200 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 51, NO. 1, JANUARY 2002 Unified Approach of GOS Optimization for Fixed Wireless Access Nathan Blaunstein, Ran Giladi, Avraham Freedman, Senior Member, IEEE, and Moshe Levin Abstract—We present analytical and simulation models that obtain the minimum number of required communication channels for subscriber units and their allocations so as to increase the level of the grade of service (GOS) per user of fixed wireless access communication systems (FWA or WACS). On the basis of the proposed analytical and simulation models, a new methodology of using user radio ports (RPs) is introduced for evaluating the optimal number of RP systems, their allocation policy, and their spatial distribution for different configurations. This methodology enables us to increase the efficiency of FWA services on the basis of GOS maximization. Index Terms—Balanced and unbalanced link matrix, controlled resources search, fixed wireless access, grade of service, predefined order search, random search. I. INTRODUCTION IXED wireless access (FWA) communication systems are considered to be part of the personal communication services (PCS) as a replacement for and enhancement of traditional wireline services. FWAs are also often called wireless access communication systems (WACSs) and radio or /wireless in the local loop (RLL or WLL, respectively). Standard organizations are currently involved in establishing and investigating various technical solutions for FWA [1]–[9]. These systems are useful in places where wireline telephone access solutions are impractical, expensive, or temporary. More and more manufacturers and operators are considering FWA in order to enhance regular services (mainly bypass operators) and to provide better, cheaper, and more flexible services. The main principle of these systems is to connect end users to the telephone network via wireless radio links, which are allocated to them when required. This is in contrast to regular wireline end users, where the reserved wireline to the end user guarantees a free channel to each end user. FWA therefore performs a kind of resource switching by allocating channels (lines) to end users per request, rather than by providing all users with resources to access the network. FWA systems, however, create problems in establishing reliable, accessible links between the end users and the public switched telephone network (PSTN), in contrast to common wireline systems. The design of these systems must take into account user traffic intensity and the probability that the system F Manuscript received June 10, 1998; revised August 16, 2001. N. Blaunstein and R. Giladi are with the Department of Communication Systems Engineering, Ben Gurion University, 84105 Beer-Sheva, Israel (e-mail: natanb@cse.bgu.ac.il). A. Freedman and M. Levin are with InnoWave-ECI Telecom, Tel-Aviv, Israel (e-mail: avif@hexagonltd.com). Publisher Item Identifier S 0018-9545(02)00437-1. cannot establish a call-for interconnection, which relates to the grade of service (GOS), i.e., the probability of a blocked system that causes the unavailability of telephone lines to customers. Until recently, an FWA infrastructure was designed by using the Erlang loss function [10]. The number of required channels was calculated as a function of user traffic intensity and desired GOS (generally smaller than 1%). The use of Erlang’s formula is justified only in cases where all users have access to all resources of the system (i.e., lines), a situation referred to as a full availability system [see Fig. 1(a)], and where there is a large number of end users using the available lines. In the case of a wireless system, various obstacles, as well as wave attenuation, require that the system components be deployed in a proper geographical distribution, so that users can access resources in a distributed way. This is referred to as a limited availability system [see Fig. 1(b)]. In other words, not all the lines are available to all the users (some users have access to some lines, while others can access another set of lines). The limited availability problem has been encountered in the design of telephone exchanges [10, Ch. 6], [11, Ch. 4], where a source has access to only a part of the channels provided. The actual switching problem was solved by grading (see [10] and [11]). However, the limited availability problem we describe in this paper is different. The resource availability is determined by the propagation conditions and cannot always be as well balanced as is achieved by grading within the exchange. To estimate the average probability of blocking the carried traffic, approximate formulas have been developed [e.g., the Palm Jacobaeus (PJ) and modified PJ formulas]. However, because the model we are using is a simple one, we chose to use the exact solutions of the state equations in order to estimate the probability of blocking. The formulas described can, in some cases, be used for more complex problems. The problem of limited availability has an effect on any cellular wireless system, be it mobile or fixed access, and should be taken into account in the cellular network design. There is a large variety of algorithms of resource allocations (or channel allocation, as the spectrum is the scarcest resource in a