Spatially Sparse Precoding in Millimeter Wave MIMO Systems

• Published in: IEEE transactions on wireless communications Vol. 13; no. 3; pp. 1499 - 1513
• Main Authors: El Ayach, Omar, Rajagopal, Sridhar, Abu-Surra, Shadi, Zhouyue Pi, Heath, Robert W.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2014; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Antenna arrays; Antennas; Applied sciences; Approximation methods; Baseband; basis pursuit; Beamforming; cellular communication; Channels; Coding, codes; Data transmission; Equipments and installations; Exact sciences and technology; Hardware; Information, signal and communications theory; limited feedback; Millimeter wave; Millimeter waves; MIMO; Mobile radiocommunication systems; multiple-input multiple-output (MIMO); precoding; Radio frequency; Radiocommunications; Signal and communications theory; sparse reconstruction; sparsity; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Transceivers; Transmission and modulation (techniques and equipments); Vectors; Performance evaluation; Wireless telecommunication; limited feedback; Cell network; sparsity; precoding; sparse reconstruction; Beam forming; Implementation; Transceiver; Millimetric wave; Three dimensional structure; Coding; antenna arrays; Electric power consumption; Pretreatment; Antenna array; Large scale system; Mobile radiocommunication; MIMO system; Coding circuit; Mixed signal circuit; Base band; multiple-input multiple-output (MIMO); Signalling; basis pursuit; cellular communication; Algorithm; Analog processing; High power; Multi-stream approach; beamforming; Millimeter wave
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2014.011714.130846

Millimeter wave (mmWave) signals experience orders-of-magnitude more pathloss than the microwave signals currently used in most wireless applications and all cellular systems. MmWave systems must therefore leverage large antenna arrays, made possible by the decrease in wavelength, to combat pathloss with beamforming gain. Beamforming with multiple data streams, known as precoding, can be used to further improve mmWave spectral efficiency. Both beamforming and precoding are done digitally at baseband in traditional multi-antenna systems. The high cost and power consumption of mixed-signal devices in mmWave systems, however, make analog processing in the RF domain more attractive. This hardware limitation restricts the feasible set of precoders and combiners that can be applied by practical mmWave transceivers. In this paper, we consider transmit precoding and receiver combining in mmWave systems with large antenna arrays. We exploit the spatial structure of mmWave channels to formulate the precoding/combining problem as a sparse reconstruction problem. Using the principle of basis pursuit, we develop algorithms that accurately approximate optimal unconstrained precoders and combiners such that they can be implemented in low-cost RF hardware. We present numerical results on the performance of the proposed algorithms and show that they allow mmWave systems to approach their unconstrained performance limits, even when transceiver hardware constraints are considered.