The prognostic significance of allelic imbalance at key chromosomal loci in oral cancer

Forty-eight primary oral squamous cell carcinomas (SCC) were screened for allelic imbalance (AI) at 3p24–26, 3p21, 3p13, 8p21–23, 9p21, 9q22 and within the Rb, p53 and DCC tumour suppressor genes. AI was detected at all TNM stages with stage 4 tumours showing significantly more aberrations than stage 1–3. A factional allelic loss (FAL) score was calculated for all tumours and a high score was associated with development of local recurrence (P = 0.033) and reduced survival (P = 0.0006). AI at one or more loci within the 3p24–26, 3p21, 3p13 and 9p21 regions or within the THRB and DCC genes was associated with reduced survival. The hazard ratios for survival analysis revealed that patients with AI at 3p24–26, 3p13 and 9p21 have an approximately 25 times increase in their mortality rate relative to a patient retaining heterozygosity at these loci. AI at specific pairs of loci, D3S686 and D9S171 and involving at least two of D3S1296, DCC and D9S43, was a better predictor of prognosis than the FAL score or TNM stage. These data suggest that it will be possible to develop a molecular staging system which will be a better predict of outcome than conventional clinicopathological features as the molecular events represent fundamental biological characteristics of each tumour. © 1999 Cancer Research Campaig

Graph-Based Multicell Scheduling in OFDMA-Based Small Cell Networks

This paper proposes a novel graph-based multicell scheduling framework to efficiently mitigate downlink intercell interference in OFDMA-based small cell networks. We define a graph-based optimization framework based on interference condition between any two users in the network assuming they are served on similar resources. Furthermore, we prove that the proposed framework obtains a tight lower bound for conventional weighted sum-rate maximization problem in practical scenarios. Thereafter, we decompose the optimization problem into dynamic graph-partitioning-based subproblems across different subchannels and provide an optimal solution using branch-and-cut approach. Subsequently, due to high complexity of the solution, we propose heuristic algorithms that display near optimal performance. At the final stage, we apply cluster-based resource allocation per subchannel to find candidate users with maximum total weighted sum-rate. A case study on networked small cells is also presented with simulation results showing a significant improvement over the state-of-the-art multicell scheduling benchmarks in terms of outage probability as well as average cell throughput

Graph-Based Multicell Scheduling in OFDMA-Based Small Cell Networks

This paper proposes a novel graph-based multicell scheduling framework to efficiently mitigate downlink intercell interference in OFDMA-based small cell networks. We define a graph-based optimization framework based on interference condition between any two users in the network assuming they are served on similar resources. Furthermore, we prove that the proposed framework obtains a tight lower bound for conventional weighted sum-rate maximization problem in practical scenarios. Thereafter, we decompose the optimization problem into dynamic graph-partitioning-based subproblems across different subchannels and provide an optimal solution using branch-and-cut approach. Subsequently, due to high complexity of the solution, we propose heuristic algorithms that display near optimal performance. At the final stage, we apply cluster-based resource allocation per subchannel to find candidate users with maximum total weighted sum-rate. A case study on networked small cells is also presented with simulation results showing a significant improvement over the state-of-the-art multicell scheduling benchmarks in terms of outage probability as well as average cell throughput

Joint TDD Backhaul and Access Optimization in Dense Small Cell Networks

This paper addresses the problem of joint backhaul and access links optimization in dense small cell networks with special focus on time division duplexing (TDD) mode of operation in backhaul and access links transmission. Here, we propose a framework for joint radio resource management where we systematically decompose the problem in backhaul and access links. To simplify the analysis, the procedure is tackled in two stages. At the first stage, the joint optimization problem is formulated for a point-to-point scenario where each small cell is simply associated to a single user. It is shown that the optimization can be decomposed into separate power and subchannel allocation in both backhaul and access links where a set of rate-balancing parameters in conjunction with duration of transmission governs the coupling across both links. Moreover, a novel algorithm is proposed based on grouping the cells to achieve rate-balancing in different small cells. Next in the second stage, the problem is generalized for multi access small cells. Here, each small cell is associated to multiple users to provide the service. The optimization is similarly decomposed into separate sub-channel and power allocation by employing auxiliary slicing variables. It is shown that similar algorithms as previous stage are applicable by slight change with the aid of slicing variables. Additionally, for the special case of line-of-sight backhaul links, simplified expressions for sub-channel and power allocation are presented. The developed concepts are evaluated by extensive simulations in different case studies from full orthogonalization to dynamic clustering and full reuse in the downlink and it is shown that proposed framework provides significant improvement over the benchmark cases

Joint TDD Backhaul and Access Optimization in Dense Small Cell Networks

This paper addresses the problem of joint backhaul and access links optimization in dense small cell networks with special focus on time division duplexing (TDD) mode of operation in backhaul and access links transmission. Here, we propose a framework for joint radio resource management where we systematically decompose the problem in backhaul and access links. To simplify the analysis, the procedure is tackled in two stages. At the first stage, the joint optimization problem is formulated for a point-to-point scenario where each small cell is simply associated to a single user. It is shown that the optimization can be decomposed into separate power and subchannel allocation in both backhaul and access links where a set of rate-balancing parameters in conjunction with duration of transmission governs the coupling across both links. Moreover, a novel algorithm is proposed based on grouping the cells to achieve rate-balancing in different small cells. Next in the second stage, the problem is generalized for multi access small cells. Here, each small cell is associated to multiple users to provide the service. The optimization is similarly decomposed into separate sub-channel and power allocation by employing auxiliary slicing variables. It is shown that similar algorithms as previous stage are applicable by slight change with the aid of slicing variables. Additionally, for the special case of line-of-sight backhaul links, simplified expressions for sub-channel and power allocation are presented. The developed concepts are evaluated by extensive simulations in different case studies from full orthogonalization to dynamic clustering and full reuse in the downlink and it is shown that proposed framework provides significant improvement over the benchmark cases