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Milcom 2015 Track 1 - Waveforms and Signal Processing

Spectrum Efficient Communications with Multiuser MIMO, Multiuser Detection and Interference Alignment Satya Ponnaluri, Sohraab Soltani, Yi Shi and Yalin Sagduyu Intelligent Automation Inc. Rockville, MD USA {sponnaluri, ssoltani, yshi, ysagduyu}@i-a-i.com

Abstract—This paper presents SECANT (Spectrum Efficient Communications and Advanced Networking Technology) as a novel multiuser communication protocol that integrates advanced physical (PHY) layer techniques for adaptive multiple access and interference management. SECANT supports concurrent multiple access (MA) transmissions to common receivers and parallel peer-to-peer (P2P) transmissions in the network. SECANT combines multiuser multiple-input multiple output (MU-MIMO), multiuser detection (MUD), and interference alignment (IA) methods at the PHY layer such that spectrum efficiency can be improved in both interference and signal spaces. An adaptive medium access control (MAC) mechanism is developed to enable spectrum efficiency gains at the PHY layer by managing concurrent transmissions and interference levels. Numerical results indicate significant spectrum efficiency gains (>14 times for fully loaded, and >56 times for 25% loading) that can be achieved over conventional single antenna systems with static MAC protocols.

domain and cannot enable spectrum efficiency gains of advanced PHY layer techniques relying on concurrent transmissions in time, space and frequency. Advanced communication schemes such as interference alignment (IA) [1], [2] and multiuser detection (MUD) [3] have been designed to approach theoretical capacity limits separately on interference and multiple access channels. However, these techniques are usually designed for a small number of nodes with certain topologies without accounting for practical implementation constraints. The theoretical analysis and realistic protocol design on how to apply both IA and MUD for a wireless network are still largely missing. In this paper, we present a protocol called SECANT that combines dynamic interference management, including interference avoidance/cancellation and IA, with spectrumaware MUD and multiuser MIMO at PHY layer to increase spectrum efficiency. Instead of using traditional MAC, a new adaptive MAC scheme is proposed in SECANT to enable PHY layer gains by allowing concurrent transmissions in a collision domain, and dynamically controlling interference and channel access with local spectrum information.

Keywords— Spectrum efficiency; MIMO; multiuser MIMO; interference alignment; multiuser detection; rate; overhead.

I.

INTRODUCTION

Wireless communication spectrum is a scarce resource that must be shared by multiple users across time, space and frequency dimensions. Thus, available spectrum resources impose fundamental limits on wireless system performance. There have been numerous efforts on increasing spectrum efficiency at all layers but a common framework with significant efficiency gains is still lacking and needs to address two main challenges: 1.

2.

We pursue a PHY layer study to evaluate the spectrum efficiency gain of three design steps: (i) multiuser multipleinput multiple-output (MU-MIMO) [4], (ii) overloaded MUMIMO with MUD (MU-MIMO-MUD), and (iii) overloaded MU-MIMO with MUD and IA (MU-MIMO-MUD-IA), over the varying network density where each node has four antennas for practical purposes (SECANT design supports an arbitrary number of antennas and spectrum efficiency will further increase with additional antenna elements). We consider concurrent multiple access (MA) and peer-to-peer (P2P) transmissions over a Wi-Fi density network with soldier walking mobility model. We use single-input single-output (SISO) TDMA quadrature phase-shift keying (QPSK) as a baseline for comparison.

Advanced PHY layer techniques are typically developed for static/small network topologies (e.g., point-to-point, MAC or interference channel) and it is not clear how to apply them to general configurations with local information and distributed control. Legacy MAC schemes, e.g., time-division multipleaccess (TDMA) and carrier-sense multiple-access (CSMA), support one transmission in a single collision

Distribution Statement A (Approved for Public Release, Distribution Unlimited). The views, opinions, and/or findings contained in this article are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. This material is based upon work supported by Defense Advanced Research Projects Agency under Contract W31P4Q-13-C-0159.

