Full Diversity Unitary Precoded Integer-Forcing

We consider a point-to-point flat-fading MIMO channel with channel state information known both at transmitter and receiver. At the transmitter side, a lattice coding scheme is employed at each antenna to map information symbols to independent lattice codewords drawn from the same codebook. Each lattice codeword is then multiplied by a unitary precoding matrix ${\bf P}$ and sent through the channel. At the receiver side, an integer-forcing (IF) linear receiver is employed. We denote this scheme as unitary precoded integer-forcing (UPIF). We show that UPIF can achieve full-diversity under a constraint based on the shortest vector of a lattice generated by the precoding matrix ${\bf P}$. This constraint and a simpler version of that provide design criteria for two types of full-diversity UPIF. Type I uses a unitary precoder that adapts at each channel realization. Type II uses a unitary precoder, which remains fixed for all channel realizations. We then verify our results by computer simulations in $2\times2$, and $4\times 4$ MIMO using different QAM constellations. We finally show that the proposed Type II UPIF outperform the MIMO precoding X-codes at high data rates.Comment: 12 pages, 8 figures, to appear in IEEE-TW

A virtual MIMO dual-hop architecture based on hybrid spatial modulation

International audienceIn this paper, we propose a novel Virtual Multiple-Input-Multiple-Output (VMIMO) architecture based on the concept of Spatial Modulation (SM). Using a dual-hop and Decode-and-Forward protocol, we form a distributed system, called Dual-Hop Hybrid SM (DH-HSM). DH-HSM conveys information from a Source Node (SN) to a Destination Node (DN) via multiple Relay Nodes (RNs). The spatial position of the RNs is exploited for transferring information in addition to, or even without, a conventional symbol. In order to increase the performance of our architecture, while keeping the complexity of the RNs and DN low, we employ linear precoding using Channel State Information (CSI) at the SN. In this way, we form a Receive-Spatial Modulation (R-SM) pattern from the SN to the RNs, which is able to employ a centralized coordinated or a distributed uncoordinated detection algorithm at the RNs. In addition, we focus on the SN and propose two regularized linear precoding methods that employ realistic Imperfect Channel State Information at the Transmitter. The power of each precoder is analyzed theoretically. Using the Bit Error Rate (BER) metric, we evaluate our architecture against the following benchmark systems: 1) single relay; 2) best relay selection; 3) distributed Space Time Block Coding (STBC) VMIMO scheme; and 4) the direct communication link. We show that DH-HSM is able to achieve significant Signal-to-Noise Ratio (SNR) gains, which can be as high as 10.5 dB for a very large scale system setup. In order to verify our simulation results, we provide an analytical framework for the evaluation of the Average Bit Error Probability (ABEP)

Performance evaluation of massive MIMO Systems for future wireless access systems

