Nadisanka Rupasinghe

Licensed-assisted access for WiFi-LTE coexistence in the unlicensed spectrum

One of the effective ways to address the exponentially increasing traffic demand in mobile communication systems is to use more spectrum. Although licensed spectrum is always preferable for providing better user experience, unlicensed spectrum can be considered as an effective complement. Before moving into unlicensed spectrum, it is essential to carry out proper coexistence performance evaluations. In this paper, we analyze WiFi 802.11n and Long Term Evolution (LTE) coexistence performance considering multi-layer cell layouts through system level simulations. We consider a time division duplexing (TDD)-LTE system with an FTP traffic model for performance evaluation. Simulation results show that WiFi performance is more vulnerable to LTE interference, while LTE performance is degraded only slightly. However, WiFi throughput degradation is lower for TDD configurations with larger number of LTE uplink sub-frames and smaller path loss compensation factors.

Reinforcement learning for licensed-assisted access of LTE in the unlicensed spectrum

In order to coexist with the WiFi systems in the unlicensed spectrum, Long Term Evolution (LTE) networks can utilize periodically configured transmission gaps. In this paper, considering a time division duplex (TDD)-LTE system, we propose a Q-Learning based dynamic duty cycle selection technique for configuring LTE transmission gaps, so that a satisfactory throughput is maintained both for LTE and WiFi systems. By explicitly taking the impact of IEEE 802.11n beacon transmission mechanism into account, we evaluate the coexistence performance of WiFi and LTE using the proposed technique. Simulation results show that the proposed approach can enhance the overall capacity performance by 19% and WiFi capacity performance by 77%, hence enabling effective coexistence of LTE and WiFi systems in the unlicensed band.

LAA-based LTE and ZigBee coexistence for unlicensed-band smart grid communications

The advent of smart grid introduces abundant number of smart meters which require bidirectional reliable communication. The deployment of advanced metering infrastructure (AMI) in smart grid networks will be auspicious if the existing infrastructure of LTE networks can be utilized. On the other hand, use of LTE infrastructure and spectrum by AMIs will further load the already congested broadband wireless networks. Recently, use of the unlicensed spectrum by the LTE technology is seen as a promising approach to offload the existing traffic from the licensed spectrum. In our study, we investigate the coexistence of LTE and ZigBee networks at the unlicensed frequency band of 2.4 GHz. We consider a time division duplexing (TDD)-LTE system accompanied by ZigBee network with FTP traffic model for system level simulations. The simulation results demonstrate that the simultaneous operation of LTE and ZigBee on the 2.4 GHz band reduces ZigBees performance, but still meets the data communication requirements for AMI as prescribed by Department of Energy (DoE).

Directional training and fast sector-based processing schemes for mmWave channels

We consider a single-cell scenario involving a single base station (BS) with a massive array serving multi-antenna terminals in the downlink of a mmWave channel. We present a class of multiuser MIMO schemes, which rely on uplink training from the user terminals, and on uplink/downlink channel reciprocity. The BS employs virtual sector-based processing according to which, user-channel estimation and data transmission are performed in parallel over non-overlapping angular sectors. The uplink training schemes we consider are non-orthogonal, that is, we allow multiple users to transmit pilots on the same pilot dimension (thereby potentially interfering with one another). Elementary processing allows each sector to determine the subset of user channels that can be resolved on the sector (effectively pilot contamination free) and, thus, the subset of users that can be served by the sector. This allows resolving multiple users on the same pilot dimension at different sectors, thereby increasing the overall multiplexing gains of the system. Our analysis and simulations reveal that, by using appropriately designed directional training beams at the user terminals, the sector-based transmission schemes we present can yield substantial spatial multiplexing and ergodic user-rates improvements with respect to their orthogonal-training counterparts.

Optimum Hovering Locations with Angular Domain User Separation for Cooperative UAV Networks

Unmanned aerial vehicles (UAVs) can be used as aerial base stations (BSs) to deliver broadband wireless connectivity during temporary events, at hotspot areas, or after disasters that may destroy existing communication infrastructure. Since UAV- BSs are low power nodes, their efficient placement is important to reap the maximum capacity and coverage benefits from their deployments. By making use of \emph{UAV mobility} and multi- antenna arrays, it is possible to achieve angular domain user separation with lower feedback requirement. In this paper, we propose an approach to identify optimum hovering locations for UAV-BSs equipped with multi-antenna arrays. We formulate the problem as a signal-to-noise ratio (SNR) maximization at ground nodes subject to a constraint that interference leakage is lower than a threshold. We consider a scenario with two UAVs each with a single user attached and develop our proposed scheme to achieve: 1) angular domain user separation, and 2) SNR maximization at each user. The performance evaluation of the proposed scheme is carried out through simulations, which show that under interference limited conditions the proposed scheme provides capacity performance better than linear zero-force-beamforming (LZFBF), without the requirement of full channel state information of all the users.