wireless system) among the various users. Reference [12] provides a very thorough and extensive description of fixed channel allocation and dynamic channel allocation algorithms. None of the algorithms, however, takes limited availability explicitly into account. Generally, a balanced load assignment (as in fixed channel allocations) or an optimal approach (like, e.g., [13]) may overcome this problem. Here, we demonstrate the limited availability problem for relatively simple cases. This paper is organized as follows. In Section II, we build a model that enables us to calculate the min- 0018-9545/02$17.00 © 2002 IEEE BLAUNSTEIN et al.: UNIFIED APPROACH OF GOS OPTIMIZATION FOR FIXED WIRELESS ACCESS (a) 201 (b) Fig. 1. (a) Full and (b) limited availability. imum number of required channels and their allocation in order to achieve a predetermined GOS per user. Analytical evaluation of the model is described in Section III, with a methodology for carrying out the numerical simulations presented in Section IV. Section V describes the simulation results, followed by an analysis of the simulations. The results obtained in Section V are very similar to those of load balancing algorithms, as described in [14] and [15]. Section VI discusses the similarities and differences of the two problems. Section VII presents a brief summary and some conclusions. II. MODEL DEFINITION A fixed wireless system is composed of shared telephone lines that are potentially offered to a fixed population of end users. This system is composed of antennas (radio ports, noted in Fig. 1 as RPs). These devices link the fixed PSTN to the end users’ devices (subscriber units, noted in Fig. 1 as SUs). RPs are “intelligent” devices that can allocate telephone lines to a large number of users (typically tens of SUs). The number of channels that the RP can allocate depends on the frequency bandwidth of the air transmission (each channel requires a certain frequency bandwidth). Given the users’ placement in a certain area, the objective is to deploy the minimum number of RPs in order to save costs while simultaneously achieving a predefined GOS per user. It is important to note that the optimal criterion here is the GOS that each user receives, and not, for instance, some other optimal criterion like the average GOS of all the users. In fact, the system must provide a sufficient level of service for each connected user. In other words, we face a min-max problem. The deployment of the RPs must also take into account geographical and topological constraints. Some zones are forbidden completely (military or private areas), while others are not convenient for installation (terrain accidents). For every RP deployment, the link loss between each RP and each SU is computed, resulting in numbers between zero and one that define the quality of the connections. These numbers are rounded to zero when they are smaller than a certain threshold and to one when they are larger than this threshold. They are stored in a matrix called the link matrix . Rows in this link matrix represent the SUs, and columns represent the RPs. Thus, a “1” in row and column indicates a physically possible connection between the th SU and the th RP, while a “0” means that no physical connection is possible. Note that when a physical connection is possible, one can always cancel this connection in order to achieve better results in the optimization of the GOS (as we will see further on). and Each user is supposed to have a fixed calling rate . In fixed wireless systems, one must give each holding time user an allocation policy for his connection to the system. The different policies that we considered are as follows. method): each user 1) Random search (denoted as the chooses randomly from among his available RPs (with “1” in the corresponding row of the link matrix) for his first attempt to get a connection. If the chosen RP has at least one free channel, the user will receive and use it, and will speak for an exponential time (with parameter ). If the chosen RP is occupied (all its channels are currently busy), the user randomly chooses another RP amongst the other RPs available to him, and so on. At the end of the process, the user that did not receive a connection gets a blocked call and tries again after an exponential time (with parameter ). 2) Predefined order search (denoted as the method): each user chooses the order of the successive connection trials to his corresponding RPs from a predefined order given in advance and stored in a database. The predefined order of size . It provides search is defined by a matrix the order of the successive connection trials for each user when attempting to connect to its RP. The th row of will be completed with zeros if the th user does not have access to all the deployed RPs in the area. For instance 202 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 51, NO. 1, JANUARY 2002 and mean that the first user will try only RP (from , we see that it is in fact the only possibility), the second user will try only RP , and the third user will try RP and, in case of a failed attempt, will try RP . For each given link matrix, there are many possibilities of a predefined order search, but in each case the results will be presented with optimized (noted as , which means achieving the best min-max (GOS) for a given link matrix. 