I've been working on networks for decades and for as long as I can remember, network proxies have existed. I first came across the idea when I worked for IBM as an SNA programmer back in the late 90s but it's in more recent years that network proxies have taken on more importance. 

As an ex military satellite communications engineer I certainly remember working with spread spectrum modulation and also frequency hopping technology in the 1980's. Wireless Local Area Networking technology today exploits a technology which was thitherto mostly hidden inside this shadowy domain of military communications and radar. This technology comprises a collection of ideas which are termed Spread Spectrum Techniques (SST). Spread Spectrum techniques have some powerful properties which make them an excellent candidate for networking applications. To better understand why, we will take a closer look at this fascinating area, and its implications for networking.

Ask your average person what a Yagi antenna is and they will probably look at you with a puzzled expression. The fact is however that everybody in the UK has probably seen a Yagi antenna and in all likelihood used one at some point.

Example of a Yagi TV aerial.


The Yagi antenna was invented by two Japanese researchers in 1926, namely Hidetsugu Yagi and Shintaro Uda.
It is more correctly called the Yagi-Uda antenna however Mr Uda seems to have slipped off the credits somewhat.
It is an example of a subtype of antenna known as the "beam antenna" but having established that its on almost every roof in the UK, why does it interest us at Rustyice Solutions?

In recent years, telecommunications has gone through a revolution with mobile communications becoming the greatest driving force behind this. Whether you like it or not all mobile communication and by definition all radio communication requires an antenna for reception and transmission of the signal. This antenna has largely become hidden from the view of the consumer with form and function of equipments dictating that an antenna can not be visible from the outside in most equipments but they are still there and play a fundamental part in everything that we do in the mobile communications world. So what does the ugly old rooftop TV antenna have to do with todays sleek 21st century devices you may ask?

'Quite a lot' is the answer. First, lets look at the technicals of the Yagi antenna itself.




The Yagi antenna is usually made up of a single driven (dipole) elelment and a reflector along with a number of parasitic elements whose size and spacing is determined by the frequencies which one wishes to receive or transmit. The size of the dipole is usually half of the wavelength (?) of the centre frequency or, if a folded dipole is used, the total length of the conductor is equal to almost 1 x ?. It is directional along the axis perpendicular to the dipole in the plane of the elements, from the reflector toward the driven (dipole) element and the parasitic elements which are also known as directors. Typical spacings between elements vary from about 1/10 to 1/4 of a wavelength, depending on the specific design and performance requirements. The lengths of the directors are smaller than that of the driven element, which is smaller than that of the reflector(s) according to an elaborate design procedure. These elements are usually parallel in one plane, supported on a single crossbar known as a boom.




Many of the higher end wireless networking manufacturers use emulated yagi antennas in their products today however we are sure you will agree that the coolest gizmo to get yourself a wifi signal where everybody else just simply can and will not be able to connect is this example of antenna technology at its finest over there on the left. In all, we believe, a perfect example of how technology might surge ahead at great speed every day but there really is no escape from good old fashioned antenna theory when you want to get yourself connected on the move.

At Rustyice Solutions, we have many shared years of experience in the field of HF, VHF, UHF and even SHF radio communications. If you or your business needs help getting connected on the fringes of reasonable reception via off the shelf products why not give us a call. We are sure we will be able to bring our considerable experience to bear in getting you connected. Of course you could always go for the item below which, it is said can connect to a wifi network at a range of 10 miles but youre as likely to get the jail as get connected so maybe you should just leave it to us.

 

Having recently completed my latest M.Eng block on the subject of “Natural and Artificial Intelligence“, I became aware of advances made in the recent decade towards a new paradigm of network traffic engineering that was being researched. This new model turns its back on traditional destination based solutions, (OSPF, EIGRP, MPLS) to the combinatorial problem of decision making in network routing  favouring instead a constructive greedy heuristic which uses stochastic combinatorial optimisation. Put in more accessible terms, it leverages the emergent ability of sytems comprised of quite basic autonomous elements working together, to perform a variety of complicated tasks with great reliability and consistency.

In 1986, the computer scientist Craig Reynolds set out to investigate this phenomenon through computer simulation. The mystery and beauty of a flock or swarm is perhaps best described in the opening words of his classic 1986 paper on the subject:

The motion of a flock of birds is one of nature’s delights. Flocks and related synchronized group behaviors such as schools of fish or herds of land animals are both beautiful to watch and intriguing to contemplate. A flock … is made up of discrete birds yet overall motion seems fluid; it is simple in concept yet is so visually complex, it seems randomly arrayed and yet is magnificently synchronized. Perhaps most puzzling is the strong impression of intentional, centralized control. Yet all evidence dicates that flock motion must be merely the aggregate result of the actions of individual animals, each acting solely on the basis of its own local perception of the world.