Efficient Noise Variance Estimation Under Pilot Contamination for Massive MIMO Systems

Massive multiple input multiple output (MIMO) is expected to be one of the enabling technologies for fifth-generation cellular networks. One of the major challenges in massive MIMO systems is the accurate joint estimation of the channel and noise variance, which significantly affects the performance of wireless communications in practical scenarios. In this paper, we first derive a novel maximum likelihood estimator for the noise variance at the receiver of massive MIMO systems considering practical impairments such as pilot contamination. Then, this estimate is used to compute the minimum mean square error estimate of the channel. In order to measure the performance of the proposed noise variance estimator, we derive the corresponding Cramér–Rao lower bound (CRLB). Simulation results show that the estimator is efficient in certain scenarios, outperforming existing approaches in the literature. Furthermore, we develop the estimator and the CRLB for equal and different noise variance at the receive antennas. Although the proposed estimator is valid for all antenna array sizes, its use is particularly effective for massive MIMO systems.

System-level performance of mmWave cellular networks for urban micro environments

In this paper, we investigate the propagation coupling loss (captures all sources of attenuation between serving cell and mobile station (MS)) and geometry metric (GM) (downlink average signal-to-interference plus noise ratio) performance of mmWave cellular networks for outdoor and indoor MSs, considering urban micro (UMi) environments. Based on these studies, we identify effective mmWave frequency bands for cellular communication. We consider 3GPP compliant system-level simulations with two power allocation schemes: 1) transmit power scaled with communication bandwidth, and 2) constant total transmit power. Simulation results show that with scaled transmit power allocation, GM performance degradation is small: 20% of MSs experience GM less than 0 dB at all mmWave frequencies considered, for outdoor MSs. With constant Tx power allocation, 20% of MSs experience GM less than 0 dB for frequencies up to 30 GHz. Furthermore, 35% (48%) of outdoor MSs experience GM performance less than 0 dB at 60 GHz (100 GHz). On the other hand, for indoor MSs, even with scaled Tx power allocation, favorable GM performance is observed only at low frequencies, i.e., 2 GHz.

Noise variance estimation for 5G wireless networks under pilot contamination

Large-scale multi-cell multi-user multiple-input multiple-output (MU-MIMO) is expected to be one of the enabling technologies for fifth generation (5G) time division duplexing (TDD) systems. One of the major challenges faced by this technology is the pilot contamination issue where interference from users in the neighboring cells may significantly impact the performance of the channel estimation process. There are different strategies proposed in the literature to overcome the pilot contamination issue. However, most of these works assume perfect knowledge of the noise variance in the channel estimation process which is not the case in realistic systems. In this paper, we study the pilot contamination issue in multi-cell MU-MIMO cellular networks and propose two noise variance estimators; 1) maximum likelihood, and 2) method of moments estimators which can be used for the channel estimation under the pilot contamination. We evaluate the performance of these noise estimators, and the impact of the noise estimation on the channel estimation performance. Further, the existing literature shows that the effects of the pilot contamination on the channel estimation is vanished when the angle-of-arrival of the desired and interfering users do not overlap. Using a pilot assignment strategy from the existing literature, we evaluate channel estimation performance at mmWave frequencies for large antenna array regime considering recently developed 5G channel models. Our simulation results show that under pilot contamination, better channel estimation can be achieved at mmWave bands compared to low frequency scenarios.

Capturing, recording, and analyzing LTE signals using USRPs and LabVIEW

Long Term Evolution (LTE) systems are currently playing a major role in catering the ever increasing traffic demand. Better performance and better throughput compared to other mobile communication technologies are main objectives of LTE deployments. To provide a better quality of experience, LTE signals from real base stations should be captured and analyzed, for possibly making adjustments to network operation parameters, or for deploying new base stations. In this paper, real LTE signals from the base stations are recorded using universal software radio peripheral (USRP) devices and NI LabVIEW software. Then, these recorded LTE signals are processed and analyzed in MATLAB to identify information such as primary cell identifier (PCI) and master information block (MIB). LabVIEW and MATLAB graphical user interfaces (GUIs) are provided to make the system more user friendly.