Avoidance or Alignment: Interference Mitigation Strategies for Wireless Communications

避免或调整：无线通信的干扰缓解策略

• 批准号: RGPIN-2014-05061
• 负责人: Blostein, Steven
• 金额: $ 2.26万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=588185
• 关键词: Avoidance; Alignment; Interference; Mitigation; Strategies

Wireless communications is transforming society, enabling rapid growth in developing areas without infrastructure and removing barriers. The proliferation of over 1 billion mobile devices (smart phones, etc.) represent convergence of computing and telecommunications. While such devices are capable of operating at very high transmission rates, networks operate in reality at rates orders of magnitude slower. This gap is growing as mobile broadband data traffic is projected to increase by a factor of 18 over the next 3 years. The proposed research program investigates key signal processing techniques that address limitations on spectrum efficiency due to multi-user interference. Nearer term research efforts aim to avoid interference by organizing transmissions orthogonally over time, frequency, and space. Traditionally, frequencies may be spatially reused by shrinking network cell sizes. However, servicing spatially non-uniform user traffic (hot spots) requires the overlapping of different cell types. The resulting heterogeneous networks challenge orthogonality. Multiple antenna systems, known as multiple-input multiple-output (MIMO) systems, may achieve directional transmission via beamforming and/or multiplex multiple data streams via space-time coded transmission. In the shorter term, we aim to maintain user orthogonality and achieve more opportunistic and dynamic use of spectrum. We propose to better identify time periods of unused spectrum via decision theory and optimization. The knowledge gained would then be applied to interference avoidance with large potential payoffs for emerging products and standards. First, we plan to develop true two-sided sequential statistical tests to more efficiently detect both appearance and absence of users over time, frequency and space. Next, we seek distributed solutions with minimum communication overhead and sensor observation energy expenditure. A parallel investigation will address increasing spectrum efficiency by reducing signaling overhead. Increasingly, advanced methods exploit channel state information, that is typically assumed to be constant over short periods, i.e., quasi-static. In the short to medium term we propose new “predictive training” methods that account for predictable channel variations. Here, we plan to extend and generalize our recently developed approach to channel estimation by taking advantage of physical properties of mobile wireless signal transmission including bandlimited fading and Doppler effects in MIMO systems. In the longer term, we aim to disrupt the interference avoidance paradigm for orthogonal transmission by advanced signal processing that achieves interference alignment (IA). This is in contrast to conventional multi-user interference cancellation that costs a spatial dimension (antenna) per interferer, or to multi-user detection that must jointly decode all user signals. In IA, user signals from different transmitters are processed to project interference onto reduced-dimension subspaces to regain lost orthogonality. Under idealized conditions of low noise levels, systems employing IA have the potential to increase throughput proportionally with numbers of users, i.e., interference becomes a benefit! However, practical realization of IA requires highly accurate channel state information, including timing and frequency synchronization. We plan to study performance limitations and propose realizable IA algorithms at finite SNR that incorporate novel approaches to channel estimation, multiple timing and frequency offset recovery. Here, we plan to leverage our extensive recent research on channel estimation and training of imperfectly synchronized multiple relay networks, which shares key “multiple access” features with the IA problem.

无线通信正在改变社会，使没有基础设施的发展中地区能够快速增长，并消除障碍。超过10亿台移动的设备（智能手机等）的激增代表了计算和电信的融合。虽然这样的设备能够以非常高的传输速率操作，但网络实际上以慢几个数量级的速率操作。随着移动的宽带数据流量预计在未来3年内增加18倍，这一差距正在扩大。拟议的研究计划调查的关键信号处理技术，解决由于多用户干扰的频谱效率的限制。 近期的研究工作旨在通过在时间、频率和空间上正交地组织传输来避免干扰。传统上，可以通过缩小网络小区大小来在空间上重用频率。然而，服务于空间上非均匀的用户业务（热点）需要不同小区类型的重叠。由此产生的异构网络挑战正交性。被称为多输入多输出（MIMO）系统的多天线系统可以经由波束成形来实现定向传输和/或经由空时编码传输来复用多个数据流。 在短期内，我们的目标是保持用户的正交性，并实现更多的机会和动态使用频谱。我们建议通过决策理论和优化来更好地识别未使用频谱的时间段。所获得的知识将应用于避免干扰，为新兴产品和标准带来巨大的潜在回报。首先，我们计划开发真正的双侧序贯统计测试，以更有效地检测用户在时间、频率和空间上的出现和不出现。接下来，我们寻求具有最小通信开销和传感器观察能量消耗的分布式解决方案。 并行调查将通过减少信令开销来解决提高频谱效率的问题。越来越多的先进方法利用信道状态信息，该信息通常被假设为在短时间内是恒定的，即，准静态在短期到中期，我们提出了新的“预测训练”方法，占可预测的信道变化。在这里，我们计划扩展和推广我们最近开发的方法，利用移动的无线信号传输的物理特性，包括在MIMO系统中的带限衰落和多普勒效应的信道估计。 从长远来看，我们的目标是通过实现干扰对齐（IA）的高级信号处理来破坏正交传输的干扰避免范式。这与传统的多用户干扰消除形成对比，传统的多用户干扰消除每个干扰消除器花费空间维度（天线），或者与必须联合解码所有用户信号的多用户检测形成对比。在IA中，来自不同发射机的用户信号被处理以将干扰投射到降维子空间上以恢复丢失的正交性。在低噪声水平的理想条件下，采用IA的系统具有与用户数量成比例地增加吞吐量的潜力，即，干扰变成了好处！然而，IA的实际实现需要高度精确的信道状态信息，包括定时和频率同步。我们计划研究的性能限制，并提出可实现的IA算法在有限的SNR，结合新的方法来信道估计，多个定时和频率偏移恢复。在这里，我们计划利用我们最近广泛的研究信道估计和训练不完全同步的多个中继网络，其中共享关键的“多址”功能与IA问题。