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Coleman Bazelon

In typical single receiver peer-to-peer P2P or ad-hoc configurations, all members of the network must transmit on the same frequency, so some method of contention control is required in order to prevent these transmissions from jamming each other at the intended receiver. The following multiple- access schemes are commonly utilized.

In time-division multiple access TDMA , separation of signals is provided by ensuring that at most one user is active on the channel at any one time. This can be implemented through a predetermined typically slotted fixed- time-slot mechanism, or through a contention-based protocol.

Slots are a fixed upper bound on capacity, and managing assignments to these slots in conditions of uneven demand Internet access, voice-call initiation,. Collision-sensing multiple-access CSMA nodes sense channel occupancy and transmit only when the channel is clear. Such an architecture requires that the devices be partitioned, so com- munication is possible only along some parings of the network node as will be discussed a few paragraphs further on. The information is recovered by the reverse process.

Additionally, the front-end may be overloaded, since it must provide analog amplification of the entire energy of the near signal, without the benefit of any coding gain or processing reduction. Some services, such as voice telephony, require duplex operation, which provides simultane- ous apparent transmission and reception for an individual device, and among a number of users of the same channel. Cellular architectures have evolved largely along the first of these two lines which requires paired up- and down-link spectrum , but the second has attracted attention where paired spectrum is not available, as is being considered for Chinese deployments.

Frequency-division duplex. Frequency-division duplex FDD separates signals in frequency. This is less general than the time-division duplex TDD meth- ods, insofar as it requires predetermined and typically static bandwidth al- location and topology. Handsets transmit on one frequency up-link , and the BTS transmits to all handsets on a single frequency. This has the ad- vantage that no handset has to be able to receive transmissions from other handsets, which might overload its receiver. This framework is used in most of the implementations of cellular systems the exception being WiMax and the proposed LTE TDD mode, which is for use with unpaired spec- trum.

Time-division duplex. TDD separates signals to and from a single node by sep- arating them in time.


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This is appropriate when the same signaling channel is used for both sides of the conversation. The control over this channel can be through a handshaking process, or through fixed time allocations. As mentioned, cellular practice has been primarily FDD. This mode has two serious issues for future deployments. Spectrum that is suitable for paired use may become less available as it becomes necessary to share spectrum or use blocks that are not optimal in spectral extent or location.

The paired-spectrum approach requires a fixed relationship of up-link and down- link spectrum. Web browsing and video are highly asymmetric, requiring much more down-link than up-link, Voice-over-Internet protocol VOIP is symmetric, and uploading of media may be asymmetric, requiring extensive up-link bandwidth. Any fixed allocation be- tween up- and down-link is likely to be wasteful of spectrum resources. Nevertheless, there are several models of wireless system organization that recur in a large number of systems, and can form classes from which most specific architec- tures can be derived.

Point-to-point 1 to 1. These architectures provide a direct path between the ini- tiating sending node and the receiving node s. Examples include analog frequency-modulation FM walkie talkies and radio networks. These sys- tems are characterized by one-to-one communications 1 to 1 topologies. Hub and spoke n to 1. Hub and spoke is a common architecture where a col- lection of nodes communicates solely with a common node, which provides 3 This discussion ignores one-directional modes, such as broadcast radio and television.

Peer-to-peer n to n.

P2P networks are often referred to as a mesh when in fixed locations or as a mobile ad-hoc network MANET when they are mobile or have intermittent connectivity. Simplistic depictions of each of these architectures are shown in Figure 4. These network organizations are loosely composable. For example, a cellular system with backhaul can be considered to be a set of hub-and-spoke networks the cellular towers connected by a point-to-point network the wireless backhaul , or, if intercon- nected, a P2P network.

Table 4. A gen- eral network construct is the wireless metropolitan area network MAN that typically links between multiple wireless local area networks WLANs. Figure 4. The convergence of wireless technologies has been accelerating over the last decade.

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Scalability, Density, and Decision Making in Cognitive Wireless Networks

In these legacy architectures, the wireless and Internet routing paths are standalone, and isolated, until deep into the Internet and TELCO infrastructure. It is very reason- able to treat these systems as independent, since their interaction is in the core of the communications infrastructure, where we can reasonably assume that the law of large numbers isolates these systems. In contrast, the architectures that are emerging are highly coupled, as shown in Figure 4. Whereas the only interaction between the cellular and premises Internet was in the net- work core in the POTS TELCO stovepipe architecture, in the emerging architecture the premises Internet supports mobile devices in parallel with the cellular services.

