Massive MIMO is a promising technique for increasing the spectral efficiency (SE) of cellular networks, by deploying antenna arrays with hundreds or thousands of active elements at the base stations and performing coherent transceiver processing. A common rule-of-thumb is that these systems should have an order of magnitude more antennas $M$ than scheduled users $K$ because the users’ channels are likely to be near-orthogonal when $M/K > 10$ . However, it has not been proved that this rule-of-thumb actually maximizes the SE. In this paper, we analyze how the optimal number of scheduled users $K^\star$ depends on $M$ and other system parameters. To this end, new SE expressions are derived to enable efficient system-level analysis with power control, arbitrary pilot reuse, and random user locations. The value of $K^\star$ in the large- $M$ regime is derived in closed form, while simulations are used to show what happens at finite $M$ , in different interference scenarios, with different pilot reuse factors, and for different processing schemes. Up to half the coherence block should be dedicated to pilots and the optimal $M/K$ is less than 10 in many cases of practical relevance. Interestingly, $K^\star$ depends strongly on the processing scheme and hence it is unfair to compare different schemes using the same $K$ .

Massive MIMO for Maximal Spectral Efficiency: How Many Users and Pilots Should Be Allocated?

Virtualized Radio Access Network (vRAN) architectures constitute a promising solution for the densification needs of 5G networks, as they decouple Base Stations (BUs) functions from Radio Units (RUs) allowing the processing power to be pooled at cost-efficient Central Units (CUs). vRAN facilitates the flexible function relocation (split selection), and therefore enables splits with less stringent network requirements compared to state-of-the-art fully Centralized (C-RAN) systems. In this paper, we study the important and challenging vRAN design problem. We propose a novel modeling approach and a rigorous analytical framework, FluidRAN, that minimizes RAN costs by jointly selecting the splits and the RUs-CUs routing paths. We also consider the increasingly relevant scenario where the RAN needs to support multi-access edge computing (MEC) services, that naturally favor distributed RAN (D-RAN) architectures. Our framework provides a joint vRAN/MEC solution that minimizes operational costs while satisfying the MEC needs. We follow a data-driven evaluation method, using topologies of 3 operational networks. Our results reveal that (i) pure C-RAN is rarely a feasible upgrade solution for existing infrastructure, (ii) FluidRAN achieves significant cost savings compared to D-RAN systems, and (iii) MEC can increase substantially the operator's cost as it pushes vRAN function placement back to RUs.

FluidRAN: Optimized vRAN/MEC Orchestration

Existing radio access networks (RANs) allow only for very limited sharing of the communication and computation resources among wireless operators and heterogeneous wireless technologies. We introduce the LayBack architecture to facilitate communication and computation resource sharing among different wireless operators and technologies. LayBack organizes the RAN communication and multi-access edge computing (MEC) resources into layers, including a devices layer, a radio node (enhanced Node B and access point) layer, and a gateway layer. LayBack positions the coordination point between the different operators and technologies just behind the gateways and thus consistently decouples the fronthaul from the backhaul. The coordination point is implemented through a software defined networking (SDN) switching layer that connects the gateways to the backhaul (core) network layer. A unifying SDN orchestrator implements an SDN-based management framework that centrally manages the fronthaul and backhaul communication and computation resources and coordinates the cooperation between different wireless operators and technologies. We illustrate the capabilities of the introduced LayBack architecture and SDN-based management framework through a case study on a novel fluid cloud RAN (CRAN) function split. The fluid CRAN function split partitions the RAN functions into function blocks that are flexibly assigned to MEC nodes, effectively implementing the RAN functions through network function virtualization. We find that for non-uniform call arrivals, the computation of the function blocks with resource sharing among operators increases a revenue rate measure by more than 25% compared to the conventional CRAN where each operator utilizes only its own resources.

LayBack: SDN Management of Multi-Access Edge Computing (MEC) for Network Access Services and Radio Resource Sharing

For many, this is no longer a valid question and the case is considered settled with software defined networking/network function virtualization (SDN/NFV) providing the inevitable innovation enablers toward the realization of a virtualized, programmable, and flexible 5G network. As SDN along with other technology enablers (including NFV) are still in the process of evolution, the first commercial deployment of 5G may take few years. However, some companies are claiming the availability of 5G solutions, but considering the monumental task of softwarization of mobile cellular networks there are genuine concerns that we may only see some point solutions involving SDN technology instead of a fully virtualized SDN-enabled 5G mobile network. In order to determine the technology readiness of SDN solutions in the context of 5G networks, this survey paper attempts to identify all important obstacles in the way, and looks at the state of the art of the relevant research. This survey is different from the previous surveys on SDN-based mobile networks as it focuses on the salient problems and discusses solutions proposed within and outside SDN literature. Our main focus is on fronthaul, backward compatibility, supposedly disruptive nature of SDN deployment, business cases and monetization of SDN related upgrades, latency of general purpose processors, and additional security vulnerabilities that softwarization brings along to the mobile network. We have also provided a summary of the architectural developments in SDN-based mobile network landscape, including deployment options for SDN within NFV framework, as not all work can be covered under the focused issues. This paper provides a comprehensive survey on the state of the art of SDN-based mobile networks and clearly points out the gaps in the technology.