3) Controlled resources search (denoted as the method): each user will know at any moment the availability of all its allowed RPs. He will use the RP with the highest number of free channels. We define a user-RP-balanced link matrix as a matrix representing a situation where each user can get connected to and use the same number of RPs, and each RP can get connected to and be used by the same number of SUs. We specify a user-RP-balanced link matrix with customers and RPs by matrix , where total number of users an and total number of RPs in the area. For instance is a balanced 2 matrix, while is not balanced because the second user can get connected to and use two RPs while the first user can only get connected to and use the first one. Note that a full “ ” matrix is always balanced [and is an matrix], being in fact the full availability case [presented in Fig. 1(a)], where the Erlang B formula is applicable [6]. This formula can be used instead of the Engset distriis infinite, bution [6] for the case when the number of SUs whereas the number of channels per RP is finite. We further define an RP-balanced link matrix as one where each RP can get connected to and be used by the same number of SUs. Note that every user-RP-balanced matrix is also an RP-balanced matrix. For instance (a) (b) Fig. 2. (a) Predefined order search state diagram. (b) Random search state diagram. then by using a random search. We use two user groups with a link matrix that describes their connections to the servers as (a nonbalanced link matrix). The analysis is based on two typical situations of SU-RP connections presented in Fig. 1(b). The state diagrams of these two servers in the two serving policies, i.e., the predefined order and the random searches, are shown in . specifies that Fig. 2(a) and (b). Each state is marked the first server is occupied by a user from the first group (1) or the second group (2), or is free (0); specifies how the second server is occupied in the same manner. In the case of a predefined order search [state diagram in Fig. 2(a)], a user from the first group can use server RP , and if it is occupied he can use server RP (if it is free; if not, he leaves the system without service, which is a “blocked” situation). A user from the second group can use only RP , if it is free. If it is not, he is blocked (he leaves the system without service). The predefined search matrix is therefore is an RP-balanced link matrix, where each RP is connected to two users (RP to user 1 and 3 and RP to user 2 and 3). III. ANALYTICAL EVALUATION In the analytical model and, subsequently, in the numerical and use the Erlang B simulations, we assume that formula [6] since in our approach the parameter is independent of the number of customers or servers in use. Below we analyze two server systems, first by using a predefined order search and The probability of the system’s being in state is state transition equilibrium equations are given by . The (1a) (1b) (1c) (1d) BLAUNSTEIN et al.: UNIFIED APPROACH OF GOS OPTIMIZATION FOR FIXED WIRELESS ACCESS (1e) (1f) (1g) After deriving system (1a)–(1g), we can obtain the probability of the event that a user of the first group gets an occupied system by the following equation: GOS (2) The probability that a user of the second group gets an occupied system can also be obtained from system (1a)–(1g) as GOS (3) The case of a random search is slightly different. The state diagram of this service is presented in Fig. 2(b). For this case, the state transition equilibrium is given by the following system: (4a) (4b) (4c) (4d) (4e) (4f) (4g) From system (4a)–(4g), one can obtain the probability that a user of the first group gets an occupied system GOS (5) 203 . As can be seen from the illustration, the difference is . In the case of the limited to a value of about 0.015 for second group of users, however, the situation is the opposite. The random search GOS is larger than the predefined order search GOS , which means that in terms of the second group of users, it is advisable to adopt the predefined order search. The difference, GOS (random) GOS (predefined) from (3) and (6), is presented in Fig. 3(b) as a function of the value of . As can be seen, there are utilization in the range . The large differences in GOS in the range better search policy, in terms of maximal GOS for all the users, is obviously the predefined order search (users of group 2 show much higher differences in GOS). The better search policy, in terms of average minimal GOS for all the users, could be evaluated by calculating GOS (predefined) GOS (random) GOS (predefined) GOS (random) (7) is the number of users where is the total number of users, is the number of users in the second in the first group, and group . The random search policy is better when (7) is positive, etc. The precise calculated difference is GOS (8) This leaves us with the problem of comparing these search policies to the controlled resource search, as well as the problem of comparing policies with bigger link matrices. As can be seen from the graph depicted in Fig. 3(c), the distance between the two differences presented in Fig. 3(a) and (b) for in the range , which is (7) for case , suggests that the predefined order search is still the best search policy. IV. METHODOLOGY