An analogy with the way ant colonies function has suggested that the emergent behaviour of ant colonies to reliably and consistently optimise paths could be leveraged to enhance the way that the combinatorial optimisation problem of complex network path selection is solved.

The fundamental difference between the modelling of a complex telecommunications network and more commonplace problems of combinatorial optimisation such as the travelling salesman problem is that of the dynamic nature of the state at any given moment of a network such as the internet. For example, in the TSP the towns, the routes between them and the associated distances don’t change. However, network routing is a dynamic problem. It is dynamic in space, because the shape of the network – its topology – may change: switches and nodes may break down and new ones may come on line. But the problem is also dynamic in time, and quite unpredictably so. The amount of network traffic will vary constantly: some switches may become overloaded, there may be local bursts of activity that make parts of the network very slow, and so on. So network routing is a very difficult problem of dynamic optimisation. Finding fast, efficent and intelligent routing algorithms is a major headache for telcommunications engineers.

So how you may ask, could ants help here? Individual ants are behaviourally very unsophisticated insects. They have a very limited memory and exhibit individual behaviour that appears to have a large random component. Acting as a collective however, ants manage to perform a variety of complicated tasks with great reliability and consistency, for example, finding the shortest routes from their nest to a food source. 



These behaviours emerge from the interactions between large numbers of individual ants and their environment. In many cases, the principle of stigmergy is used. Stigmergy is a form of indirect communication through the environment. Like other insects, ants typically produce specific actions in response to specific local environmental stimuli, rather than as part of the execution of some central plan. If an ant’s action changes the local environment in a way that affects one of these specific stimuli, this will influence the subsequent actions of ants at that location. The environmental change may take either of two distinct forms. In the first, the physical characteristics may be changed as a result of carrying out some task-related action, such as digging a hole, or adding a ball of mud to a growing structure. The subsequent perception of the changed environment may cause the next ant to enlarge the hole, or deposit its ball of mud on top of the previous ball. In this type of stigmergy, the cumulative effects of these local task-related changes can guide the growth of a complex structure. This type of influence has been called sematectonic. In the second form, the environment is changed by depositing something which makes no direct contribution to the task, but is used solely to influence subsequent behaviour which is task related. This sign-based stigmergy has been highly developed by ants and other exclusively social insects, which use a variety of highly specific volatile hormones, or pheromones, to provide a sophisticated signalling system. It is primarily this second mechanism of sign based sigmergy that has been successfully simulated with computer models and applied as a model to a system of network traffic engineering.

In the traditional network model, packets move around the network completely deterministically. A packet arriving at a given node is routed by the device which simply consults the routing table and takes the optimum path based on its destination. There is no element of probability as the values in the routing table represent not probabilities, but the relative desirability of moving to other nodes.

In the ant colony optimisation model, virtual ants also move around the network, their task being to constantly adjust the routing tables according to the latest information about network conditions. For an ant, the values in the table are probabilities that their next move will be to a certain node.The progress of an ant around the network is governed by the following informal rules:

• Ants start at random nodes.

 

• They move around the network from node to node, using the routing table at each node as a guide to which link to cross next.

 

• As it explores, an ant ages, the age of each individual being related to the length of time elapsed since it set out from its source. However, an ant that finds itself at a congested node is delayed, and thus made to age faster than ants moving through less choked areas.

 

• As an ant crosses a link between two nodes, it deposits pheromone however, it leaves it not on the link itself, but on the entry for that link in the routing table of the node it left. Other ‘pheromone’ values in that column of the nodes routing table are decreased, in a process analogous to pheromone decay.

 

• When an ant reaches its final destination it is presumed to have died and is deleted from the system.R.I.P.

Testing the ant colony optimisation system, and measuring its performance against that of a number of other well-known routing techniques produced good results and the system outperformed all of the established mechanisms however there are potential problems of the kind that constantly plague all dynamic optimisation algorithms. The most significant problem is that, after a long period of stability and equilibrium, the ants will have become locked into their accustomed routes. They become unable to break out of these patterns to explore new routes capable of meeting new conditions which could exist if a sudden change to the networks conditions were to take place. This can be mitigated however in the same way that evolutionary computation introduces mutation to fully explore new possibilities by means of the introduction of an element of purely random behaviour to the ant.

‘Ant net’ routing has been tested on models of US and Japanese communications networks, using a variety of different possible traffic patterns. The algorithm worked at least as well as, and in some cases much better than, four of the best-performing conventional routing algorithms. Its results were even comparable to those of an idealised ‘daemon’ algorithm, with instantaneous and complete knowledge of the current state of the network.

