We study the issue of handling unknown inter-cell interference in multi-cell environments with antenna arrays at the base stations. First, we demonstrate that the presence of unknown inter-cell interference that cannot be predicted accurately, can significantly degrade the system performance when unaccounted for. Second, we propose two algorithms that are aimed at providing a quality-of-service guarantee in the form of packet error rate (PER) in the presence of unknown inter-cell interference. The first algorithm is based on a derived expression for average PER as a function of the distribution of experienced signal to interference and noise ratio and dynamically adjusts the input parameters based on the channel condition and interference. The second algorithm makes use of the observation that the distribution of inter-cell interference experienced by a mobile can be well approximated by a log-normal distribution. We demonstrate that these algorithms achieve the target PER

We study the application of antenna arrays at the base stations in a multi-cell environment. Simulation results demonstrate that the inter-cell interference significantly degrades the system performance in terms of packet loss probability (PLP) and total throughput. In order to cope with the presence of inter-cell interference in multi-cell networks we propose two algorithms: the first algorithm replaces the noise term in the beamforming algorithm by the sum of average inter-cell interference and noise, and is shown to perform well for certain type of link curves. We derive an expression for the average PLP as a function of the target SINR value corresponding to the target PLP. This expression tells us that the average PLP does not equal the target PLP in general, and offers an explanation for the good performance of the first algorithm for some link curves. Based on this expression, the second proposed algorithm makes use of more than just the mean of the SINR distribution in the beamforming algorithm. The experimental results demonstrate that this algorithm achieves the target PLP with general link curves and different scheduling algorithms under various wireless channel models. We characterize the inter-cell interference distribution for different scheduling and beamforming algorithms under various channel models. It is shown that the inter-cell interference can be well approximated by a log-normal random variable and exhibits weak temporal correlation.

We study the downlink scheduling problem in a cellular wireless network. The base stations are equipped with antenna arrays and can transmit to more than one mobile user at any time instant, provided the users are spatially separable. In previous work, an infinite traffic demand model is used to study the physical layer beamforming and power control algorithms that maximize the system throughput. We consider finite user traffic demands. A scheduling policy makes a decision based on both the queue lengths and the spatial separability of the users. The objective of the scheduling algorithm is to maintain the stability of the system. We derive an optimal scheduling policy that maintains the stability of the system if it is stable under any scheduling policy. However, this optimal scheduling policy is exponentially complex in the number of users which renders it impractical. We propose four heuristic scheduling algorithms that have polynomial complexity. The first two algorithms are for the special case of single cell systems, while the other two algorithms deal with multiple cell systems. Using a realistic multipath wireless channel model, we evaluate the performance of the proposed algorithms through computer simulations. The results demonstrate the benefits of joint consideration of queue length and dynamic base station assignment.

Recent advances in the area of wireless communications have revealed the emerging need for efficient wireless access in personal, local and wide area networks. Space division multiple access (SDMA) with smart antennas at the base station is recognized as a promising means of increasing system capacity and supporting rate-demanding services. However, the existence of SDMA at the physical layer raises significant issues at higher layers. In this paper, we attempt to capture the impact of SDMA on channel allocation at the media access control (MAC) layer. This impact obtains different forms in TDMA, CDMA and OFDMA access schemes, due to the different cochannel and interchannel interference instances, as well as the different effect of corresponding channels (time slots, codes or subcarrier frequencies) on user channel characteristics. We follow a unified approach for these multiple access schemes and propose heuristic algorithms to allocate channels to users and adjust down-link beamforming vectors and transmission powers, with the objective to increase achievable system rate and provide QoS to users in the form of minimum rate guarantees. We consider the class of greedy algorithms, based on criteria such as minimum induced or received interference and minimum signal-to-interference ratio (SIR), as well as the class of SIR balancing algorithms. Our results indicate that this cross-layer approach yields significant performance benefits and that SIR balancing algorithms achieves the best performance.

The use of smart antennas in extending coverage range and capacity of wireless networks dictates the employment of novel media access control protocols, with which the base station (BS) or access point (AP) provides access to users by learning their locations. We consider the class of protocols that employ beam forming and use contention-based or contention-free polling methods to locate users residing in or out of coverage range of the AP. Such protocols allow rapid media access and can be embedded in existing MAC protocols.

QoS provisioning to users in the presence of volatility of the wireless channel is the most challenging issue in wireless system design. We consider the problem of scheduling constant bit rate (CBR) traffic packets over the wireless channel, subject to packet delivery deadline constraints. We cast the problem as a Markov decision process and derive the optimal scheduling policy, in the sense of minimizing long-term packet loss due to deadline expirations. Performance bounds and design guidelines for general scheduling algorithms are obtained through analysis and simulations.