Because an analytical description for the general case is impossible, we used simulations of the model presented above. We assumed that there are two groups of users connected to two RPs, as in Fig. 1(b), and used the structures of matrices defined above. To check how the structure of the matrix and the allo2 cation policy affect the GOS, we ran simulations using matrices in the general form and the probability that a user of the second group gets an occupied system — — GOS (9) (6) It is worthwhile to compare the two search methods in this case. It turns out that the predefined order search GOS is slightly larger than the random search GOS , which means that in terms of the first group of users, it is advisable to adopt the random GOS (random) search. The difference, GOS (predefined) , from (2) and (5), depends on the value of utilization, as follows from the graph [Fig. 3(a)] for values in the range — — rows of (1,0), rows of (0,1), and rows where there are . This -matrix defines three of (1,1), where users that can classes of users: the first class is composed of only use RP ; the second class is composed of users that can 204 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 51, NO. 1, JANUARY 2002 (a) (b) (c) Fig. 3. The difference in GOS for users of (a) group 1 and (b) group 2. (c) Average random GOS and average predefined GOS. only use RP ; and the third class is composed of users that can use both RP and RP , as in Fig. 1(b). The general form of this presentation enables us to check a variety of situations, such as a user-RP-balanced matrix, where and (so we get an ( 2,1) balanced and (so we get an ( ,2) balmatrix) or anced matrix). We can also check an RP-balanced matrix, when , varying from a slightly user-RP-unbalanced or ) to a severely link matrix (when user-RP-unbalanced link matrix (when or ). To optimize the number of RPs and their location in the area, we ran simulations with different configurations of the BLAUNSTEIN et al.: UNIFIED APPROACH OF GOS OPTIMIZATION FOR FIXED WIRELESS ACCESS link matrix. We began with the balanced configuration and then gradually added links to degrade this balanced situation. Then we compared different allocation policies for all cases. The user load is assumed constant, conforming to data that we obtained from experimental situations composed of incoming and outgoing calls symmetrically distributed. The main parameters assumed in the simulations are as follows: user load equals , and the number of 0.0625, the size of the population . channels per RP (available lines from each RP) 205 TABLE I GOS RESULTS OF A DIVERTED USER-RP-BALANCED LINK MATRIX V. SIMULATION RESULTS Using simulations, we examined the behavior of the maximal GOS , in terms of two value of the grade of service, i.e., main parameters: 1) the structure of the connections between RP and SU; 2) the allocation policy, i.e., the method used by each user to establish his connection to the system. In the following section, we analyze these results for the case of two different link matrices. A. The User-RP-Balanced Link Matrix We start with balanced situation ( , where the matrix and the predefined search order is 2,1). The simulation uses and , Fig. 4. GOS results of a diverted user-RP-balanced link matrix. configuration: predefined search order will now be — . The — (11) — — — — (10) — — (which is the only possibility in this case). The maximum GOS of all the users, for each of the three and , which are the same in this particular methods ( case), is 2.98% maxgosR, 2.94% maxgosP, and 2.98% maxgosF, respectively. In fact, in this case, the Erlang B formula [6] is users and the last users form applicable because the first two independent user teams (they have their own independent servers [RP and RP , as follows from Fig. 1(b)]. The Erlang B formula yields GOS 2.94 % and the Engset formula yields GOS 3.01%. These results are in accordance with our results. On this basis, we assume that the results of the simulation should be closer to those obtained by the Engset formula; however, in , the Erlang B and the Engset approaches our case with are almost the same. Again, in this particular case, we find that the expected theoretical GOS result is identical for every user and does not depend on the policy given to users. Now, we diverge from the user-RP-balanced link matrix by gradually adding -type users to the Note that this is the optimal order and is intuitively determined; RP serves more possible users than RP [Fig. 1(b)], so it is sensible to permit the third class of users to try RP first. The and ) are results of simulation for the three methods ( presented in Table I and graphically in Fig. 4. Note that when is zero, we are dealing with two independent groups—half the users are in the first group using RP and the other half are increases up to 20, we in the second group using RP . When transfer users from the second group (that uses RP ) to a new group, a third one, that allows the use of both RP and RP . is 20, we are in a situation where half the users are When in the first group, using RP , and the other half are in the third group, using both RP and RP . In this case, the predefined order method is the best of the three methods, and adding links (until half of the users get connected to RP and RP ) makes matters worse; disconnecting links to establish a balanced situation might even be advisable. This is, of course, true with respect to the maximal GOS of all the users. This kind of conin figuration makes life easier for users in the new group terms of their GOS. 206 Fig. 5. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 51, NO. 1, JANUARY 2002 Fig. 6. GOS results of the RP-balanced link matrix. GOS results of diverting to a full availability link matrix. In the next cases, we check what happens if we shift the balwith even more supplementary connections, anced matrix getting closer to the full availability case . The predefined search order is TABLE II GOS RESULTS OF DIVERTING TO A FULL AVAILABILITY LINK MATRIX matrix will have the configuration: and the predefined search order matrix — , — (12) — — — — — — — — Note that this is the optimal order, intuitively determined; RP is granted first to the first team who can access only one RP, while the second team tries RP first. The results of simulation for the three methods are presented in Table II and graphically in Fig. 5. Note again that we are essentially continuing the previous case. After the second group was emptied, we started to move users from the first group to the third one. In this situation, when , we are approximately in the same situation as the last phase in the previous example, i.e., half the users are in the first group, using RP , and the other half are in the third group, using both increases, and more users are moved from RP and RP . As the first group to the third one, we end up with . This is the most efficient situation since almost all users are in the third group and are using both RP and RP . From the histograms presented in Fig. 5, we see that in this case, the predefined order to method is again the best of the three methods. For , the maxgosP (2.91% to 0.54%, respectively) becomes smaller than the balanced situation [( 2,1) with 2.94%]. Note , we reach the full availability case [Fig. 1(a)], that for which we can predict by the Erlang B formula [6] GOS % . This is the ( ,2) balanced matrix case that achieves the best level of service. B. The RP-Balanced Matrix We start by examining how the GOS behaves in the RP-balanced matrix case for the three methods ( , , and ). The link where the last two partitions of belong to the third class of users and the number of rows is equal to the number of ). Note that is intu(2,1) rows (and equal to itively optimized by equalizing the load on each RP. The results of simulation for the three methods are presented in Table III and graphically in Fig. 6. In this situation, we are starting with two independent groups, each being served by a separate RP. Half the customers are in increases, we the first group and the rest in the second. As take users from the two groups and move them to a new third is 20, then group being served by both RP and RP . When a quarter of the users are in the first group, being served by RP ; another quarter are in the second group, being served by RP ; and half are in the third group, using both RP and RP . approaches 38, almost all users are in the third group, When using both RP and RP . The histograms presented in Fig. 6 reveal a strange phenomenon: using the and methods and adding more links from the balanced situation ( 2,1) to the GOS (in the pseudobalanced situation only increases the method, we get a degradation of the GOS from 2.99% BLAUNSTEIN et al.: UNIFIED APPROACH OF GOS OPTIMIZATION FOR FIXED WIRELESS ACCESS 207 TABLE III GOS RESULTS OF THE RP-BALANCED LINK MATRIX to 3.28%, and for the method from 2.99% to 3.33%). The method behaves in the opposite way: the GOS decreases from 2.87% to 1.11%. The GOS in the balanced situation (from the Erlang B formula [6]), as mentioned earlier, is equal to 3.01%. Thus, the and methods are never worthwhile, but using the method always improves the GOS in comparison with the balanced situation. C. Analysis From the simulation results presented in Tables I–III and Figs. 4–6, it follows that for the case of a user-RP-balanced link matrix, the maximum GOS of all users for the three methods of the search is 3.0%, and it degrades as links are added (the maximum GOS increases) in non-RP-balanced configurations. is the best of Moreover, the predefined search order method the three methods for different non-RP-balanced configurations of link matrices. In the case of the RP-balanced link matrix for various kinds of configurations, only the controlled resources search method improves the GOS level, decreasing it from 2.87% to 1.11% as we move from one user-RP-balanced situation ( 2,1) (two separate groups) to a second user-RP-balanced situation ( ,2) (full availability case). The other two methods GOS : The -search increase is from 2.99% increase the to 3.28% and the -search from 2.99% to 3.33%. We also notice that going from one user-RP-balanced situation to another [( 2,1) to ( ,2)], the link matrix can reach many configurations, including RP-balanced link matrix configurations. When the configuration is not an RP-balanced link matrix case, it is better to use the -search method in order to achieve the best (minimal) maximum GOS. However, if an RP-balanced situation does not exist, links can be added or deleted, so that it can be configured to the nearest balanced configuration, an effect that