This architecture becomes even more integrated with the introduction of femtocells, which make the existence and use of this access path invisible to network users. With femto- cells, the wireless service is supported through both its own access to the Internet and that provided by the distributed infrastructure supporting the femtocell devices. The limiting case of this architecture would be to have the cellular base stations essentially eliminated,5 and operate the cellular service primarily through the femtocells. These anecdotal examples show the necessity to approach the emerging complex, dense, and highly adaptable wireless systems with much more generality than that pro- vided by the currently rigid and fixed hub—spoke architecture through dedicated, and separate, infrastructure.

Instead, the transition to Internet access as the primary service 5 Complete elimination is not practical for coverage reasons, but they can be transitioned to become the exception, not the primary path for the bulk of wireless communications. Each successive generation provided more alternatives in the choice of delivery path, management of interference, spectrum choices, and power man- agement. These decisions were increasingly devolved to local awareness and decision making within the nodes.

With the advent of the architecture shown in Figure 4. It is thus reasonable to imagine that the concepts of self-organizing and ad-hoc networks that have been under study by academic researchers can find an application in the commercial practice dominated by less adaptive cellular technology. The incorporation of these technologies may be a fundamental enabler to future devolved and decentralized wireless architectures.

Under- standing of the general concepts of self-organizing networks will likely be essential to the development of future commercial architectures. Although many of these concepts might appear too general and unstructured for application to current wireless practice, they are likely to become increasingly relevant as the challenges of density, spectrum reuse, routing, and other decisions must be devolved to distributed devices.

Therefore, the assumption is that deconflicting channel usage is the fundamental task for any spectrum-management regime. Presumably, if the spectrum is appropriately partitioned, and no frequency is in use by more than one user at a time, no spectrum-management conflicts would ex- ist. Unfortunately, the inherent and inevitable imperfections of receiver circuits create interactions even among devices whose frequencies are deconflicted.

Section 2. In later chapters, we will extend the discussion of spectrum-occupancy constraints to 6 In , it was reported [3] that the population of femtocells had already exceeded that of base stations in their first several years of availability. This issue is not a subtle engineering one, but has significant impact on the operation of devices. It is commonly referred to as desensitization, receiver overload, and co-site interference.

Consequently, we are only looking for centroids that minimize the distance between them and other nodes, which maps to the K-medoids problem. In addition, our problem becomes harder with multiple APs since centroid selection is also affected by the deployed position of the APs, as we attempt to also minimize the distance between each AP and the centroids cluster heads that it serves.

To this end, we generate in the results section optimal solutions only for relatively small scale scenarios. In addition, we propose in the following section an efficient proactive heuristic algorithm for reliable cluster formation that is scalable to large scenarios with close-to-optimal performance.

In our work, we capitalize on this approach and extend it to address the problem of reliable cluster formation in cooperative wireless networks. In the algorithm, each device is assigned a negative fixed charge while virtual centroids are assigned dynamic positive charges; hence, the force among centroids is repulsive, while the force between centroids and devices is attractive. In the context of our problem, we let the charge of each device be a function of its own reliability function as follows:.

This allows devices to be clustered based on their reliability and relative positions. As for identifying the best centroid locations, we set the charge of a given centroid k to be inversely proportional to the number of devices N k associated to it.

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Thus, the charge of centroid k changes in every iteration as follows:. In the network, there exist two main types of forces, namely, repulsion and attraction. Virtual centroids repel each other because they carry like charges while a centroid and a device attract each other because they carry unlike charges. The electrostatic force of attraction exerted by device j on centroid k is similarly calculated as follows:. Consequently, an electric field is formed among the nodes causing centroids to repel from each other and attract to devices.

Hence, centroids move until electrostatic equilibrium is reached where the sum of forces is balanced and centroids are fixed. The algorithm runs over multiple iterations till it reaches a stable state, whereby the virtual centroids positions vary only locally within a circle of small radius. The algorithm then goes into a loop that only halts when the centroids reach a stable state whereby their positions are nearly no longer varying.

