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Research at Communication Systems

Massive MIMO --- the Scalable 5G Technology

Different array types.

Massive MIMO is a leading 5G technology candidate, that aims at delivering uniformly good service to wireless terminals in high-mobility environments. The key concept is to equip base stations with large arrays of antennas that serve many terminals simultaneously, in the same time-frequency resource. Massive MIMO arrays have attractive form factors, for example, at the 2 GHz band, a lambda/2-spaced rectangular array with 200 dual-polarized elements would be about 1.5 x 0.75 meters large. Massive MIMO operates in TDD mode and the downlink beamforming exploits the reciprocity of radio propagation --- specifically the base station array uses channel estimates obtained from uplink pilots transmitted by the terminals. This makes Massive MIMO entirely scalable with respect to the number of base station antennas. Base stations in Massive MIMO operate autonomously, with no sharing of payload data or channel state information.


Some links:

Research problems include:

  • Coding and signal processing
  • Capacity analysis
  • System optimization
  • Design of protocols for random access and multiple-access
  • Waveform design

Resource Allocation in Wireless Networks

Cross-talk in a multi-user communication situation.

Spectrum is a scarce resource which has to be efficiently used to accomodate the exponentially increasing traffic. Modern wireless systems are already using aggressive frequency reuse factors and emerging ones may even need to allow spectrum sharing between different operators. The main impairement that needs to be tackled is the interference created by simultaneous use of the spectral resources in adjacent areas. Our work especially addresses resource allocation for multiple-antenna systems, where interference can be mitigated by transmit beamforming and power control techniques.

Cooperative Localization

Cooperative Localization.

A number of applications in wireless sensor networks (WSN) require sensor nodes to obtain their absolute or relative positions. Equipping every sensor with a GPS receiver may be expensive, energy prohibitive and limited to outdoor applications. Therefore, we consider the problem in which some small number of sensors, called anchor nodes, obtain their coordinates via GPS or by installing them at points with known coordinates, and the rest, unknown nodes, must determine their own coordinates using the anchor nodes and measured inter-sensor distances. If unknown nodes were capable of high-power transmission, they would be able to make measurements with all anchor nodes. This represents single-hop localization. However, we prefer to use energy-conserving devices without energy necessary for long-range communication. In this case, each unknown node has available only the noisy measurements of the distance to several neighboring nodes (not necessarily anchor nodes). In other words, we still allow unknown nodes to make measurements with anchor nodes (if possible), but now we additionally allow unknown nodes to make measurements with other unknown nodes. It is still necessary that there is minimum of three (for 2D) or four (for 3D) anchor nodes in the network, but not necessarily directly connected to all unknown nodes. This technique is known as cooperative (or multi-hop) localization (CL).

CL typically consists of two phases: the measurement phase, and the localization phase. In the first phase, the inter-sensor distances are estimated using time-of-arrival (TOA), time-difference-of-arrival (TDOA), or received-signal-strength (RSS). It is also possible to estimate the angle of arrival (AOA), but this approach is rarely used due to the high equipment cost. Then, these estimates are used as input for a localization algorithm which provides the estimates of all unknown sensor positions and eventually associated uncertainties. CL may enable many of applications such as search-and-rescue, equipment monitoring and control, intrusion detection, target tracking, road traffic monitoring, health monitoring, and surveillance. There are many open issues in this field, such as increased complexity, high communication overhead, and decreased performance caused by non-rigid or loopy graphs.

Agile and Energy-Efficient Digital Signal Processing (DSP)

Energy efficiency of signal processing hardware is vital for several reasons, in particular regarding environmental burden reductions, longer stand-by times, smaller and lighter batteries, and simpler cooling of circuits. The need for a reduced energy consumption is also accentuated by the trend of adding more and more functionality into electronic devices, which means that the efficiency of each functionality has to be increased. The focus here is to develop agile and efficient DSP algorithms targeting mainly communication systems with time-varying operation modes and quality requirements. This is addressed by developing DSP algorithms that are agile both in terms of functionality and in terms of varying quality requirements, typically given as signal-to-noise ratio (SNR) requirements which relate to bit error rate (BER). An example is a filter with adaptable bandwidths and center frequencies (functionality) as well as adaptable attenuations and word-lengths (quality requirements). Such DSP algorithms can be adapted online to match the requirements on the present mode of the communication system. In this way, one can meet the requirements with minimum computational complexity and energy consumption for each mode. In addition, it should be possible to carry out the adaptation in a short time to enable real-time operation. The DSP algorithms in focus here are found in the physical layer of transceivers, including sampling rate conversion, resampling, channelization, carrier aggregation, multiplexing/transmultiplexing, frequency-band reallocation, spectrum sensing, channel equalization, multi-channel signal reconstruction, and channel and parameter estimation.

Page responsible: Erik G. Larsson
Last updated: 2016 10 25   07:21