It would seem we have not heard the last of these routing antics…. (sorry, couldnt resist).

With the huge growth of mobile phones complementing a revolution wireless network technologies there has been a huge change in the consumer’s lifestyle and dependence on mobile phones. With the emergence of smart phones (mobile web) consumers are replacing not only their fixed lines but have started downsizing the number of PC's in the home. Fundamentally, consumers want great voice quality, reliable service, and low prices. But today’s mobile phone networks often provide poor indoor coverage and expensive per-minute pricing. In fact, with the continued progress in broadband VoIP offerings such as Vonage and Skype, wireless operators are at a serious disadvantage in the home.

Hence the wireless operators are looking to enhance their macro-cell coverage with the help of micro-cell coverages(indoor) by deploying small base stations such as Femtocells or with the help of Relay technology. These miniature base stations are the size of a DSL router or cable modem and provide indoor wireless coverage to mobile phones using existing broadband Internet connections.

Pointing out some key advantages of Femtocells and Relays we will then focus on their adoption in advanced wireless networks(WiMAX and LTE)



 

 

 

 

 

FEMTOCELLS

Technical Advantages:

Low Cost: The Business Model would be initially by offering Femtos as a consumer purchase through mobile operators

Low Power: around 8mW- 120 mW lower than Wi-Fi APs.

Easy to Use: Plug-and-Play easily installed by consumers themselves

Compatibility & Interoperability: Compatibility with UMTS,EVDO standards and WiMAX,UMB & LTE standards

Deployment: In Wireless Operator owned licensed spectrum unlike WiFi

Broadband connected: Femtocells utilize Internet protocol (IP) and flat base station architectures, and will connect to mobile operator networks via a wired broadband Internet service such as DSL, cable, or fibre optics.

Customer’s point of view:

Increased Indoor Coverage: Coverage radius is 40m – 600m in most homes providing full signal throughout the household

Load sharing: Unlike in macro cells which supports hundreds of users, Femtos will support 5-7 users simultaneously  enabling lesser contention in accessing medium delivering higher data rates/user.

Better Voice Quality: As the users will be in the coverage envelope and closer to Femtos, they will definitely be supported with a better voice and sound quality with fewer dropped calls

Better Data/Multimedia Experience: It will deliver better and higher data performance with streaming musics, downloads and web browsing with lesser interruptions and loss of connections compared to a macro-cell  environment.

Wireless Operator’s point of view:

Lower CAPEX: Increased usage of femtocells will cut down huge capital costs on macro cell equipments & deployments. This includes costs savings in site acquisitions, site equipments, site connections with the switching centers.

Increased network capacity: Increased usage of femtocells will reduce stress on macro cells increasing overall capacity of mobile operators

Lower OPEX: With lesser macro cell sites it reduces the overall site maintenance, equipment maintenance and backhaul costs.

Newer Revenue Opportunities: With provision of excellent indoor coverage and superior user experience with voice and multimedia data services operators has an opportunity of raising its ARPU with more additions to family plans

Reduced Churn: Due to improved coverage, user multimedia experience and fewer dropped calls, will lead to a significant reduction in customer churn

Technical hurdles:

Spectrum: Femtocells works on licensed spectrum and as the spectrum is the most expensive resource it will be a major technical hurdle for the wireless operator for frequency planning.

RF Coverage Optimization: Radio tuning and optimization for RF coverage in macro cells is manually done by technicians which is now not possible at each femtocell level, henceforth self optimization and tuning over time according to the indoor coverage map has to be done either automatically or remotely which is a technical challenge.

RF Interference: Femtocells might be prone to femto-macro interference and also femto-femto interference in highly dense macro or micro environments which might affect the user experience.

Automatic System Selection: When an authorized user of a femto cell moves in or out of the coverage of the femto cell – and is not on an active call – the handset must correctly select the system to operate on. In particular, when a user moves from the macro cell into femto cell coverage, the handset must automatically select the femto cell, and visa versa.

Handoffs: When an authorized user of a femto cell moves in or out of coverage of the femto cell – and is on an active call – the handset must correctly hand off between the macro cell and femto cell networks. Such handoffs are especially critical when a user loses the coverage of a network that is currently serving it, as in the case of a user leaving the house where a femto cell is located.

Security & Scalability: A femto cell must identify and authenticate itself to the operator’s network as being valid. With millions of femto cells deployed in a network, operators will require large scale security gateways at the edge of their core networks to handle millions of femto cell-originated IPsec tunnels.

Femto Management: Activation on purchase and plug and play by end user is an important step and with a proper access control management allowing end-user to add/delete active device connections in the household. In addition, operators must have management systems that give first-level support technicians full visibility into the operation of the femto cell and its surrounding RF environment.