depends on the degree of nonbalancing of the link matrix. After a certain point, when links are disconnected in order to return to the balanced situation, a GOS can be achieved using the -search method. better This point depends on the ratio of the links to the size of population and RP number. In the RP-balanced situation, the -search method allows us to obtain the correct solution to the SU and RP optimization problems. When using the nonbalanced link matrix, the -search is best in terms of maximal GOS, but the -search gives us a good alternative as well, in comparison with the and search methods. In practice, the main parameters might be different from those presented in the numerical simulation. In fact, a typical size of might be in the hundreds, and the number the population of deployed RPs might be about ten. Fortunately, in the case of RP-balancing, the -policy gives us the correct solution to the optimization problem. In other cases (where the -matrix is severely nonbalanced), the predefined search order method is optimal, but might not be so easy to calculate. In these cases, the -search method still provides a good alternative. VI. COMPARISON TO OTHER APPROACHES The results of the analysis suggest that balancing the load on each RP gives the optimal solution of the problem. Load balancing has been treated extensively, mainly in the context of networks; however, it is not identical to the problem described above. On the other hand, the similar solutions might suggest that some of the methods and algorithms described in the context of load balancing can be applied for the case of limited availability. Work in [14] introduces an algorithm for rehoming in order to balance the load of mobiles on mobile switching centers (MSCs). The home MSC handles call-related network activities, such as mobile termination and call delivery, and inter–visitor location register (VLR) registration activities, which load the MSC and can be balanced by moving a group of mobiles to another MSC. The algorithm suggested therein is doing the rehoming process according to the criterion of maximum benefit to the load standard deviation ratio, where benefit is expressed in the terms of maximal load reduction. This algorithm could be applied to the limited availability case if there is an overlap of coverage area of more than two RPs. The measure of the benefits and costs would depend on the characteristics of the system in question. Work in [15] formulates the load balancing problem as an optimal control problem and shows that variants of the “least loaded routing” policy is asymptotically optimal for large call arrival rates. The problems descirbed in [15] are very similar to the problem described above, especially the “basic dynamic model” given in Section III. However, the cost function chosen in [15] does not reflect the real cost for an FWA system, which aims to minimize the probability of blocking of the user located in the worst location. In [15], on the other hand, the cost function was a quadratic function of the load, which is not at all identical. The least loaded routing algorithm studied in [15] is the algorithm suggeted above, which was shown here to be the best choice in most cases. VII. SUMMARY This paper presents a new methodology that is based on three different search methods by the user/subscriber unit, whose purpose is to optimize the number of RPs and their spatial , predefined order , and controlled allocation: random . For each of these methods, we carried out resources simulations for two different configurations of link matrices: 208 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 51, NO. 1, JANUARY 2002 the user-RP-balanced and RP-balanced matrices. The grade of service was evaluated for two variants of communication channel service policies. The simulated results resembled those of the traditional approach based on Erlang’s canonical formula. Generally, the controlled resource approach was proven to be the optimal policy, in terms of GOS, as the simulations showed. In some cases, however, the predefined order approach was preferred. Both analytical and numerical analysis enabled us to predict the maximal GOS level and to obtain the optimal number of radio ports and their effective allocation with subscriber units for wireless access communication systems. Similar results were obtained in studies of the load-balancing problems, although the problem by itself is not identical. Load-balancing algorithms can thus be applied, at least on a first trial basis, for the limited availability problem as well. Ran Giladi received the B.A. degree in physics and the M.Sc. degree in biomedical engineering from The Technion, Haifa, Israel, and the Ph.D. degree in computers and information systems from Tel-Aviv University, Tel-Aviv, Israel. He is the President and CEO of InfoCyclone Inc., a company he cofounded in October 2000. He previously was the Founder and Head of the Department of Communication Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel (1992–2000). He taught at the Business School and at the Electrical Engineering Department of Tel-Aviv University (1987–1993). He cofounded Ramir Ltd., which was later acquired by Adacom, and served as Vice President of R&D in both companies (1984–1986). Prior to that, he was an R&D Manager with Fibronics