The IEEE 802.16m Reference Model

Figure below illustrates the IEEE 802.16 reference model. The data link layer of IEEE 802.16 standard comprises three sub-layers. The service-specific convergence sub-layer (CS) provides any transformation or mapping of network-layer data packets into MAC SDUs. On the transmitter side, the CS receives the data packets through the CS Service Access Point (SAP) and delivers MAC SDUs to the MAC Common Part Sub-layer (MAC CPS) through the MAC SAP. This includes classifying network-layer SDUs and associating them with the proper MAC Service Flow Identifiers (SFID) and Connection Identifiers (CID). The convergence sub-layer also includes payload header suppression function to compress the higher-layer protocol headers. Multiple CS specifications are provided for interfacing with various network-layer protocols such as Asynchronous Transfer Mode (ATM)i and packet-switched protocols such as IP or Ethernet. The internal format of the CS payload is unique to the CS, and the MAC CPS is not required to understand the format of or parse any information from the CS payload.  

The IEEE 802.16 reference model

The MAC CPS provides the core MAC functionality of system access, bandwidth allocation, connection establishment, and connection maintenance. It can receive data from the various convergence sub-layers, through the MAC SAP classified into particular MAC connections. An example of MAC CPS service definition is given in reference. The Quality of Service (QoS) is further applied to the transmission and scheduling of data over the physical layer. 

The MAC also contains a separate security sub-layer providing authentication, secure key exchange, and encryption. The user data, physical layer control, and statistics are transferred between the MAC CPS and the Physical Layer (PHY) via the PHY SAP which is implementation-specific. The IEEE 802.16 physical layer protocols include multiple specifications, defined through several amendments and revisions, each appropriate for a particular frequency range and application.

The IEEE 802.16 compliant devices include mobile stations or base stations. Given that the IEEE 802.16 devices may be part of a larger network, and therefore would require interfacing with entities for management and control purposes, a Network Control and Management System (NCMS) abstraction has been introduced in the IEEE 802.16 standard as a “black box” containing these entities. The NCMS abstraction allows the physical and MAC layers specified in the IEEE 802.16 standard to be independent of the network architecture, the transport network, and the protocols used in the backhaul, and therefore would allow greater flexibility. The NCMS entity logically exists at both BS and MS sides of the radio interface. Any necessary inter-BS coordination is coordinated through the NCMS entity at the BS. An IEEE 802.16 entity is defined as a logical entity in an MS or BS that comprises the physical and MAC layers on the data, control, and management planes.

The IEEE 802.16f amendment (currently part of IEEE 802.16-2009 standard) provided enhancements to IEEE 802.16-2004 standard, defining a management information base (MIB), for the physical and medium access control layers and the associated management procedures. The management information base originates from the Open Systems Interconnection Network Management Model and is a type of hierarchical database used to manage the devices in a communication network. It comprises a collection of objects in a virtual database used to manage entities such as routers and switches in a network.

The IEEE 802.16 standard describes the use of a Simple Network Management Protocol (SNMP),ii i.e., an IETF protocol suite, as the network management reference model. The standard consists of a Network Management System (NMS), managed nodes, and a service flow database. The BS and MS managed nodes collect and store the managed objects in the form of WirelessMAN Interface MIB and Device MIB that are made available to network management system via management protocols, such as SNMP. A Network Control System contains the service flow and the associated Quality of Service information that have to be provided to BS when an MS enters into the network. The Control SAP (C-SAP) and Management SAP (M-SAP) interface the control and management plane functions with the upper layers. The NCMS entity presents within each MS. The NCMS is a layer-independent entity that may be viewed as a management entity or control entity. Generic system management entities can perform functions through NCMS and standard management protocols can be implemented in the NCMS. If the secondary management connection does not exist, the SNMP messages, or other management protocol messages, may go through another interface in the customer premise or on a transport connection over the air interface. Figure 3-4 describes a simplified network reference model. Multiple mobile stations may be attached to a BS. The MS communicates to the BS over the air interface using a primary management connection, basic connection or a secondary management connection. The latter connection types have been replaced with new connection types in IEEE 802.16m standard

OPTIMIZATION AND CELL PLANNING MODEL

The second problem is to find the set of active base stations, their orientation, and transmission power to achieve the optimum coverage and capacity assignment to users. The problem is separated into two parts.

