Next-generation wireless networks target dense deployment scenarios where many heterogeneous devices, from high-end laptops to low-power Internet of Things (IoT) devices and wearables, must coexist and operate reliably. With increased deployment of IoT devices and wearables, increasing the energy efficiency without increasing complexity#x002F;cost is highly desirable in future dense wireless networks. In current wireless local area networks (WLAN), implementing multi-input multi-output (MIMO) techniques which utilizes multiple RF chains has become the norm, however, for IoT and wearable-type devices this is not efficient due to size and cost constraints. This paper studies the application of the Spatial Modulation (SM) waveform to the WLAN system (commercially named Wi-Fi). Specifically, the SM concept is applied to the MIMO orthogonal frequency division multiplexing (OFDM) transceiver architecture of the 802.11 WLAN standards calling it spatially-modulated OFDM (SM-OFDM) WLAN. Thus, using a small number of RF chains, and ideally a single RF chain, is highly desirable for future low-cost devices. The proposed SM-OFDM scheme helps the Access Point (AP) to efficiently communicate with this diverse set of devices while addressing challenging design trade-offs between energy efficiency, implementation complexity, and overall network spectral efficiency.

Differential space time block coding (STBC) achieves full spatial diversity and avoids channel estimation overhead. Over highly frequency-selective channels, STBC is integrated with orthogonal frequency division multiplexing (OFDM) to achieve high performance. However, low-cost implementation of differential STBC- OFDM using direct-conversion transceivers is sensitive to In-phase/Quadrature-phase imbalance (IQI). In this paper, we quantify the performance impact of IQI at the receiver front-end on differential STBC-OFDM systems and propose a compensation algorithm to mitigate its effect. The proposed receiver IQI compensation works in an adaptive decision-directed manner without using known pilots or training sequences, which reduces the rate loss due to training overhead. Our numerical results show that our proposed compensation algorithm can effectively mitigate receive IQI in differential STBC-OFDM.

We study the problem of multi-stream joint maximum sum-rate precoder and minimum mean-squared error equalizer design for interference alignment in the multi-input multi-output interference channel. Joint precoder and equalizer optimization requires alternation between the forward and reverse links and assumes perfect synchronization at each network node between the transmitters and receivers, resulting in extensive overhead and spectral efficiency loss. To overcome this serious drawback, we propose a new design approach based on weighted-sum-rate maximization assuming a virtual equalizer type at the transmitter to limit the optimization process to the transmitter side. Furthermore, we examine the achievable weighted-sum-rate when the virtual equalizer type is either matched or mismatched to the actual equalizer type used at the receivers. In addition, we quantify the performance loss due to mismatched equalizer types and demonstrate the robustness of our proposed sum-rate weighting strategy to such mismatches. Finally, we derive asymptotic performance expressions and verify their accuracy numerically, even for a moderate number of users.

In this paper, we study the problem of per-stream maximum sum-rate joint precoder and minimum mean-squared error equalizer design for the multi-input multi-output interference channel. We consider the general case of more than three users with more than one stream per user. We propose a generalized iterative algorithm which directly maximizes the sum-rate without assuming the signal-to-noise ratio to be infinite. To reduce complexity, which can become prohibitive for large network size, we examine the performance-complexity tradeoffs involved in a sparse equalizer design. Joint precoder and equalizer optimization requires alternation between the forward and reverse links and assumes perfect synchronization between the transmitters and receivers at each network node, resulting in extensive overhead and spectral efficiency loss. To overcome this serious drawback, we propose a new design approach based on weighted-sum-rate maximization assuming a virtual equalizer type at the transmitter to limit the optimization process to the transmitter side. In addition, we quantify the sum-rate loss due to mismatched equalizer types and demonstrate the robustness of our proposed sum-rate weighting strategy to such mismatches with perfect or imperfect channel knowledge. Finally, we derive asymptotic performance expressions and verify their accuracy numerically even for a moderate number of users.

We study the problem of per-stream joint maximum sum-rate (MSR) precoder and minimum mean-squared error (MMSE) equalizer design for interference alignment in the multi-input multi-output interference channel. We consider the general case of more than three users with more than one stream per user. We propose a new generalized iterative algorithm which directly maximizes the average overall sum-rate without assuming the signal-to-noise ratio to be infinite. The receivers' implementation complexity increases proportional to the square of the number of equalizer taps which becomes prohibitive as the network size increases. To address this issue, we examine the performance-complexity tradeoffs involved in a sparse equalizer design. Our numerical results demonstrate that, for the full-complexity MMSE equalizer design, our proposed algorithm achieves higher overall sum-rate compared to previously proposed interference alignment algorithms. In addition, we reduce the MMSE linear equalizer complexity by 30% while limiting the sum-rate loss to about 10%, at most, compared to the full-complexity design.