With all the talk of 3G/4G/5G wireless networks and the speeds they provide, the question really comes down to how much speed is enough? With mobile network operators (MNOs) data caps limiting overall data utilized, faster speeds really just mean reaching the limit faster
The advent of low cost operators and MVNOs in the past few years has put enormous pressure on Mobile Network Operators (MNOs) to reassess their conventional capital expenditure strategies to provide enhanced network quality and retain constantly eroding margins for the best possible ROI. MNOs have reacted to this trend by adjusting their offer mix to maintain average revenue per user (ARPU) levels and their marketing efforts to optimize their cost basis. It has become all the more important now for MNOs to consider the cost optimization opportunities relating to daily “build and run” activities.
Engineering discussions tend to focus on how to improve the KPIs in the sense of network quality, reduce drops, improve accessibility, etc. Fixing coverage holes is always noted, but this tends to be fixing coverage the “right” way, which is typically the long term capital improvement. Long term network fixes come through large scale site build, low-band spectrum acquisition, improved hardware deployment, or alternative network types which is typically large capital expenditures
Once the physical considerations of a small cell design are accounted for, there is the soft side of it that also needs to be looked at to ensure if fits into the overall network and provides the desired impact. There are many soft considerations on specific parameters that are unique to each vendors in terms of load balancing, offsets and such, but we will consider some of the major soft considerations that impact the design.
To maximize the use of spectrum, a mix of footprint optimization or RF shaping is required in conjunction with parameter tuning and feature optimization. Cluster/Area optimization of a LTE and 3G network with macro and oDAS layers requires a complete cell footprint view in order to effectively optimize and maintain performance in a mature wireless network.
For a single event like Super Bowl, a stadium may have over a terabyte of data passing through the serving cells. Building and optimization of a DAS is a challenging process which requires equipment validation, DAS configuration adjustments, surrounding network interference mitigation, RAN network parameter optimization, and neighbour and Layer management optimization to support heavily loaded conditions.
The recent announcement by Uber to release anonymized data for over 2 billion trips to improve urban planning through the Uber Movement site could also improve what Uber relies on the most, wireless networks. The greatest challenge to mobile wireless network is simply mobility.
The visualization of social media data provides insights into a highly mobile segment of the population. Knowing where people typically post and that the typical geotagged posts with GPS are from mobile devices provides insight into where MNOs need to have the best network to serve them.
In any industry, there is continual progress made towards employee safety and overall process efficiency. Technological advances often help in achieving this goal. Unmanned small aircraft or drones could be considered one of these new tools for wireless network engineering and operations.
Software Defined Radios (SDRs) allow operators and service providers to provide alternative equipment testing methods to the traditional heavy scanners and tests currently conducted in the field. SDRs open an opportunity to low-cost options of not just scanning predefined networks, but quickly finding competitor networks, bandwidths, and use configurations with open source stacks and crowd funded hardware.
Mobile Network Operators (MNOs) are slated to begin massive 5G upgrades in parallel to existing LTE expansions. These capital expenditures tend to leave large impacts on the existing customer base through network outages, degraded performance, and physical mis-configuration from the work done at each node location. Many times the issue not found and addressed for 72+ hours.