Based Caching in Cloud-Aided Wireless Networks

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.

On the performance of cognitive full-duplex relaying under spectrum sharing constraints

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.

Average Error Probability in Wireless Sensor Networks With Imperfect Sensing and Communication for Different Decision Rules

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.