1 Transmission Towers Construction

We define the concept of Transmission tower as a fixed set of active base stations placed at one active site. One site can have many transmission towers but only one of them can be active. We try to explore different alternatives for the number of antennas, their transmission power, orientation, and radiation pattern. After we build them, the problem reduces to choose one of them from every available active site.

The process begins with a set of candidate sites. We discard candidate sites with very low coverage. Also, if there are two sites with similar coverage, we discard the one with the lowest coverage. To build the transmission towers at one site, we begin placing one omni-directional antenna with the maximum transmission power. If this base station is not saturated, i.e., all covered users can connect to it, then we create several transmission towers with one single antenna and different transmission powers, chosen from a set of discrete values. We use also 120° and 180° sectorized antennas.

On the other case, if the first omni-directional antenna base station is saturated, i.e., not all covered users can connect because of capacity restrictions, then we build a set oftransmission towers composed of several antennas with 120° and 180° sectors. We use all possible combinations of transmission power and sectors to build several options for the site. We solve coverage and capacity assignment by previously described algorithms to find the orientation of each set of active base stations. We finally remove redundanttransmission towers from the set of available ones. We solve this for every site to get a set of transmission towers and a matrix that keeps a record of the users that connect to each one of them. This information is used in the optimization process.

2 Optimization Process

In this process, we try to find the set of active transmission towers to optimize coverage and connect the highest number of users. We fix the number of sites during one execution of the algorithm to find the best solution. After that, we increment the number of sites and run the algorithm once again. At the beginning, we start from an empty solution, then we try to improve it by activating, deactivating, or moving transmission towers. We do this every iteration using a probabilistic model to decide if a new solution is chosen or not over the current one. However, we keep track of the best solution that has been reached so far. The iterative process has two main components:

• Building of a new solution: In this process, we start from the current solution and try to improve it by a randomly chosen modification. Our modifications are based on those presented in Ref. [20]. We can deactivate any active transmission tower and activate any inactive transmission tower. We deactivate an activetransmission tower randomly by assigning a deactivation probability inversely proportional to the number of users connected to it. We activate a new transmission tower randomly by assigning an activation probability proportional to the number of uncovered users that could connect to it. A new solution is analyzed for a feasible channel assignment by trying several combinations to reduce interference. We fix the number of available channels. We finally use the channel combination that has the lowest interference level, represented by the highest number of connected users.
• Optimization process: This algorithm iterates, comparing the new candidate solution and the current solution. This is the core process for simulated annealing, in which a new candidate solution replaces the current solution according to the improvement and the temperature of the system. If the candidate solution is better than the current solution, it is accepted. Otherwise, it has an acceptance probability that depends on how bad is the new solution with respect to the current solution and the temperature of the system. This process keeps track of the current solution and the best solution ever found.

The metric used to decide the performance of a solution is the percentage of users that could connect to any base station. It means that a better solution has more connected users than a previous one. We must recall, that other optimization criteria are included in the inner process.

• Interference is reduced during the building of a candidate solution by choosing the lowest interference channel assignment, i.e., each new candidate solution tries to increase the number of connected users with the minimal interference.
• For the iteration process we add and remove base transmission towers to the new candidate solution. If we have two solutions with similar number of connected users, we choose the one with the lowest number of base stations. This way we reduce the cost related to the number of base stations.
• The QoS guarantees are included in the User-Base station assignment model, where we try to connect users to base stations according to their spectrum efficiency. Also, in the Capacity assignment model, if we connect a user to a base station, we guarantee that the requirements are satisfied.