Inside the loop, the algorithm initially starts by resetting all associations between centroids and devices that have been done in the previous iteration by setting the centroids degree to zero and finding the best set of devices for each centroid. After finding the best centroid for each device, each centroid c k gets its own vector of associated devices which form the set A k ; hence, A k with c k form an initial cluster. It starts by defining a set M that stores the indices of the cluster head node for each device d i. Initially, d i is set to -1 which means that this set does not belong to any group.

The allocation should maximize performance quality while respecting the constraint on the number of LR connections per AP. For the optimization problem solution, we use the intlinprog mixed integer linear programming function in Matlab denoted as R O p t i m a l in the sequel.

We also compare the R F o r c e algorithm to the standard k M e a n s clustering algorithm in terms of reliability, download bit rates, and execution complexity. We consider several network scenarios and vary system parameters to produce a wide range of results that allow for extracting insights and capturing tradeoffs.

For the Monte-Carlo simulation results, we average over 25 runs for each set of network scenario and system parameters and plot average performance metrics. This clearly demonstrates the gap in complexity between generating the optimal solution and generating solutions using our proposed R F o r c e algorithm and the standard k M e a n s algorithm; the computational complexity of the optimal solution increases exponentially with feasible outcome only for network scenarios having up to devices.

On the other hand, R F o r c e and k M e a n s are shown to have similar low complexity and fast execution time which makes both of them applicable to ultra dense network scenarios with dynamic adaptation over time. The results also demonstrate that this effectiveness in reliability is not negatively impacting communications bit rate or download speed, as the average bit rate on the SR links is shown to be close between R F o r c e and k M e a n s , with both worse than the optimal reliable clustering solution.

For example, assuming devices in a given area with one AP, clusters formed by k M e a n s lead to around triple the failure cost compared to the solution produced by R F o r c e , at the expense of around 0. The same trends and insights on the effectiveness of R F o r c e are also demonstrated in Fig.

We have also compared the various algorithms in terms of average network LR bit rate with results shown in Fig. Similar to the SR bit rates, the results produced by R F o r c e are almost equivalent to k M e a n s and both are not far from R O p t i m a l. Next, we present sample snapshot network scenarios with results generated using R F o r c e to provide additional insights on number of clusters, their locations, and selection of cluster heads.

We represent APs as red squares, cluster heads as orange triangles, connected devices as blue circles, and unconnected devices in outage as gray circles. We also designate the reliability factor of each device by its opacity level. The figure shows that highly reliable devices darker colors are selected as cluster heads and they are also well positioned among the devices they serve in their cluster. We notice that R F o r c e tends to select more cluster heads when the number of APs increases as the algorithm tends to choose more devices as cluster heads to be served directly by the APs due to their proximity.

It is shown also that there are clusters composed of a single device served by in its own by the AP in order to avoid an increase in outage rate. In order to provide a more tangible quantification of the impact of the failure cost metric, we estimate the lifetime of each selected cluster head based on a given set of assumptions taking into account available battery budget. The accuracy of the corresponding information can be refined by computing the intersection area of all cells that cover the current position of the device.

However, the computation of this intersection area, e. To avoid unnecessary workload, one would like to preestimate the possible reduction of location uncertainty, i. The contribution of this paper is an analytical and numerical investigation of the problem. Several approaches are presented for the computation of the information gain, based on stochastic geometry and on a Monte-Carlo method. We show that simple scaling arguments can be used to estimate the order of magnitude of the average information gain, while more complex approximations based on Voronoi cells lead to relatively good results.

Pedro Braconnot Velloso 1 , Marcelo G. Rubinstein 2 , Otto Carlos M. Duarte 3. We evaluate the influence of mobility on the number of sources transmitting voice. Another issue addressed in this paper is the effect of node density on voice transmission.

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Our simulation model has allowed us to identify the main reasons for voice degradation in ad hoc networks. Results show that voice transmission capacity degrades with mobility and network load, being more sensitive to high mobility due to link failures. We also show that node density is also relevant when considering voice transmission on multi-hop networks.

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In our wireless communications system, we define an eightdimensional 8D signal constellation as Cartesian product of four two-dimensional 2D rectangular signal sets. The transmission of an 8D point from the first antenna is achieved by transmitting four concatenated 2D points in four consecutive channel uses. The union of two sets of quasiorthogonal transmission matrices forms a so-called super-quasiorthogonal signal set.

Sallent, R.