The vision for 5G is predicated on three main cornerstones. These are massive Machine Type Communication (mMTC) technologies, Ultlra Reliable Low Latency Communications (uRLLC) and enhanced Mobile Broadband (eMBB). In order to achieve the high capacity needed for enhanced Mobile Broadband (eMBB) to be a reality, a number of technologies have been proposed in various research forums. These include use of spectrum bands like 26GHz which are called the mmWave spectra. Although the use of mmWave spectra brings in a lot of capacity because of increased bandwidth, the signal attenuates quickly as it suffers a lot of diffraction losses at such high frequencies. In order to mitigate this the use of massive MIMO technology has been proposed. Massive MIMO improves both spectral efficiency and energy efficiency and is therefore also proposed for spectra below 6GHz. This study focuses on assessing the potential of massive MIMO through evaluation of linear precoding and receive combining methods that may be applicable to massive MIMO. Linear signal detection and precoding for MIMO is generally not optimal. Optimal methods such as Maximal Likelihood (ML) signal processing methods have high computational complexity such that their practical implementation is difficult. The complexity for ML is O(MN ) for an M−ary modulated signal and N antennas. This is compared to a linear signal processing method called Zero Forcing (ZF) with a complexity of the order of O(N3 ). Assessing the performance of linear signal processing methods is therefore invaluable for the success of massive MIMO in general and 5G in particular. Simulations to evaluate spectral efficiency for massive MIMO were done in MATLAB. Linear and sub-optimal signal processing methods like minimum mean square error (MMSE), zero forcing (ZF), regularized zero forcing (RZF) and maximal ratio combining (MRC) detection and precoding algorithms with relatively less complexity were evaluated. The spectral efficiency (SE) of these signal processing methods were evaluated through a Monte Carlo simulation method in a massive MIMO single base station cell, a 16 cell grid network and a 64 cell grid network. SE values of up to 200 kbps/Hz/cell were obtained with 100 antenna elements and 10 users per cell. The effect of pilot reuse factor for both detection and precoding signal processing systems was also evaluated. A pilot reuse factor of 8 seemed optimal for 64 cell grid network modeled. The overall results obtained from this study show that the Spectral Efficiency (SE) improves as the number of antenna elements to users ratio increased. MMSE and RZF had the best performance under all simulation conditions while for Maximal Ratio Combining (MR) a much larger number of antenna elements was needed in order to approach the performance of MMSE and RZF. An evaluation of the effect of the iii dominant propagation channel conditions was also done by evaluating the spectral efficiency performance of the four detection methods in correlated and uncorrelated channels. Lastly the effect of pilot contamination was investigated. The results showed that an optimal value that maximizes the obtainable spectral efficiency for a massive MIMO network can be obtained

Interference driven antenna selection for Massive Multi-User MIMO

Low-complexity linear precoders are known to be close-to-optimal for massive multi-input multi-output (M-MIMO) systems. However, the large number of antennas at the transmitter imposes high computational burdens and high hardware overloads. In line with the above, in this paper we propose a low complexity antenna selection (AS) scheme which selects the antennas that maximize constructive interference between the users. Our analyses show that the proposed AS algorithm, in combination with a simple matched filter (MF) precoder at the transmitter, is able to achieve better performances than systems equipped with a more complex channel inversion (CI) precoder and computationally expensive AS techniques. First, we give an analytical definition of constructive and destructive interference, based on the phase of the received signals from phase-shifted-keying (PSK) modulated transmissions. Then, we introduce the proposed antenna selection algorithm, which identifies the antenna subset with the highest constructive interference, maximizing the power received by the user. In our studies, we derive the computational burden of the proposed technique with a rigorous and thorough analysis and we identify a closed form expression of the upper bound received power at the user side. In addition, we evaluate in detail the power benefits of the proposed transmission scheme by defining an efficiency metric based on the achieved throughput. The results presented in this paper prove that antenna selection and green radio concepts can be jointly used for power efficient M-MIMO, as they lead to significant power savings and complexity reductions

Performance Analysis of Multistream Receive Spatial Modulation in the MIMO Broadcast Channel

International audienceIn this paper, Multi-Stream Receive-Spatial Modulation (MSR-SM) for application to the Multiple-Input Multiple-Output (MIMO) broadcast channel is introduced and studied. MSR-SM is a closed-loop transmission scheme, which applies the concept of multistream space modulation at the receiver side. An accurate mathematical framework for the evaluation of the Bit Error Rate (BER) is proposed. In addition, the diversity order and coding gain of the new architecture are derived. Note that the proposed analytical framework takes into account both the small-scale fading and the system topology, and is directly applicable to the conventional MIMO broadcast channel. Compared with the state-of-the-art MIMO transmission in the broadcast channel, it is mathematically shown that MSR-SM achieves the same diversity order and a better coding gain, in the high Signal-to-Noise Ratio (SNR) regime. Finally, the proposed mathematical framework and the new findings are validated via Monte Carlo simulation results