RELAYS:

Relay transmission can be seen as a kind of collaborative communications, in which a relay station (RS) helps to forward user information from neighboring user equipment (UE)/mobile station (MS) to a local eNode-B (eNB)/base station (BS). In doing this, an RS can effectively extend the signal and service coverage of an eNB and enhance the overall throughput performance of a wireless communication system. The performance of relay transmissions is greatly affected by the collaborative strategy, which includes the selection of relay types and relay partners (i.e., to decide when, how, and with whom to collaborate).

Relays that receive and retransmit the signals between base stations and mobiles can be used to effectively  increase throughput extend coverage of cellular networks. Infrastucture relays do not need wired connection to network thereby offering savings in operators’ backhaul costs. Mobile relays can be used to build local area networks between mobile users under the umbrella of the wide area cellular networks



Advantages:

Increased Coverage: With multi-hop relays the macro cell coverage can be expanded to the places where the base station cannot reach.

Increased Capacity: It creates hotspot solutions with reduced interference to increase the overall capacity of the system.

Lower CAPEX & OPEX: Relays extending the coverage eliminates the need of additional base stations and corresponding backhaul lines saving wireless operators deployment costs and corresponding maintenance costs. The relays can be user owned relays provided by operators and can be mounted on roof tops or indoors.

Better Broadband Experience: Higher data rates are therefore now available as users are close to the mini RF access point.

Reduced Transmission power: With Relays deployed there is a considerable reduction in transmission power reducing co-channel interference and increased capacity.

Faster Network rollout: The deployment of relays is simple and quickens the network rollout process with a higher level of outdoor to indoor service and leading to use of macrodiversity increasing coverage quality with lesser fading and stronger signal levels.

As a hot research topic with great application potential, relay technologies have been actively studied and considered in the standardization process of next-generation mobile communication systems, such as 3GPP LTE-Advancedand IEEE 802.16j (multihop relays for WiMAX standards).






Pairing Schemes for Relay Selection

One of the key challenges is to select and pair nearby RSs and UE units to achieve the relay/cooperative gain. The selection of relay partners (i.e., with whom to collaborate) is a key element for the success of the overall collaborative strategy. Practically, it is very important to develop effective pairing schemes to select appropriate RSs and UE units to collaborate in relay transmissions, thus improving throughput and coverage performance for future relay-enabled mobile communication networks.

This pairing procedure can be executed in either a centralized or distributed manner. In a centralized pairing scheme, an eNB will serve as a control node to collect the required channel and location information from all the RSs and UE units in its vicinity, and then make pairing decisions for all of them. On the contrary, in a distributed pairing scheme, each RS selects an appropriate UE unit in its neighborhood by using local channel information and a contention-based medium access control (MAC) mechanism. Generally speaking, centralized schemes require more signaling overhead, but can achieve better performance

Relay Transmission Schemes

 

Many relay transmission schemes have been proposed to establish two-hop communication between an eNB and a UE unit through an RS

Amplify and Forward — An RS receives the signal from the eNB (or UE) at the first phase. It amplifies this received signal and forwards it to the UE (or eNB) at the second phase. This Amplify and Forward (AF) scheme is very simple and has very short delay, but it also amplifies noise.

Selective Decode and Forward — An RS decodes (channel decoding) the received signal from the eNB (UE) at the first phase. If the decoded data is correct using cyclic redundancy check (CRC), the RS will perform channel coding and forward the new signal to the UE (eNB) at the second phase. This DCF scheme can effectively avoid error propagation through the RS, but the processing delay is quite long.

Demodulation and Forward — An RS demodulates the received signal from the eNB (UE) and makes a hard decision at the first phase (without decoding the received signal). It modulates and forwards the new signal to the UE (eNB) at the second phase. This Demodulation and Forward (DMF) scheme has the advantages of simple operation and low processing delay, but it cannot avoid error propagation due to the hard decisions made at the symbol level in phase one.

Comparison between 3GPP LTE Advanced and IEEE 802.16j RSs

Below shows comparison between Type I(3GPP- LTE Advanced) and Non-Transparency(IEEE -802.16j) RSs



Technical Issues

Practical issues of cooperative schemes like signaling between relays and different propagation delays due to different locations of relays are  often overlooked.  If  the difference in time of arrival between the direct path from source to destination and the paths source-relay-destination is constrained then relays must locate inside the ellipsoid as depicted below. Thus,  in practice, such a cooperative system shoiuld be a narrow band one, or guard interval between transmitted symbols should be used to avoid intersymbol interference due to relays.

In band relays consume radio resources and Out of band relays need multiple transceivers.