Convolutional coding for finite-state channels

We propose new decoders for decoding convolutional codes over finite-state channels. These decoders are sequential and utilize the information about the channel state sequence contained in the channel output sequence. The performance of these decoders is evaluated by simulation and compared to the performance of memoryless decoders with and compared to the performance of memoryless decoders with and without interleaving. Our results show that the performance of memoryless decoders with and without interleaving. Our results who that the performance of these decoders is good whenever the channel statistics are such that the joint estimate of the channel state sequence and the channel input sequence is good, as, for example, when the channel is bursty. In these cases using even a partial search decoder such as the Fano decoder over the appropriate trellis is nearly optimal. However, when the information between the output sequence and the sequence of channel states and input diminishes, the memoryless decoder with interleaving outperforms even the optimal decoder which knows the channel state

Implied costs for multirate wireless networks

Implied costs for multirate wireless networks are calculated and their use is demonstrated for quantifying mobility, traffic load, call pricing, network optimization and for evaluating trade-offs between calls of different rates. User mobility is modeled by assigning call termination and call handoff probabilities. Fixed Channel Assignment (FCA) is used with priority for handoffs over new call arrivals by reserving a number of channels in all the cells. The performance measures used are new call blocking and handoff drop probabilities. The implied cost is calculated for the network net revenue, which considers the revenue generated by accepting a new call arrival into the network as well as the cost of a handoff drop in any cell. Simulation and numerical results are presented to show the accuracy of the model. The implied costs are used to suggest pricing techniques for different calls based on mobilities and bandwidth. Finally, a nonlinear constrained optimization problem is formulated to calculate the sum revenue for a given network by maximizing the net revenue using implied costs in a gradient descent algorithm, The implied cost analysis also shows that matching capacity distribution to not only exogenous traffic, but also to mobility can significantly increase revenue

Effects of Call Arrival Rate and Mobility on Network Throughput in Multi-Cell CDMA

This presentation discusses call arrival rate and mobility. The effect of call arrival rate on the capacity of a code-division multiple-access (CDMA) cellular network is evaluated

Mobility-based CAC algorithm for arbitrary call-arrival rates in CDMA cellular systems

This paper presents a novel approach for designing a call-admission control (CAC) algorithm for code-division multiple-access (CDMA) networks with arbitrary call-arrival rates. The design of the CAC algorithm uses global information; it incorporates the call-arrival rates and the user mobilities across the network and guarantees the users\u27 quality of service (QoS) as well as prespecified blocking probabilities. On the other hand, its implementation in each cell uses local information; it only requires the number of calls currently active in that cell. We present several cases for a nontrivial network topology where our CAC algorithm guarantees QoS and blocking probabilities while achieving significantly higher throughput than that achieved by traditional techniques. We also calculate the network capacity, i.e., the maximum throughput for the entire network, for prespecified blocking probabilities and QoS requirements. © 2005 IEEE

Flexible allocation of capacity in multi-cell CDMA networks

The effect of reverse power levels on the capacity of a code-division multiple-access (CDMA) cellular network is evaluated. The inter-cell and intra-cell interferences of every cell on every other cell are first calculated for a given network topology. Based on this, the nominal power of users is increased by a factor we call the Power Compensation Factor (PCF) which enables small cells to overcome the excessive interference from adjacent large cells. The PCF\u27s provide flexibility in the allocation of capacity. By changing the PCF\u27s, capacity can be exchanged between cells. The implied cost of the network capacity function with respect to the PCF\u27s is calculated and used to maximize the capacity of the network by applying a gradient descent algorithm

Call admission control scheme for arbitrary traffic distribution in CDMA cellular systems

Designing a call admission control (CAC) algorithm that guarantees call blocking probabilities for arbitrary traffic distribution in CDMA networks is difficult. Previous approaches have assumed a uniform traffic distribution or excluded mobility to simPlifY the design complexity. We define a set of feasible call configurations that results in a GAG algorithm that captures the effect of having an arbitrary traffic distribution and whose complexity scales linearly with the number of cells. To study the effect of mobility and to differentiate between the effects of blocking new calls and blocking handoff calls, we define a net revenue function. The net revenue is the sum of the revenue generated by accepting a new call and the cost of a forced termination due to a handoff failure. The net revenue depends implicitly on the GAG algorithm. We calculate the implied costs which are the derivatives of the implicitly defined net revenue function and capture the effect of increases in the number of calls admitted in one cell on the revenue of the entire network. Given a network topology with established traffic levels, the implied costs are used in the calculation of a CAC algorithm that enhances revenue and equalizes call blocking probabilities. Moreover, our algorithm provides guaranteed grade-of-service for all the cells in the network for an arbitrary traffic distribution

Cell placement in a CDMA network

Traditional design rules, wherein cells are dimensioned in order to get an equal amount of demand in each cell are not directly applicable to CDMA networks where large cells can cause a lot of interference to adjacent small cells. In order to enable iterative cell placement we use a computationally efficient iterative process to calculate the inter-cell and intra-cell interferences as a function of pilot-signal power and base station location. These techniques enable us to improve the placement of cells in a CDMA network so as to enhance network capacity. We show examples of how networks using this design technique will provide higher capacity than ones designed using conventional techniques