and System Programmer in the computer center of The Technion. He also founded the Israeli Consortia for research on network management systems and served as the Chairman of the Consortia Board of Directors. His research interests include computer and communications systems performance, data networks and communications, and network management systems. REFERENCES [1] “Generic framework criteria for version 1.0 wireless access communications systems (WACS),” Bellcore, FA-NWT-001 318, 1992. [2] “Comparison of the economics of radio and conventional distribution for rural areas,” Bellcore, SR-NPL-000 676, 1987. [3] “ETR on radio in the local loop,” ETSI, 1994. [4] “Overview of different access techniques for point to multipoint radio relay systems,” ETSI, Sophia Antipolis, France, TR 101 274, 1998. [5] “Wireless local loop,” European Radio Communications Office (ERO), Copenhagen, Denmark, Oct. 1997. [6] “Wireless access local loop,” International Telecommunication Union (ITU-R), Geneva, vol. 1, 1996. [7] “Fixed service trends post-1998,” European Radio Communications Office (ERO), Copenhagen, Denmark, May 1998. [8] C. V. Macario, Cellular Radio, London, U.K.: Macmillan, 1993. [9] Cellular Radio Handbook, Quantom, New York, 1990. [10] R. Syski, Introduction to Congestion Theory in Telephone Systems, 2nd ed. New York: North-Holland, 1986. [11] J. R. Boucher, Voice Teletraffic System Engineering, 2nd ed. Norwood, MA: Artech House, 1988. [12] I. Katzela and M. Naghshinen, “Channel assignment schemes for cellular mobile telecommunication systems: A comprehensive survey,” IEEE Personal Commun., pp. 10–31, June 1996. [13] S. Papavassiliou and L. Tassiulas, “Joint optimal channel base station and power assignment for wireless access,” IEEE/ACM Trans. Networking, vol. 4, pp. 857–872, Dec. 1996. [14] W. Yuan et al., “Load balancing in Wireless Networks,” in Proc. GLOBECOM, 1997, pp. 1616–1620. [15] M. Alanyali and B. Hajek, “On simple algorithm for dynamic load balancing,” in Proc. IEEE INFOCOM, vol. 1, 1995, pp. 230–238. Nathan Blaunstein received the B.Sc. and M.Sc. degrees in radiophysics and electronics from Tomsk University, Tomsk, Russia, in 1972 and 1976, respectively, and the Ph.D. and D.Sc. degrees in radiophysics and electronics from the Institute of Geomagnetism, Ionosphere, and Radiowave Propagation (IZMIR), Academy of Science USSR, Moscow, Russia, in 1985 and 1991, respectively. From 1979 to 1984, he was an Engineer, a Lecturer, and then, from 1984 to 1992, a Senior Scientist, an Associate Professor, and a Professor at Moldavian University, Beltsy, Moldova, former USSR. From 1993 to 1995, he was a Researcher in the Scientific “Shapira” Program, Ben-Gurion University of the Negev, Beer Sheva, Israel. Since 1995, he has been a Senior Scientist with the Department of Electrical and Computer Engineering and a Visiting Professor in the Wireless Cellular Communication Program at Ben-Gurion University, where he is now a Professor. His research interests include problems of radiowave propagation, diffraction, and scattering in various media for purposes of radio location, mobile-satellite and terrestrial communications, and cellular and mobile systems performance and services. Avraham Freedman (M’87–SM’98) received the B.Sc., M.Sc., and Ph.D degrees from Tel-Aviv University, Tel-Aviv, Israel, in 1974, 1985, and 1993, respectively, all in electrical engineering. Between 1980 and 1993, he was with the Israel Defense Forces and with Elta Electronic Industries, Ashdod, Israel. Between 1993 and 1996, he was a Research Fellow with the Valley Forge Research Center, School of Electrical Engineering, University of Pennsylvania, Philadelphia, and was involved in development and research of communication, antenna, and radar systems. Between 1996 and 2000, he was with Inno Wave—ECI Wireless Systems, Petach-Tikva, Israel, as a member of the Business Research Group, and involved in the research and planning of present and future FWA systems. He was CTO of Hexagon System Engineering, a consulting firm specilizing in tools for cellular systems planning and deployment, where he is still involved and consults. He is currently with the Electronic Systems Department, Faculty of Engineering, Tel-Aviv University. He has published more than 20 papers in the fields of radar and communication systems. He is active in ETSI, IEEE, and other standardization bodies in defining and developing standards for FWA systems. Moshe Levin received the B.S. degree in electrical engineering in from The Technion—Israel Institute of Technology, Haifa, Israel, in 1981. He received the M.A. degree in electrical engineering, the Ph.D. degree in radar signal processing, and the Ph.D. degree in business administration (strategic planning) from Tel-Aviv University, Tel-Aviv, Israel in 1986, 1989, and 2000, respectively. From 1981 to 1992, he worked in the defense industry, mainly in research and development of signal-processing algorithms. From 1992 to 1998, he was with Tadiran Telecommunications as the Vice-President of Research and Planning and as the Chief Scientist of the Wireless Systems Division. Currently, he is President and CEO of Hexagon System Engineering Ltd., a fixed wireless access consulting firm. He is the author of more than 40 papers covering various aspects of wireless local-loop systems.