/pdf/will-sdn-be-part-of-5g-49ztesluf9.pdf

Will SDN Be Part of 5G

A maximum likelihood (ML) estimator for digital sequences disturbed by Gaussian noise, intersymbol interference (ISI) and interchannel interference (ICI) is derived. It is shown that the sampled outputs of the multiple matched filter (MMF) form a set of sufficient statistics for estimating the input vector sequence. Two ML vector sequence estimation algorithms are presented. One makes use of the sampled output data of the multiple whitened matched filter and is called the vector Viterbi algorithm. The other one is a modification of the vector Viterbi algorithm and uses directly the sampled output of the MMF. It appears that, under a certain condition, the error performance is asymptotically as good as if both ISI and ICI were absent.

Maximum Likelihood Receiver for Multiple Channel Transmission Systems

The next generation, 5G wireless access, is going to support a wide range of new applications and use cases, with the capabilities including very high achievable data rate, very low latency, ultra-high reliability and the possibility to handle extreme device densities. To avoid the costly deployment, operation and maintenance of future mobile network, Radio Access Network (RAN) virtualization or Cloud RAN is the answer to the problem. The idea is to move the baseband processing to the data center and run the RAN L1, L2 and L3 protocol layers using the commodity hardware, such as high-performance general purpose processors. An open source software-based LTE implementation, such as OpenAirInterface (OAI), is definitely accelerating the RAN cloudization and also realizing the possibility of low cost LTE network deployment in the future. In this paper, we describe the OAI LTE implementation emphasizing on the user plane data flow. We have successfully emulated over-the-air transmission for 1 UE and 1 eNB LTE network supporting both FDD Band 5 and TDD Band 38. We have also performed a thorough profiling of OAI, in terms of execution time, on the user plane data flow. Our results could be served as the reference for future optimization by open source community.

/pdf/performance-study-of-lte-experimental-testbed-using-k4gcct04sv.pdf

Performance study of LTE experimental testbed using OpenAirInterface

Most of the mobile devices today support multimedia services as well as legacy mobile services such as voice, short message service (SMS) and multimedia messaging service (MMS). Real-time services like video streaming and multimedia gaming are given higher priority to be scheduled as compared to the non-real-time services like web browsing and sending email. However, both real-time and non-real-time services have equal demand and the urgency for the services to be scheduled cannot be neglected. This paper investigates the effect of tuning the parameter beta (β) in the Exponential Blind Equal Throughput (EXP-BET) algorithm. Different values of beta used were between 0.1 and 0.9. The simulations have been done using the LTE-Sim simulator and the performance of the proposed algorithm was observed in terms of fairness and throughput for video and best effort flows. From here, it can be concluded that when β = 0.1, the algorithm delivers best performance of fairness for both real-time and non-real-time services.

Performance Analysis of EXP-BET Algorithm

Future network should be able to handle many types of traffic such as video conferencing, audio and data transfer. Each of this traffic has specified Quality of Service (QoS). It is crucial for the switch design to guaranteed QoS for all applications. In this paper, a new ways of handling multi-class traffic is presented. The analysis is done on N × N switch with two traffic classes, high priority for delay sensitive cells and low priority for loss sensitive cells. To avoid starvation problem in low priority class, a novel approach have been introduced in the scheduling technique. By applying dual threshold setting, a better control on priority setting for both classes can be achieved. The threshold value is set based on the waiting time and threshold serving probability value. The proposed method has been simulated to show that the starvation problem can be overcome.

Multi-class scheduling technique using dual threshold

This paper proposes a channel tracking technique for MIMO-OFDM system, in order to solve timing sensitivity issue with the pilot-based Channel Estimator (CE) reported in [1]. The proposed technique exploits the pilot symbols embedded within the OFDM symbol to estimate the MIMO channel coefficients. Then the estimated channel values of the pilot subcarriers, as well as the initial LTF-based channel estimated values are processed to provide a weighting factor, which is used to track the channel coefficients for the entire transmission packet. The performance of the proposed CE is evaluated with the aid of actual captured data. Our performance study considers a Non-LOS indoor environment, where the transmitter and receiver are approximately 15 meters apart, and located in different rooms. Our study shows that the proposed CE successfully mitigates the susceptibility of the original pilot-based CE to the timing synchronization error.

Frequency-domain channel tracking for MIMO-OFDM systems, validated with an experimental data in 5.2 GHz wireless channel

Active service discovery in 802.11 requires the WLAN clients to broadcast the probe request frame based on its' list of preferred networks while searching for Access Point (AP) prior to authentication and association. The Medium Access Control (MAC) address, a unique identifier allocated to a network interface, is embedded in the probe request frame. Coupling with his or her personally identifiable information (PII), such as user profile on the social network, the MAC address can be used for user location tracking. Sniffing the probe request frame requires Wi-Fi chipsets to be configured as promiscuous mode or monitor mode. Unfortunately, not all Wi-Fi chipset supports the promiscuous mode. At the same time, it is rather difficult to find an embedded system with multiple antennas attached to multiple Radio Frequency (RF) interfaces that could allow us to sniff the probe request frame coming from different direction. By utilizing the Wireless Open Access Research Platform (WARP) platform with Mango 802.11 reference design and its Python experiments framework, we have successfully implemented a 802.11 probe request scanner. This platform provides us up to 4 radios with 4 antennas for our sniffing purpose using the two onboard radios and also two additional FPGA Mezzanine Card (FMC) radios.

Abdul Aziz Abdul Rahman

Papers

Performance study of LTE experimental testbed using OpenAirInterface

Performance Analysis of EXP-BET Algorithm

Multi-class scheduling technique using dual threshold

Frequency-domain channel tracking for MIMO-OFDM systems, validated with an experimental data in 5.2 GHz wireless channel

Implementing 802.11 probe request scanner using WARP platform