978-1-5090-0073-9/15/$31.00 ©2015 IEEE

1

By accounting for protocol overhead, our results show that MU-MIMO-MUD-IA achieves the spectrum efficiency improvement reaching from × 14 to × 20 with the coverage area varying from 100 × 100 to 400 × 400 and with node density of 0.3 per square meters. SECANT MAC

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Milcom 2015 Track 1 - Waveforms and Signal Processing

adapts to dynamic traffic compared to fixed traffic load balancing via TDMA. In particular, the improvement factor achieved by SECANT under dynamic traffic conditions (25% loading) is from ×56 to ×80.

4.

The rest of the paper is organized as follows. Section II provides the multi-layer system architecture for SECANT. MU-MIMO, overloaded MU-MIMO and IA are combined gradually in Sections III, IV, and V, respectively. Section VI studies constrained alphabet signaling effect. SECANT MAC is described in Section VII along with the overhead analysis. Finally, Section VIII concludes the paper. II.

interference from P2P transmissions is aligned within a single dimension. MUD: Each HN uses MUD to decode the information simultaneously transmitted by potentially multiple transmitters.

SYSTEM ARCHITECTURE FOR SECANT

The network is dynamically divided into clusters (with a distributed clustering algorithm for ad hoc networks such as the one in [5]), each with a head node (HN) responsible for coordinating local transmissions and managing interference. SECANT supports multiple simultaneous MA transmissions to HNs in parallel with multiple P2P transmissions. Fig. 1 shows the SECANT network reference model, which is a logical representation of the network architecture.

Fig. 2: The SECANT protocol.

SECANT system model allows nodes in a network, each with antennas, and supports simultaneous transmissions up to transmitters to each HN, along with up to simultaneous P2P direct transmissions. The P2P direct transmissions do not interfere with each other, but may interfere with the transmissions received at a HN. The × 1 received signal vector at a HN in a given cluster is

,

∈

,

!∈"

!

!,

!,

! !

# ,

where , is the × channel matrix between the $-th node and 0-th node (i.e., a HN); %, is the received power from the &-th node; % is the power control parameter at the &-th node; ⊂ (1, 2, … , *, | | , , , is the set of users intending to communicate with the HN; " ⊂ (1,2, … , *, with " ∩ . and |"| , is the set of users intending to communicate with other users in the network; is the × 1 beam-forming vector used by the $-th user; ∈ / is the symbol transmitted by the $ -th user; and # is the complex additive Gaussian noise vector with zero mean and covariance matrix 0 1 2. Similarly, for the P2P communications, the received signal 5 ⊂ (1,2, … , *, " 5 ∩ " ., " 5∩ at the node 34 ∈ " 5 5 ) is ., " ↔ " (there is a one-to-one map between " and " 4 !

Fig. 1: SECANT-MA and SECANT-P2P.

III.

The SECANT protocol is designed based on this network architecture with the following characteristics: 1.

2.

3.

!∈"

4 ! !,!

4 ! ! !,!

#!4 .

SECANT PHY: MU-MIMO

Suppose ∈ transmitters in a given cluster communicate with a HN simultaneously. Then, the × 1 received signal vector at the HN is

Coordination: Nodes are frame-synchronized, and at the beginning of every frame (shown in Fig. 2), nodes requiring channel access request the current HN for channel resources. Joint MA & P2P communication: Based on the number of antennas available, each HN provides channel access to nodes that require the MA and P2P communications. Beamforming and IA: Each HN determines the beamforming vectors for transmitters to ensure that the



∈

,

,

#

7

# ,

where in the second representation, is a matrix of size × whose columns are 8 9 , and 7 is a column , , ∈ vector containing the symbols transmitted by all the users.

2

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Milcom 2015 Track 1 - Waveforms and Signal Processing

For linear receivers with single-user decoders to decode all the transmissions, the underlying matrix must be invertible, and the following conditions must be satisfied: 1. there should be at most transmitters, otherwise there will be fewer dimensions to support all the users, and 2. the channel matrices and beamforming vectors should be such that the resultant vectors from all the users should be linearly independent. In general, if the channel matrices are drawn from a continuous distribution such as zero-mean complex Gaussian (the amplitude is Rayleigh distributed), then the channels themselves are invertible, and therefore :

;< , =

/ ?@

;< , =

@?A

∈( ,